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Patel Dineshkumar Vinubhai, Kamalesh V. N, and Madhukar G. "CROP IMAGE CLASSIFICATION." Scientific Digest : Journal of Applied Engineering 13, no. 7(1) (2025): 92–101. https://doi.org/10.70864/joae.2025.v13.i7(1).pp92-101.
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Emotion detection plays a vital role in advancing human-computer interaction by enabling systems to recognize and respond appropriately to human emotions. This study introduces a deep learning-based multimodal emotion detection model that combines speech recognition and facial expression analysis to enhance classification accuracy. The proposed approach utilizes Convolutional Neural Network (CNN) architectures to simultaneously process audio signals and facial images, effectively capturing complementary information from both data types. While traditional methods like Random Forest Classifier (RFC) and Deep Neural Networks (DNN) have been used for emotion detection, they often fall short in extracting complex spatial and temporal features required for high-performance multimodal analysis. To address this, the study employs a transfer learning framework to extract meaningful features from both speech and facial modalities, followed by a hybrid feature fusion technique to integrate these features for improved robustness and accuracy. The model is trained and tested using publicly available multimodal emotion datasets. Results from the experiments reveal that the proposed transfer learning-based system significantly outperforms existing models in terms of accuracy, precision, recall, and F1-score. This innovative approach offers a strong foundation for realtime emotion detection with wide-ranging applications in human-computer interaction, healthcare, surveillance, and personalized services.
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Jiang, Xuetao, Binbin Yong, Soheila Garshasbi, Jun Shen, Meiyu Jiang, and Qingguo Zhou. "Crop and weed classification based on AutoML." Applied Computing and Intelligence 1, no. 1 (2021): 46–60. http://dx.doi.org/10.3934/aci.2021003.
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<abstract> <p>CNN models already play an important role in classification of crop and weed with high accuracy, more than 95% as reported in literature. However, to manually choose and fine-tune the deep learning models becomes laborious and indispensable in most traditional practices and research. Moreover, the classic objective functions are not thoroughly compatible with agricultural farming tasks as the corresponding models suffer from misclassifying crop to weed, often more likely than in other deep learning application domains. In this paper, we applied autonomous machine learning with a new objective function for crop and weed classification, achieving higher accuracy and lower crop killing rate (rate of identifying a crop as a weed). The experimental results show that our method outperforms state-of-the-art applications, for example, ResNet and VGG19.</p> </abstract>
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Ashrith Sambaraju, Marikanti Sathvika, Mainam Krupa, Velagapudi Venkata Sai Mahendra Kumar, Mrs.K. Revathi, and Dr. M. Ramesh. "Automated Brain Tumor Classification Using Hybrid Deep Learning Models." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 05 (2025): 2171–77. https://doi.org/10.47392/irjaeh.2025.0318.
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Agricultural price forecasting plays a vital role in empowering farmers with market intelligence, enhancing crop planning, and supporting economic resilience. This project presents an efficient and user-friendly system for predicting the Minimum Support Price (MSP) of crops using machine learning techniques, with a real-time interface built using Streamlit. The system leverages an XGBoost regression model trained on historical crop price datasets, including commodity name, crop variety, type, and year. To increase accessibility and impact, the application incorporates Twilio SMS integration, enabling users to send MSP predictions directly to farmers’ mobile phones. The web interface includes a step-wise selection mechanism for crop type, commodity, and variety, along with intuitive visualization of prediction results and comparison with actual MSP values when available. The model achieves a strong R² score, indicating reliable predictive performance across crop types and years. By integrating machine learning with SMS-based communication, this solution offers a practical and scalable tool for agricultural advisory systems, especially in rural and low-resource settings.
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Bakshi, Krish. "Crop Classification using Convolutional Neural Network." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41238.
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- Crop Classification using CNN - A Multi-Model Approach for Crop Classification and Health Assessment Using Convolutional Neural Networks and GPT Integration Crop classification and health assessment are critical tasks in precision agriculture, aimed at improving yield and minimizing losses. In this research, we propose a multi-model pipeline leveraging Convolutional Neural Networks (CNNs) to classify crops and assess their health conditions based on leaf images. The pipeline consists of two primary models: (1) a detection and classification model to identify crop types (e.g., potato, tomato) and (2) a health condition classification model to diagnose plant health (e.g., healthy, early blight, late blight). The pipeline integrates a Generative Pre-trained Transformer (GPT) API to generate actionable quality summaries based on the model outputs. Extensive experiments were conducted to compare YOLOv5, ResNet-50, EfficientNet-B0, and other models, supported by performance metrics, real-world applicability, and insights from generated summaries. Key Words: Deep Learning in Agriculture, Convolutional Neural Networks (CNNs), Plant Disease Detection, Generative AI for Crop Analysis, Precision Agriculture.
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Bakshi, Krish. "Survey paper on Crop Classification using Convolutional Neural Network." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41381.
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Crop Classification using CNN - A Multi-Model Approach for Crop Classification and Health Assessment Using Convolutional Neural Networks and GPT Integration. The integration of deep learning and natural language processing (NLP) in agriculture has gained significant attention for automating crop classification and disease diagnosis. This survey provides an extensive review of various deep learning models applied in crop classification, plant health assessment, and NLP-based report generation. The study explores the effectiveness of object detection models like YOLO, classification networks such as ResNet and EfficientNet, and NLP models like GPT for generating quality assessments. Additionally, this paper discusses the challenges and future research directions in this domain. The survey serves as a precursor to our research implementation, which integrates CNN-based models for crop identification, health assessment, and GPT-based NLP solutions for automated reporting. Key Words: Deep Learning in Agriculture, Convolutional Neural Networks (CNNs), Plant Disease Detection, Generative AI for Crop Analysis, Precision Agriculture.
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Ashwin, John, Davis Denzil, Merin C. Tom Athulya, Davis Dibin, and Davis Jasmi. "Soil Classification and Crop Recommendation System." International Journal of Innovative Science and Research Technology 7, no. 6 (2022): 80–83. https://doi.org/10.5281/zenodo.6692499.
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In this project we will be making a Soil Classification and Crop Recommendation System. This appplication will help the farmers to test the quality of the soil for the cultivation, so the farmers no need of going to the laboratories for testing the soil. By checking this we can find which crop can give more yield. With the help of a smartphone the farmers can test the soil by themselves. We are implementing this system by applying machine learning algorithm. The models are trained on the basis of a large dataset, so it will increase the accuracy of the model.
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Teixeira, Igor, Raul Morais, Joaquim J. Sousa, and António Cunha. "Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review." Agriculture 13, no. 5 (2023): 965. http://dx.doi.org/10.3390/agriculture13050965.
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In recent years, the use of remote sensing data obtained from satellite or unmanned aerial vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield prediction, soil classification or crop mapping. The ready availability of information, with improved temporal, radiometric, and spatial resolution, has resulted in the accumulation of vast amounts of data. Meeting the demands of analysing this data requires innovative solutions, and artificial intelligence techniques offer the necessary support. This systematic review aims to evaluate the effectiveness of deep learning techniques for crop classification using remote sensing data from aerial imagery. The reviewed papers focus on a variety of deep learning architectures, including convolutional neural networks (CNNs), long short-term memory networks, transformers, and hybrid CNN-recurrent neural network models, and incorporate techniques such as data augmentation, transfer learning, and multimodal fusion to improve model performance. The review analyses the use of these techniques to boost crop classification accuracy by developing new deep learning architectures or by combining various types of remote sensing data. Additionally, it assesses the impact of factors like spatial and spectral resolution, image annotation, and sample quality on crop classification. Ensembling models or integrating multiple data sources tends to enhance the classification accuracy of deep learning models. Satellite imagery is the most commonly used data source due to its accessibility and typically free availability. The study highlights the requirement for large amounts of training data and the incorporation of non-crop classes to enhance accuracy and provide valuable insights into the current state of deep learning models and datasets for crop classification tasks.
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Sardeshmukh, Mhalsakant, Midhun Chakkaravarthy, Sagar Shinde, and Divya Chakkaravarthy. "Crop image classification using convolutional neural network." Multidisciplinary Science Journal 5, no. 4 (2023): 2023039. http://dx.doi.org/10.31893/multiscience.2023039.
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A crop image classification using convolutional neural network is proposed in the paper. Classification of crop images is important and required in many applications such as yield prediction, decease detection etc. (Yang et al., 2020)(Kavitha et al., 2022). The main challenges are availability of the large dataset and extraction of meaningful features to describe a class of image(Barbedo, 2018). We have proposed a convolutional neural network to and the pre-trained models like VGG 16 and Resnet 50 for crop image classification. The pre-trained models trained on millions of images for a very large class size. The results shows that VGG 16 can be best used for our application as gives the accuracy of more than 98 %. The CNN training accuracy is 93 % but testing accuracy is only 42%. This is due to the lack of training data available. The accuracy of the CNN can be improved using large dataset. The Resnet 50 fails for crop image classification.
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P, Venkatasaichandrakanth, and Iyapparaja M. "GROUNDNUT CROP PEST DETECTION AND CLASSIFICATION USING COMPREHENSIVE DEEP-LEARNING MODELS." Suranaree Journal of Science and Technology 31, no. 1 (2024): 020028(1–17). http://dx.doi.org/10.55766/sujst-2024-01-e02544.
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Pests pose a significant threat to crops, leading to substantial economic losses and decreased food production. Early detection and accurate classification of pests in crops are crucial for effective pest management strategies. In this study, we propose a method for pest detection and classification in groundnut crops using deep learning models. In this research, we compare the performance of three deep learning models, namely Custom CNN [proposed], LeNet-5, and VGG-16, for groundnut pest detection and classification. A comprehensive dataset containing images of diverse groundnut crop pests, including thrips, aphids, armyworms, and wireworms, from the IP102 dataset was utilized for model evaluation. The performance is evaluated using reliability metrics such as accuracy and loss. These findings demonstrate the utility of deep learning models for reliable pest classification of groundnut crops. The Custom CNN [proposed] model demonstrates high training accuracy but potential overfitting, while the VGG-16 model performs well on both training and test data, showcasing its ability to generalize. The models’ accuracy in predicting pest species underscores their capability to capture and utilize visual patterns for precise classification. These findings underscore the potential of deep learning models, particularly the VGG-16 model, for pest detection and classification in groundnut crops. The knowledge gained from this study can contribute to the development of practical pest management strategies and aid in maintaining crop health and productivity. Further analysis and comparisons with other models are recommended to comprehensively evaluate the competitiveness and suitability of deep learning models in real-world applications of pest detection and classification in agricultural settings.
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G, Suma. "Soil Classification and Crop Prediction." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (2021): 168–74. http://dx.doi.org/10.22214/ijraset.2021.37300.
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Agribusiness is the core of numerous nations and soil is the primary significant component of horticulture. There are diverse soil sorts and every sort has various highlights for various yields. In this field, presently a day's various techniques and models are utilized to build the amount of the harvests. So the primary motivation behind this of this task is to make a model that assists ranchers with realizing which harvest should take in a specific kind of soil. In this task, we measure the dirt pictures to produce an advanced soil characterization framework for rustic ranchers for minimal price. Tensorflow climate is utilized from this we can download the necessary bundles. We are utilizing two datasets, that is preparing set comprises of four sorts of soil Alluvial, Red, Dark, Earth and train set. Soil surface is the principle factor to be considered prior to doing development. In this methodology, we can gather 50 examples from the various areas of our country. The examples are shot under light condition utilizing an any camera. Soil pictures are handled through the various stages like Convolution layer is to separate highlights from the info picture, Max pool layer is to decrease the spatial component of the information volume for next layers, Drop out layer is arbitrarily sets input units to 0 with a recurrence of rate at each progression during preparing time, which forestalls over fitting, and different layers.
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GE, Shuang, JinShui ZHANG, and Shuang ZHU. "Spatial generalization ability analysis of deep learning crop classification models." National Remote Sensing Bulletin 27, no. 12 (2023): 2796–814. http://dx.doi.org/10.11834/jrs.20221408.
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Zhou, Yueyue, Hongping Yan, Kun Ding, Tingting Cai, and Yan Zhang. "Few-Shot Image Classification of Crop Diseases Based on Vision–Language Models." Sensors 24, no. 18 (2024): 6109. http://dx.doi.org/10.3390/s24186109.
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Accurate crop disease classification is crucial for ensuring food security and enhancing agricultural productivity. However, the existing crop disease classification algorithms primarily focus on a single image modality and typically require a large number of samples. Our research counters these issues by using pre-trained Vision–Language Models (VLMs), which enhance the multimodal synergy for better crop disease classification than the traditional unimodal approaches. Firstly, we apply the multimodal model Qwen-VL to generate meticulous textual descriptions for representative disease images selected through clustering from the training set, which will serve as prompt text for generating classifier weights. Compared to solely using the language model for prompt text generation, this approach better captures and conveys fine-grained and image-specific information, thereby enhancing the prompt quality. Secondly, we integrate cross-attention and SE (Squeeze-and-Excitation) Attention into the training-free mode VLCD(Vision-Language model for Crop Disease classification) and the training-required mode VLCD-T (VLCD-Training), respectively, for prompt text processing, enhancing the classifier weights by emphasizing the key text features. The experimental outcomes conclusively prove our method’s heightened classification effectiveness in few-shot crop disease scenarios, tackling the data limitations and intricate disease recognition issues. It offers a pragmatic tool for agricultural pathology and reinforces the smart farming surveillance infrastructure.
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Rahman, Md Shahinoor, Liping Di, Eugene Yu, Chen Zhang, and Hossain Mohiuddin. "In-Season Major Crop-Type Identification for US Cropland from Landsat Images Using Crop-Rotation Pattern and Progressive Data Classification." Agriculture 9, no. 1 (2019): 17. http://dx.doi.org/10.3390/agriculture9010017.
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Crop type information at the field level is vital for many types of research and applications. The United States Department of Agriculture (USDA) provides information on crop types for US cropland as a Cropland Data Layer (CDL). However, CDL is only available at the end of the year after the crop growing season. Therefore, CDL is unable to support in-season research and decision-making regarding crop loss estimation, yield estimation, and grain pricing. The USDA mostly relies on field survey and farmers’ reports for the ground truth to train image classification models, which is one of the major reasons for the delayed release of CDL. This research aims to use trusted pixels as ground truth to train classification models. Trusted pixels are pixels which follow a specific crop rotation pattern. These trusted pixels are used to train image classification models for the classification of in-season Landsat images to identify major crop types. Six different classification algorithms are investigated and tested to select the best algorithm for this study. The Random Forest algorithm stands out among selected algorithms. This study classified Landsat scenes between May and mid-August for Iowa. The overall agreements of classification results with CDL in 2017 are 84%, 94%, and 96% for May, June, and July, respectively. The classification accuracies have been assessed through 683 ground truth data points collected from the fields. The overall accuracies of single date multi-band image classification are 84%, 89% and 92% for May, June, and July, respectively. The result also shows higher accuracy (94–95%) can be achieved through multi-date image classification compared to single date image classification.
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Phartiyal, G. S., and D. Singh. "COMPARATIVE STUDY ON DEEP NEURAL NETWORK MODELS FOR CROP CLASSIFICATION USING TIME SERIES POLSAR AND OPTICAL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 675–81. http://dx.doi.org/10.5194/isprs-archives-xlii-5-675-2018.
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<p><strong>Abstract.</strong> Crop classification is an important task in many crop monitoring applications. Satellite remote sensing has provided easy, reliable, and fast approaches to crop classification task. In this study, a comparative analysis is made on the performances of various deep neural network (DNN) models for crop classification task using polarimetric synthetic aperture radar (PolSAR) and optical satellite data. For PolSAR data, Sentinel 1 dual pol SAR data is used. Sentinel 2 multispectral data is used as optical data. Five land cover classes including two crop classes of the season are taken. Time series data over the period of one crop cycle is used. Training and testing samples are measured and collected directly from the ground over the study region. Various convolutional neural network (CNN) and long short-term memory (LSTM) models are implemented, analysed, evaluated, and compared. Models are evaluated on the basis of classification accuracy and generalization performance.</p>
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Balasani Raghupathi and Dr. Amit Sharma. "Optimizing Chilli Crop Disease Classification Models through Hybrid Differential Evolution and Simulated Annealing." International Journal of Scientific Research in Science and Technology 11, no. 6 (2024): 768–79. https://doi.org/10.32628/ijsrst241161132.
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Crop diseases caused by chillies are a serious threat to food security and agricultural production. Timely intervention and mitigation efforts are contingent upon an accurate categorization of these disorders. Here, we provide a new method that combines the methods of Simulated Annealing (SA) and Differential Evolution (DE) to improve classification models for illnesses affecting chilli crops. Our hybrid strategy seeks to improve illness classification models' performance and efficiency by using the advantages of both optimization methods. We conducted experiments on a comprehensive dataset comprising diverse chilli crop disease instances. Results show that replacement of either optimization demonstrates greater accuracy and robustness of classification models than either method individually. Additionally, our approach is promising for applications in precision agriculture, giving farmers useful information for proactive disease management and crop protection in the real world. This research progresses the field of agricultural decision support systems by establishing a sound framework for optimizing chilli crop disease classification models with reliability, ensuring sustainable farming practices, and food security globally.
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Matsikira, Normias, Gideon Mazambani, and Martin Muduva. "A Comparative Study of Ensemble Classification Algorithms for Crop Yield Forecasting." Advances in Machine Learning & Artificial Intelligence 6, no. 1 (2025): 01–05. https://doi.org/10.33140/amlai.06.01.05.
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This study explores the application of ensemble learning techniques to improve predictive model accuracy. It focuses on combining classifiers to outperform individual models using structured and unstructured data from agricultural datasets. Artificial neural networks (ANNs) and ensemble methods were used to increase deep neural network efficiency. Experiments with different network structures, training iterations, and topologies were conducted, evaluating measures like sensitivity and specificity. The research also includes predicting crop yields using ensemble classification algorithms, comparing accuracy with conventional methods. The study highlights the importance of crop yield prediction for agricultural management and discusses the benefits of ensemble methods. Results show that Random Forest, XGBoost, AdaBoost, and ANNs perform well in predicting crop yields as compared to the other algorithms. This research contributes to understanding the impact of weather patterns and genotype on crop yields.
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Li, Qianjing, Jia Tian, and Qingjiu Tian. "Deep Learning Application for Crop Classification via Multi-Temporal Remote Sensing Images." Agriculture 13, no. 4 (2023): 906. http://dx.doi.org/10.3390/agriculture13040906.
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The combination of multi-temporal images and deep learning is an efficient way to obtain accurate crop distributions and so has drawn increasing attention. However, few studies have compared deep learning models with different architectures, so it remains unclear how a deep learning model should be selected for multi-temporal crop classification, and the best possible accuracy is. To address this issue, the present work compares and analyzes a crop classification application based on deep learning models and different time-series data to exploit the possibility of improving crop classification accuracy. Using Multi-temporal Sentinel-2 images as source data, time-series classification datasets are constructed based on vegetation indexes (VIs) and spectral stacking, respectively, following which we compare and evaluate the crop classification application based on time-series datasets and five deep learning architectures: (1) one-dimensional convolutional neural networks (1D-CNNs), (2) long short-term memory (LSTM), (3) two-dimensional-CNNs (2D-CNNs), (4) three-dimensional-CNNs (3D-CNNs), and (5) two-dimensional convolutional LSTM (ConvLSTM2D). The results show that the accuracy of both 1D-CNN (92.5%) and LSTM (93.25%) is higher than that of random forest (~ 91%) when using a single temporal feature as input. The 2D-CNN model integrates temporal and spatial information and is slightly more accurate (94.76%), but fails to fully utilize its multi-spectral features. The accuracy of 1D-CNN and LSTM models integrated with temporal and multi-spectral features is 96.94% and 96.84%, respectively. However, neither model can extract spatial information. The accuracy of 3D-CNN and ConvLSTM2D models is 97.43% and 97.25%, respectively. The experimental results show limited accuracy for crop classification based on single temporal features, whereas the combination of temporal features with multi-spectral or spatial information significantly improves classification accuracy. The 3D-CNN and ConvLSTM2D models are thus the best deep learning architectures for multi-temporal crop classification. However, the ConvLSTM architecture combining recurrent neural networks and CNNs should be further developed for multi-temporal image crop classification.
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Uppal, Ashima, Mahaveer Singh Naruka Hai, and Gaurav Tewari. "Classification Models for Plant Diseases Diagnosis: A Review." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 11 (2022): 91–106. http://dx.doi.org/10.17762/ijritcc.v10i11.5785.
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Plants are important source of our life. Crop production in a good figure and good quality is important to us. The diagnosis of a disease in a plant can be manual or automatic. But manual detection of disease in a plant is not always correct as sometimes it can be not be seen by naked eyes so an automatic method of detection of plant diseases should be there. It can make use of various artificial intelligence based or machine learning based methods. It is a tedious task as it needs to be identified in earlier stage so that it will not affect the entire crop. Disease affects all species of plant, both cultivated and wild. Plant disease occurrence and infection severity vary seasonally, regarding the environmental circumstances, the kinds of crops cultivated, and the existence of the pathogen. This review attempts to provide an exhaustive review of various plant diseases and its types, various methods to diagnose plant diseases and various classification models used so as to help researchers to identify the areas of scope where plant pathology can be improved.
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Mandliya, Dilip, and Dr Manish Vyas. "Crop Infestation Classification Using MIL-Attention Based CNN." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28309.
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Agriculture constantly faces various challenges including attacks from new pests and insects. With large farm sizes and plummeting manpower in the agricultural sector, it becomes challenging to continuously monitor crops for pest infestation. In this research paper, a specific type of pest attack known as the white fly attack has been investigated which affects a variety of crops. This paper presents a multiple instance learning based deep learning approach based on Convolutional Neural Networks for the detection of whitefly pests. A comparative analysis with conventional machine learning and deep learning techniques has also been presented. The performance of the proposed system has been evaluated in terms of the classification accuracy. The experimental results obtained show that the proposed technique attains a classification accuracy of 98.5% and outperforms both separate feature trained machine learning models and well as baseline deep learning models in terms of classification accuracy. Keywords: Precision Agriculture, Whitefly Pest Detection, Feature Extraction, Machine Learning, Deep Learning, Multi Instance Learning (MIL), Classification Accuracy.
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Suriya, S., and Hema A. "Weeds Classification using Convolutional Neural Network Architectures." Journal of Soft Computing Paradigm 5, no. 2 (2023): 116–33. http://dx.doi.org/10.36548/jscp.2023.2.003.
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Agriculture is an important sector for both human survival and economic growth. It has to be managed efficiently. This can be done by the use of technology in order to minimize human effort. It can be managed efficiently by following crop management tasks. One such crop management task is the identification and removal of weeds. Weeds are considered to be plants which are not required to be grown with the agricultural crops, because the weeds also utilize the water and nutrients like the agricultural crop and cause impact on the growth of agricultural crops. In order to identify weeds, deep learning technology can be used. The proposed system helps to classify weeds using Convolutional Neural Networks. This system employs models like, ResNet50, MobileNetV2 and InceptionV3, which are used for better classification. The system is evaluated based on these models, and all the three models have resulted in better accuracy.
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Solanki, Shital. "COMPARATIVE ASSESSMENT OF MACHINE LEARNING MODELS IN RICE CROP STAGE CLASSIFICATION." International Journal of Advanced Research 12, no. 01 (2024): 632–37. http://dx.doi.org/10.21474/ijar01/18164.
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The effective classification of rice crops plays a crucial role in optimizing agricultural management and enhancing yield forecasts. In this paper, we explored the efficacy of various machine learning (ML) techniques in advancing the classification of rice crops. Four machine learning classification algorithms, namely k-nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forests (RF), Decision Trees (DT), and XG Boost, are assessed using a dataset comprising rice crop images and environmental parameters. The studys findings reveal that XG Boost significantly outperforms other models, achieving an impressive accuracy of 96.78%, along with high precision and F1-Score. The Support Vector Machine also demonstrates strong performance with an accuracy of 93.83%. These findings emphasize the potential of ML in advancing agricultural practices and decision-making, highlighting the role of precision agriculture in food security.
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Singh, Jagdip. "Enhancing Crop Classification in Smart Agriculture Using Multiclass Deep Learning Models." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 2479–85. https://doi.org/10.22214/ijraset.2024.65630.
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Smart agriculture is increasingly recognized as a vital approach to addressing the challenges of modern farming, including the need for improved yield prediction and efficient resource management. A cornerstone of this innovation is the precise classification of crops, which directly impacts decision-making and resource allocation. In this study, we investigate the effectiveness of several state-of-the-art deep learning models—VGGNet, Sequential, Artificial Neural Network (ANN), and ResNet50—for multiclass crop classification. A diverse and extensive crop image dataset was employed to ensure a comprehensive evaluation. The methodology incorporates rigorous preprocessing steps to enhance data quality, followed by meticulous model training and validation to achieve reliable outcomes. Comparative analysis of these models reveals their relative strengths and limitations, providing insights into their applicability in different agricultural scenarios. The findings emphasize the transformative potential of deep learning in agriculture, enabling precise crop identification and early detection of health issues, thus supporting smarter, more sustainable farming practices. This work lays a foundation for integrating advanced AI solutions into agriculture, paving the way for increased efficiency and resilience in crop management systems.
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V, Shruthi. "Machine Learning Based Weed Crop Classification Using Raspberry PI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49185.
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CHAPTER 1 Introduction Weed-crop classification using machine learning is an emerging area of research in precision agriculture that aims to automate the process of identifying and distinguishing weeds from crops in agricultural fields. Weeds compete with crops for essential resources such as sunlight, water, and nutrients, ultimately reducing crop yield and quality. Traditional weed control methods, including manual weeding and blanket application of herbicides, are often labor-intensive, time-consuming, and environmentally harmful. To address these challenges, machine learning techniques, particularly supervised learning algorithms, have been increasingly applied to develop intelligent systems capable of performing accurate and efficient weed detection and classification. In this approach, machine learning models are trained on labeled datasets containing images of both crops and weeds. These models learn to extract relevant features and patterns that differentiate one plant type from another. Convolutional Neural Networks (CNNs), a popular deep learning architecture, are particularly effective in handling image-based classification tasks due to their ability to automatically learn hierarchical features from raw image data. Once trained, these models can be deployed on embedded systems or mobile platforms to perform real-time classification in the field. This enables precision spraying or mechanical removal of weeds without harming the crops, thereby improving agricultural productivity and sustainability.
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Kong, Weilang, Xiaoqi Huang, Jialin Liu, Min Liu, Luo Liu, and Yubin Guo. "Cascade Learning Early Classification: A Novel Cascade Learning Classification Framework for Early-Season Crop Classification." Remote Sensing 17, no. 10 (2025): 1783. https://doi.org/10.3390/rs17101783.
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Accurate early-season crop classification is critical for food security, agricultural applications and policymaking. However, when classification is performed earlier, the available time-series data gradually become scarce. Existing methods mainly focus on enhancing the model’s ability to extract features from limited data to address this challenge, but the extracted critical phenological information remains insufficient. This study proposes a Cascade Learning Early Classification (CLEC) framework, which consists of two components: data preprocessing and a cascade learning model. Data preprocessing generates high-quality time-series data from the optical, radar and thermodynamic data in the early stages of crop growth. The cascade learning model integrates a prediction task and a classification task, which are interconnected through the cascade learning mechanism. First, the prediction task is performed to supplement more time-series data of the growing stage. Then, crop classification is carried out. Meanwhile, the cascade learning mechanism is used to iteratively optimize the prediction and classification results. To validate the effectiveness of CLEC, we conducted early-season classification experiments on soybean, corn and rice in Northeast China. The experimental results show that CLEC significantly improves crop classification accuracy compared to the five state-of-the-art models in the early stages of crop growth. Furthermore, under the premise of obtaining reliable results, CLEC advances the earliest identifiable timing, moving from the flowing to the third true leaf stage for soybean and from the flooding to the sowing stage for rice. Although the earliest identifiable timing for corn remains unchanged, its classification accuracy improved. Overall, CLEC offers new ideas for solving early-season classification challenges.
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Vandana, Saini, Shalini, and Kumar Sharma Krishan. "Weed identification using machine learning models." i-manager’s Journal on Pattern Recognition 9, no. 2 (2022): 9. http://dx.doi.org/10.26634/jpr.9.2.19086.
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Image classification is a complex process and an important direction in the field of image processing. Image classification methods require learning and training stages. Using machine learning classification models in image classification gives better results. Decision Tree, Random Forest, Gradient Boosting, Bagging Classifier, Multi-Layer Perceptron (MLP) Classifier, and Support Vector Machine (SVM) are different machine-learning classification models. The goal of this paper is to analyze the machine learning classification models. These models classify 12 kinds of plant seedlings, of which 3 are crop seedlings and 9 are weed seedlings. This paper suggests that, when using a V2 Plant Seedlings dataset, the accuracy of SVM is 0.71 and the accuracy of other models is less compared to SVM. The experimental results in this paper show that the machine learning model SVM has a better solution effect and higher recognition accuracy. This paper focuses on model building, training, and assessing the quality of the model by generating a confusion matrix and a classification report.
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Zheng, Zuojun, Jianghao Yuan, Wei Yao, Hongxun Yao, Qingzhi Liu, and Leifeng Guo. "Crop Classification from Drone Imagery Based on Lightweight Semantic Segmentation Methods." Remote Sensing 16, no. 21 (2024): 4099. http://dx.doi.org/10.3390/rs16214099.
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Technological advances have dramatically improved precision agriculture, and accurate crop classification is a key aspect of precision agriculture (PA). The flexibility and real-time nature of UAVs have led them to become an important tool for acquiring agricultural data and enabling precise crop classification. Currently, crop identification relies heavily on complex high-precision models that often struggle to provide real-time performance. Research on lightweight models specifically for crop classification is also limited. In this paper, we propose a crop classification method based on UAV visible-light images based on PP-LiteSeg, a lightweight model proposed by Baidu. To improve the accuracy, a pyramid pooling module is designed in this paper, which integrates adaptive mean pooling and CSPC (Convolutional Spatial Pyramid Pooling) techniques to handle high-resolution features. In addition, a sparse self-attention mechanism is employed to help the model pay more attention to locally important semantic regions in the image. The combination of adaptive average pooling and the sparse self-attention mechanism can better handle different levels of contextual information. To train the model, a new dataset based on UAV visible-light images including nine categories such as rice, soybean, red bean, wheat, corn, poplar, etc., with a time span of two years was created for accurate crop classification. The experimental results show that the improved model outperforms other models in terms of accuracy and prediction performance, with a MIoU (mean intersection ratio joint) of 94.79%, which is 2.79% better than the original model. Based on the UAV RGB images demonstrated in this paper, the improved model achieves a better balance between real-time performance and accuracy. In conclusion, the method effectively utilizes UAV RGB data and lightweight deep semantic segmentation models to provide valuable insights for crop classification and UAV field monitoring.
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MATHARASI,, P. BAVITHRA. "Harvest Guard: Crop Loss Detection Enhanced with Inception-Based Models." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem02651.
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Diseases of plants are a major risk factor for world agriculture, causing severe crop loss and decreased food yields. This paper discusses the usage of deep learning models, that is InceptionV3 and InceptionResNetV2, in plant disease classification using the PlantVillage dataset. Preprocessing methods like the removal of duplicates and blur from images are performed to improve the performance of the model, and then data augmentation is used for enhanced learning. The data is systematically split into training, validation, and test sets to facilitate proper evaluation. Performance of the model is evaluated based on accuracy, precision, recall, and F1-score. The outcomes reveal that data augmentation significantly improves classification performance, with InceptionResNetV2 performing better than InceptionV3 in accuracy. Additionally, visual inspection of training patterns and mistakes gives insight into the strengths and weaknesses of the model. This study showcases the capability of deep learning in early detection of plant diseases and can lead to less crop loss and increased agricultural productivity.
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Osouli, Mohammadreza, and Faramarz F. Samavati. "Evaluation of Data Sufficiency for Interannual Knowledge Transfer of Crop Type Classification Models." Remote Sensing 16, no. 11 (2024): 2007. http://dx.doi.org/10.3390/rs16112007.
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We present a study on the effectiveness of using varying data sizes to transfer crop type classification models from one year to the next, emphasizing the balance between data sufficiency and model accuracy. The significance of crop detection through satellite imaging lies in its potential to enhance agricultural productivity and resource management. Machine learning, particularly techniques like long short-term memory (LSTM) models, has become instrumental in interpreting these satellite data due to its predictive accuracy and adaptability. However, the direct application of models trained in one year to subsequent years poses challenges due to variations in environmental conditions and agricultural practices. Fine-tuning pre-existing models is a prevalent strategy to overcome these temporal discrepancies, though it necessitates a careful evaluation of the quantity and relevance of new data. This study explores the cost–benefit of fine-tuning existing models versus developing new ones based on the quantity of new data, utilizing LSTM models for their transferability and practicality in agricultural applications. Experiments conducted using satellite data from farms in southern Alberta reveal that smaller datasets, with fewer than 25 fields per class, can effectively fine-tune models for accurate interannual classification, while larger datasets are more conducive to training new models. This poses a key challenge in optimizing data usage for crop classification, straddling the line between data sufficiency and computational efficiency. The findings offer valuable insights for optimizing data use in crop classification, benefiting both academic research and practical agricultural applications.
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Nalawade, Viraj, Bhagyashree Kadam, Chetan Jadhav, Gaurav Pabale, and Pradeep Kokane. "Crop Advisor: Intelligent Crop Recommendation System." Indian Journal of Agriculture Engineering 5, no. 1 (2025): 1–6. https://doi.org/10.54105/ijae.a1525.05010525.
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Agriculture has long been a cornerstone of the Indian economy, crucial in sustaining livelihoods and contributing to national growth. By 2024, the sector will contribute approximately 18-20% of India's GDP and employ nearly half of the population. It also ensures food security for over 1.4 billion people. However, crop yields per hectare continue to lag international standards, which has been a significant factor contributing to the rising suicide rates among farmers. This paper proposes a machine learning-based Crop Regulating System to assist farmers. The system takes inputs such as historical and current yield data, weather conditions, soil quality and fertiliser usage from farmers and predicts weather impact, rainfall, and disease effect to predict crop yield before sowing. Also, the system takes inputs such as current market data, sowed land, market import/export data, historical retail data, and consumer data for market demand analysis. Machine learning algorithms analyze this data to predict the market demand and the yield for a chosen crop. After that machine learning algorithms like Regression Forest (RF) and Support Vector Machine (SVM) were used to provide Decision support to Farmers. Regression models like Support Vector Machines (SVM) and Random Forests (RF), Multiple Linear Regression (MLR) and classification models like K-Nearest Neighbors (KNN) are utilized for Crop Yield Prediction. Time series models such as Auto Regressive Integrated Moving Average (ARIMA), and Genetics Algorithms (GAs) are used for Market Demand Analysis.
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Viraj, Nalawade. "Crop Advisor: Intelligent Crop Recommendation System." Indian Journal of Agriculture Engineering (IJAE) 5, no. 1 (2025): 1–6. https://doi.org/10.54105/ijae.A1525.05010525.
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<strong>Abstract: </strong>Agriculture has long been a cornerstone of the Indian economy, crucial in sustaining livelihoods and contributing to national growth. By 2024, the sector will contribute approximately 18-20% of India's GDP and employ nearly half of the population. It also ensures food security for over 1.4 billion people. However, crop yields per hectare continue to lag international standards, which has been a significant factor contributing to the rising suicide rates among farmers. This paper proposes a machine learning-based Crop Regulating System to assist farmers. The system takes inputs such as historical and current yield data, weather conditions, soil quality and fertiliser usage from farmers and predicts weather impact, rainfall, and disease effect to predict crop yield before sowing. Also, the system takes inputs such as current market data, sowed land, market import/export data, historical retail data, and consumer data for market demand analysis. Machine learning algorithms analyze this data to predict the market demand and the yield for a chosen crop. After that machine learning algorithms like Regression Forest (RF) and Support Vector Machine (SVM) were used to provide Decision support to Farmers. Regression models like Support Vector Machines (SVM) and Random Forests (RF), Multiple Linear Regression (MLR) and classification models like K-Nearest Neighbors (KNN) are utilized for Crop Yield Prediction. Time series models such as AutoRegressive Integrated Moving Average (ARIMA), and Genetics Algorithms (GAs) are used for Market Demand Analysis.
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Wang, Xue, Jiahua Zhang, Lan Xun, et al. "Evaluating the Effectiveness of Machine Learning and Deep Learning Models Combined Time-Series Satellite Data for Multiple Crop Types Classification over a Large-Scale Region." Remote Sensing 14, no. 10 (2022): 2341. http://dx.doi.org/10.3390/rs14102341.
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Accurate extraction of crop cultivated area and spatial distribution is essential for food security. Crop classification methods based on machine learning and deep learning and remotely sensed time-series data are widely utilized to detect crop planting area. However, few studies assess the effectiveness of machine learning and deep learning algorithm integrated time-series satellite data for identifying multiple crop type classification over a large-scale region. Hence, this study aims to evaluate the effectiveness of machine learning and deep learning models in crop classification and provide a framework for large-scale multiple crop type classification based on time-series of satellite data. The time-series of the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and NaE (combined NDVI and EVI) were adopted as input features, and four widely used machine learning models, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and their integration (Stacking) were compared to examine the performance of multiple crop types (i.e., wheat, corn, wheat-corn, early rice, and early rice-late rice) classification in the North China Plain. The performance of two types of deep learning classifiers: the One-dimensional Convolutional Neural Network (Conv1D) and Long Short-Term Memory Networks (LSTM), were also tested. The results showed that the NaE feature performed best among three input features, and the Stacking model produced the highest accuracy (77.12%) compared to other algorithms.
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Li, Jingtao, Yonglin Shen, and Chao Yang. "An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images." Remote Sensing 13, no. 1 (2020): 65. http://dx.doi.org/10.3390/rs13010065.
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Due to the increasing demand for the monitoring of crop conditions and food production, it is a challenging and meaningful task to identify crops from remote sensing images. The state-of the-art crop classification models are mostly built on supervised classification models such as support vector machines (SVM), convolutional neural networks (CNN), and long- and short-term memory neural networks (LSTM). Meanwhile, as an unsupervised generative model, the adversarial generative network (GAN) is rarely used to complete classification tasks for agricultural applications. In this work, we propose a new method that combines GAN, CNN, and LSTM models to classify crops of corn and soybeans from remote sensing time-series images, in which GAN’s discriminator was used as the final classifier. The method is feasible on the condition that the training samples are small, and it fully takes advantage of spectral, spatial, and phenology features of crops from satellite data. The classification experiments were conducted on crops of corn, soybeans, and others. To verify the effectiveness of the proposed method, comparisons with models of SVM, SegNet, CNN, LSTM, and different combinations were also conducted. The results show that our method achieved the best classification results, with the Kappa coefficient of 0.7933 and overall accuracy of 0.86. Experiments in other study areas also demonstrate the extensibility of the proposed method.
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Yao, Yihan, Jibo Yue, Yang Liu, et al. "Classification of Maize Growth Stages Based on Phenotypic Traits and UAV Remote Sensing." Agriculture 14, no. 7 (2024): 1175. http://dx.doi.org/10.3390/agriculture14071175.
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Maize, an important cereal crop and crucial industrial material, is widely used in various fields, including food, feed, and industry. Maize is also a highly adaptable crop, capable of thriving under various climatic and soil conditions. Against the backdrop of intensified climate change, studying the classification of maize growth stages can aid in adjusting planting strategies to enhance yield and quality. Accurate classification of the growth stages of maize breeding materials is important for enhancing yield and quality in breeding endeavors. Traditional remote sensing-based crop growth stage classifications mainly rely on time series vegetation index (VI) analyses; however, VIs are prone to saturation under high-coverage conditions. Maize phenotypic traits at different growth stages may improve the accuracy of crop growth stage classifications. Therefore, we developed a method for classifying maize growth stages during the vegetative growth phase by combining maize phenotypic traits with different classification algorithms. First, we tested various VIs, texture features (TFs), and combinations of VI and TF as input features to estimate the leaf chlorophyll content (LCC), leaf area index (LAI), and fractional vegetation cover (FVC). We determined the optimal feature inputs and estimation methods and completed crop height (CH) extraction. Then, we tested different combinations of maize phenotypic traits as input variables to determine their accuracy in classifying growth stages and to identify the optimal combination and classification method. Finally, we compared the proposed method with traditional growth stage classification methods based on remote sensing VIs and machine learning models. The results indicate that (1) when the VI+TFs are used as input features, random forest regression (RFR) shows a good estimation performance for the LCC (R2: 0.920, RMSE: 3.655 SPAD units, MAE: 2.698 SPAD units), Gaussian process regression (GPR) performs well for the LAI (R2: 0.621, RMSE: 0.494, MAE: 0.397), and linear regression (LR) exhibits a good estimation performance for the FVC (R2: 0.777, RMSE: 0.051, MAE: 0.040); (2) when using the maize LCC, LAI, FVC, and CH phenotypic traits to classify maize growth stages, the random forest (RF) classification method achieved the highest accuracy (accuracy: 0.951, precision: 0.951, recall: 0.951, F1: 0.951); and (3) the effectiveness of the growth stage classification based on maize phenotypic traits outperforms that of traditional remote sensing-based crop growth stage classifications.
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Pramod Kumar Dwivedi and Dr. Prabhat Pandey. "Crop Yield Prediction and Classification using the Agriculture Resources and Data Mining Techniques." Journal of Advances in Science and Technology 20, no. 2 (2024): 131–37. http://dx.doi.org/10.29070/krwa0448.
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This study investigates the application of data mining techniques in meteorological forecasting to enhance crop yield prediction accuracy Moreover, the integration of meteorological forecasting with agronomic models and geographical information systems (GIS) facilitates site-specific crop management and precision agriculture practices. By combining meteorological data with soil properties, crop phenology, and socio-economic factors, data mining techniques enable the development of predictive models that enhance crop yield potential and optimize resource allocation. By leveraging advanced data analytics and machine learning algorithms, agricultural stakeholders can make informed decisions, mitigate risks, and enhance productivity in the face of changing climatic conditions and environmental uncertainties.
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He, Shan, Peng Peng, Yiyun Chen, and Xiaomi Wang. "Multi-Crop Classification Using Feature Selection-Coupled Machine Learning Classifiers Based on Spectral, Textural and Environmental Features." Remote Sensing 14, no. 13 (2022): 3153. http://dx.doi.org/10.3390/rs14133153.
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Machine learning (ML) classifiers have been widely used in the field of crop classification. However, having inputs that include a large number of complex features increases not only the difficulty of data collection but also reduces the accuracy of the classifiers. Feature selection (FS), which can availably reduce the number of features by selecting and reserving the most essential features for crop classification, is a good tool to solve this problem effectively. Different FS methods, however, have dissimilar effects on various classifiers, so how to achieve the optimal combination of FS methods and classifiers to meet the needs of high-precision recognition of multiple crops remains an open question. This paper intends to address this problem by coupling the analysis of three FS methods and six classifiers. Spectral, textual, and environmental features are firstly extracted as potential classification indexes from time-series remote sensing images from France. Then, three FS methods are used to obtain feature subsets and combined with six classifiers for coupling analysis. On this basis, 18 multi-crop classification models (FS–ML models) are constructed. Additionally, six classifiers without FS are constructed for comparison. The training set and the validation set for these models are constructed by using the Kennard-Stone algorithm with 70% and 30% of the samples, respectively. The performance of the classification model is evaluated by Kappa, F1-score, accuracy, and other indicators. The results show that different FS methods have dissimilar effects on various models. The best FS–ML model is RFAA+-RF, and its Kappa coefficient can reach 0.7968, which is 0.33–46.67% higher than that of other classification models. The classification results are highly dependent on the original classification index sets. Hence, the reasonability of combining spectral, textural, and environmental indexes is verified by comparing them with the single feature index set. The results also show that the classification strategy combining spectral, textual, and environmental indexes can effectively improve the ability of crop recognition, and the Kappa coefficient is 9.06–65.52% higher than that of the single unscreened feature set.
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Tsuchiya, Yuta, and Rei Sonobe. "Crop Classification Using Time-Series Sentinel-1 SAR Data: A Comparison of LSTM, GRU, and TCN with Attention." Remote Sensing 17, no. 12 (2025): 2095. https://doi.org/10.3390/rs17122095.
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This study investigates the performance of temporal deep learning models with attention mechanisms for crop classification using Sentinel-1 C-band synthetic aperture radar (C-SAR) data. A time series of 16 scenes, acquired at 12-day intervals from 25 April to 22 October 2024, was used to classify six crop types: beans, beetroot, grassland, maize, potato, and winter wheat. Three temporal models—long short-term memory (LSTM), bidirectional gated recurrent unit (Bi-GRU), and temporal convolutional network (TCN)—were evaluated with and without an attention mechanism. All model configurations achieved accuracies above 83%, demonstrating the potential of Sentinel-1 SAR data for reliable, weather-independent crop classification. The TCN with attention model achieved the highest accuracy of 85.7%, significantly outperforming the baseline. LSTM also showed improved accuracy when combined with attention, whereas Bi-GRU did not benefit from the attention mechanism. These results highlight the effectiveness of combining temporal deep learning models with attention mechanisms to enhance crop classification using Sentinel-1 SAR time-series data. This study further confirms that freely available, regularly acquired Sentinel-1 observations are well-suited for robust crop mapping under diverse environmental conditions.
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Corceiro, Ana, Nuno Pereira, Khadijeh Alibabaei, and Pedro D. Gaspar. "Leveraging Machine Learning for Weed Management and Crop Enhancement: Vineyard Flora Classification." Algorithms 17, no. 1 (2023): 19. http://dx.doi.org/10.3390/a17010019.
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The global population’s rapid growth necessitates a 70% increase in agricultural production, posing challenges exacerbated by weed infestation and herbicide drawbacks. To address this, machine learning (ML) models, particularly convolutional neural networks (CNNs), are employed in precision agriculture (PA) for weed detection. This study focuses on testing CNN architectures for image classification tasks using the PyTorch framework, emphasizing hyperparameter optimization. Four groups of experiments were carried out: the first one trained all the PyTorch architectures, followed by the creation of a baseline, the evaluation of a new and extended dataset in the best models, and finally, the test phase was conducted using a web application developed for this purpose. Of 80 CNN sub-architectures tested, the MaxVit, ShuffleNet, and EfficientNet models stand out, achieving a maximum accuracy of 96.0%, 99.3%, and 99.3%, respectively, for the first test phase of PyTorch classification architectures. In addition, EfficientNet_B1 and EfficientNet_B5 stood out compared to all other models. During experiment 3, with a new dataset, both models achieved a high accuracy of 95.13% and 94.83%, respectively. Furthermore, in experiment 4, both EfficientNet_B1 and EfficientNet_B5 achieved a maximum accuracy of 96.15%, the highest one. ML models can help to automate crop problem detection, promote organic farming, optimize resource use, aid precision farming, reduce waste, boost efficiency, and contribute to a greener, sustainable agricultural future.
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R. Salini, Et al. "Analysis on Leaf Disease Identification using Classification Models." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1115–20. http://dx.doi.org/10.17762/ijritcc.v11i10.8632.
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The Researchers have all been aware of the rising food demand brought on by the population's rapid growth and the high mortality rates caused by medical developments. One of the many farming practises where computerization in agriculture has made significant progress is the identification of numerous plant diseases. The focus of almost every nation has shifted towards mechanising agriculture in order to achieve accuracy and precision and to meet the continually increasing demand for food. Identification of plant diseases is one of the most difficult tasks in agriculture and has a significant effect on crop yield. Artificial intelligence has recently begun to concentrate on smart agriculture science.Ground-breaking methods in plant science through deep learning and hyperspectral imaging to locate and recognise plant diseases has been addressed in this study.
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GC, Sunil, Yu Zhang, Kirk Howatt, Leon G. Schumacher, and Xin Sun. "Multi-Species Weed and Crop Classification Comparison Using Five Different Deep Learning Network Architectures." Journal of the ASABE 67, no. 2 (2024): 43–55. http://dx.doi.org/10.13031/ja.15590.
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Highlights ConvNeXt model was applied for weed and crop species classification, a first in this field. Five CNN architectures were utilized to classify six weed species and eight crop species. CNN architectures with millions of parameters were trained for improved performance. All CNN models showcased impressive performance, except for Densenet. Abstract. The detection of individual weed and crop species from RGB images is a challenging task that becomes even more difficult as the number of species increases. This is because similarities in the phenotypic traits of weeds and crops make it difficult to accurately distinguish one species from another. In this study, five deep learning Convolutional Neural Networks (CNNs) were employed to classify six weed and eight crop species from North Dakota and assess the performance of each model for specific species from a single image. An automated data acquisition system was utilized to collect and process RGB images twice in a greenhouse setting. The first set of data was used to train the CNN models by updating all of its convolutional layers, while the second set was used to evaluate the performance of the models. The results showed that all CNN architectures, except Densenet, demonstrated strong performance, with macro average f1-scores (measurement of model accuracy) ranging from 0.85 to 0.87 and weighted average f1-scores ranging from 0.87 to 0.88. The presence of three weed classes—palmer amaranth, redroot pigweed, and waterhemp, all of which share similar phenotypic traits—negatively affected the model's performance. In conclusion, the results of this study indicate that CNN architectures hold great potential for classifying weed and crop species in North Dakota, with the exception of situations where plants have similar visible characteristics. Keywords: Deep learning, Precision agriculture, Weed and crop classification.
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Sowmya, M., M. Balasubramanian, and K. Vaidehi. "Classification of Animal Behaviour Using Deep Learning Models." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 13 (December 31, 2024): e31638. https://doi.org/10.14201/adcaij.31638.
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Damage to crops by animal intrusion is one of the biggest threats to crop yield. People who stay near forest areas face a major issue with animals. The most significant task in deep learning is animal behaviour classification. This article focuses on the classification of distinct animal behaviours such as sitting, standing, eating etc. The proposed system detects animal behaviours in real time using deep learning-based models, namely, convolution neural network and transfer learning. Specifically, 2D-CNN, VGG16 and ResNet50 architectures have been used for classification. 2D-CNN, «VGG-16» and «ResNet50» have been trained on the video frames displaying a range of animal behaviours. The real time behaviour dataset contains 682 images of animals eating, 300 images of animas sitting and 1002 images of animals standing, therefore, there is a total of 1984 images in the training dataset. The experiment shows good accuracy results on the real time dataset, achieving 99.43 % with Resnet50 compared to 2D CNN ,VGG19 and VGG166.
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Singh, Rajwinder, Rahul Rana, and Sunil Kr Singh. "Performance Evaluation of VGG models in Detection of Wheat Rust." Asian Journal of Computer Science and Technology 7, no. 3 (2018): 76–81. http://dx.doi.org/10.51983/ajcst-2018.7.3.1892.
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The agricultural sector is the backbone of Indian economy and social development but due to lack of awareness towards crop management, a large number of crops get wasted each year. Automated Systems are required for this purpose. This paper tries to highlight the efficiency of two existing models of deep learning, VGG16 and VGG19 for proper detection of wheat rust disease in the infected wheat crop. These two models use convolutional neural networks for image classification and which can be used to design an intelligent system which can easily detect wheat rust in crop images. This paper basically presents the comparative analysis of the accuracy and efficiency along with usability to select the best model for systems that can be used for crop safety.
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Li, Zhang, Zhang, and Atkinson. "A hybrid OSVM-OCNN Method for Crop Classification from Fine Spatial Resolution Remotely Sensed Imagery." Remote Sensing 11, no. 20 (2019): 2370. http://dx.doi.org/10.3390/rs11202370.
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Accurate information on crop distribution is of great importance for a range of applications including crop yield estimation, greenhouse gas emission measurement and management policy formulation. Fine spatial resolution (FSR) remotely sensed imagery provides new opportunities for crop mapping at a detailed level. However, crop classification from FSR imagery is known to be challenging due to the great intra-class variability and low inter-class disparity in the data. In this research, a novel hybrid method (OSVM-OCNN) was proposed for crop classification from FSR imagery, which combines a shallow-structured object-based support vector machine (OSVM) with a deep-structured object-based convolutional neural network (OCNN). Unlike pixel-wise classification methods, the OSVM-OCNN method operates on objects as the basic units of analysis and, thus, classifies remotely sensed images at the object level. The proposed OSVM-OCNN harvests the complementary characteristics of the two sub-models, the OSVM with effective extraction of low-level within-object features and the OCNN with capture and utilization of high-level between-object information. By using a rule-based fusion strategy based primarily on the OCNN’s prediction probability, the two sub-models were fused in a concise and effective manner. We investigated the effectiveness of the proposed method over two test sites (i.e., S1 and S2) that have distinctive and heterogeneous patterns of different crops in the Sacramento Valley, California, using FSR Synthetic Aperture Radar (SAR) and FSR multispectral data, respectively. Experimental results illustrated that the new proposed OSVM-OCNN approach increased markedly the classification accuracy for most of crop types in S1 and all crop types in S2, and it consistently achieved the most accurate accuracy in comparison with its two object-based sub-models (OSVM and OCNN) as well as the pixel-wise SVM (PSVM) and CNN (PCNN) methods. Our findings, thus, suggest that the proposed method is as an effective and efficient approach to solve the challenging problem of crop classification using FSR imagery (including from different remotely sensed platforms). More importantly, the OSVM-OCNN method is readily generalisable to other landscape classes and, thus, should provide a general solution to solve the complex FSR image classification problem.
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Kuang, Xiaofei, Jiao Guo, Jingyuan Bai, Hongsuo Geng, and Hui Wang. "Crop-Planting Area Prediction from Multi-Source Gaofen Satellite Images Using a Novel Deep Learning Model: A Case Study of Yangling District." Remote Sensing 15, no. 15 (2023): 3792. http://dx.doi.org/10.3390/rs15153792.
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Neural network models play an important role in crop extraction based on remote sensing data. However, when dealing with high-dimensional remote sensing data, these models are susceptible to performance degradation. In order to address the challenges associated with multi-source Gaofen satellite data, a novel method is proposed for dimension reduction and crop classification. This method combines the benefits of the stacked autoencoder network for data dimensionality reduction, and the convolutional neural network for classification. By leveraging the advantages of multi-dimensional remote sensing information, and mitigating the impact of dimensionality on the classification accuracy, this method aims to improve the effectiveness of crop classification. The proposed method was applied to the extraction of crop-planting areas in the Yangling Agricultural Demonstration Zone, using multi-temporal spectral data collected from the Gaofen satellites. The results demonstrate that the fusion network, which extracts low-dimensional characteristics, offers advantages in classification accuracy. At the same time, the proposed model is compared with methods such as the decision tree (DT), random forest (RF), support vector machine (SVM), hyperspectral image classification based on a convolutional neural network (HICCNN), and a characteristic selection classification method based on a convolutional neural network (CSCNN). The overall accuracy of the proposed method can reach 98.57%, which is 7.95%, 4.69%, 5.68%, 1.21%, and 1.10% higher than the above methods, respectively. The effectiveness of the proposed model was verified through experiments. Additionally, the model demonstrates a strong robustness when classifying based on new data. When extracting the crop area of the entire Yangling District, the errors for wheat and corn are only 9.6% and 6.3%, respectively, and the extraction results accurately reflect the actual planting situation of crops.
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Rusňák, Tomáš, Tomáš Kasanický, Peter Malík, et al. "Crop Mapping without Labels: Investigating Temporal and Spatial Transferability of Crop Classification Models Using a 5-Year Sentinel-2 Series and Machine Learning." Remote Sensing 15, no. 13 (2023): 3414. http://dx.doi.org/10.3390/rs15133414.
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Multitemporal crop classification approaches have demonstrated high performance within a given season. However, cross-season and cross-region crop classification presents a unique transferability challenge. This study addresses this challenge by adopting a domain generalization approach, e.g., by training models on multiple seasons to improve generalization to new, unseen target years. We utilize a comprehensive five-year Sentinel-2 dataset over different agricultural regions in Slovakia and a diverse crop scheme (eight crop classes). We evaluate the performance of different machine learning classification algorithms, including random forests, support vector machines, quadratic discriminant analysis, and neural networks. Our main findings reveal that the transferability of models across years differs between regions, with the Danubian lowlands demonstrating better performance (overall accuracies ranging from 91.5% in 2022 to 94.3% in 2020) compared to eastern Slovakia (overall accuracies ranging from 85% in 2022 to 91.9% in 2020). Quadratic discriminant analysis, support vector machines, and neural networks consistently demonstrated high performance across diverse transferability scenarios. The random forest algorithm was less reliable in generalizing across different scenarios, particularly when there was a significant deviation in the distribution of unseen domains. This finding underscores the importance of employing a multi-classifier analysis. Rapeseed, grasslands, and sugar beet consistently show stable transferability across seasons. We observe that all periods play a crucial role in the classification process, with July being the most important and August the least important. Acceptable performance can be achieved as early as June, with only slight improvements towards the end of the season. Finally, employing a multi-classifier approach allows for parcel-level confidence determination, enhancing the reliability of crop distribution maps by assuming higher confidence when multiple classifiers yield similar results. To enhance spatiotemporal generalization, our study proposes a two-step approach: (1) determine the optimal spatial domain to accurately represent crop type distribution; and (2) apply interannual training to capture variability across years. This approach helps account for various factors, such as different crop rotation practices, diverse observational quality, and local climate-driven patterns, leading to more accurate and reliable crop classification models for nationwide agricultural monitoring.
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Huang, Qixuan. "Comparison of Deep Transfer Learning Models for Pest Image Classification in Agriculture." Highlights in Science, Engineering and Technology 94 (April 26, 2024): 9–16. http://dx.doi.org/10.54097/kxbxjn03.
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In the field of agriculture, crops are susceptible to attacks by pests. Accurately identifying and classifying crop pests, especially in their early stages of growth, is a significant challenge. Convolutional neural networks have become effective instruments for agricultural pest classification because of their capacity to extract and learn intricate information from photos. This study explores the use of transfer learning methods to compare efficient models with complex models to improve the effectiveness of pest and disease classification. The purpose of this study is to compare efficient models with complicated models using transfer learning techniques in order to increase the accuracy of pest and disease classification. By conducting experiments on the same pest and disease dataset, this research compares their performance in terms of accuracy, model size, computational resource consumption, and practical feasibility in real-world applications. The research results clearly demonstrate the performance comparison between different models, with efficient models excelling in some aspects while large models have advantages in others. This study contributes to better understanding for decision-makers in the agricultural sector on how to choose the appropriate deep learning models to enhance crop protection and increase yields.
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Yoon, Hyoup-Sang, and Seok-Bong Jeong. "Performance Comparison of Base CNN Models in Transfer Learning for Crop Diseases Classification." Journal of Society of Korea Industrial and Systems Engineering 44, no. 3 (2021): 33–38. http://dx.doi.org/10.11627/jkise.2021.44.3.033.
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Abasi, Ammar Kamal, Sharif Naser Makhadmeh, Osama Ahmad Alomari, Mohammad Tubishat, and Husam Jasim Mohammed. "Enhancing Rice Leaf Disease Classification: A Customized Convolutional Neural Network Approach." Sustainability 15, no. 20 (2023): 15039. http://dx.doi.org/10.3390/su152015039.
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In modern agriculture, correctly identifying rice leaf diseases is crucial for maintaining crop health and promoting sustainable food production. This study presents a detailed methodology to enhance the accuracy of rice leaf disease classification. We achieve this by employing a Convolutional Neural Network (CNN) model specifically designed for rice leaf images. The proposed method achieved an accuracy of 0.914 during the final epoch, demonstrating highly competitive performance compared to other models, with low loss and minimal overfitting. A comparison was conducted with Transfer Learning Inception-v3 and Transfer Learning EfficientNet-B2 models, and the proposed method showed superior accuracy and performance. With the increasing demand for precision agriculture, models like the proposed one show great potential in accurately detecting and managing diseases, ultimately leading to improved crop yields and ecological sustainability.
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McCormick, Richard, Prasad S. Thenkabail, Itiya Aneece, Pardhasaradhi Teluguntla, Adam J. Oliphant, and Daniel Foley. "Artificial Neural Network Multi-layer Perceptron Models to Classify California's Crops using Harmonized Landsat Sentinel (HLS) Data." Photogrammetric Engineering & Remote Sensing 91, no. 2 (2025): 91–100. https://doi.org/10.14358/pers.24-00072r3.
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Advances in remote sensing and machine learning are enhancing cropland classification, vital for global food and water security. We used multispectral Harmonized Landsat 8 Sentinel-2 (HLS) 30-m data in an artificial neural network (ANN) multi-layer perceptron (MLP) model to classify five crop classes (cotton, alfalfa, tree crops, grapes, and others) in California's Central Valley. The ANN MLP model, trained on 2021 data from the United States Department of Agriculture's Cropland Data Layer, was validated by classifying crops for an independent year, 2022. Across the five crop classes, the overall accuracy was 74%. Producer's and user's accuracies ranged from 65% to 87%, with cotton achieving the highest accuracies. The study highlights the potential of using deep learning with HLS time series data for accurate global crop classification.
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Gulzar, Yonis, Zeynep Ünal, Hakan Aktaş, and Mohammad Shuaib Mir. "Harnessing the Power of Transfer Learning in Sunflower Disease Detection: A Comparative Study." Agriculture 13, no. 8 (2023): 1479. http://dx.doi.org/10.3390/agriculture13081479.
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Sunflower is an important crop that is susceptible to various diseases, which can significantly impact crop yield and quality. Early and accurate detection of these diseases is crucial for implementing appropriate management strategies. In recent years, deep learning techniques have shown promising results in the field of disease classification using image data. This study presents a comparative analysis of different deep-learning models for the classification of sunflower diseases. five widely used deep learning models, namely AlexNet, VGG16, InceptionV3, MobileNetV3, and EfficientNet were trained and evaluated using a dataset of sunflower disease images. The performance of each model was measured in terms of precision, recall, F1-score, and accuracy. The experimental results demonstrated that all the deep learning models achieved high precision, recall, F1-score, and accuracy values for sunflower disease classification. Among the models, EfficientNetB3 exhibited the highest precision, recall, F1-score, and accuracy of 0.979. whereas the other models, ALexNet, VGG16, InceptionV3 and MobileNetV3 achieved 0.865, 0.965, 0.954 and 0.969 accuracy respectively. Based on the comparative analysis, it can be concluded that deep learning models are effective for the classification of sunflower diseases. The results highlight the potential of deep learning in early disease detection and classification, which can assist farmers and agronomists in implementing timely disease management strategies. Furthermore, the findings suggest that models like MobileNetV3 and EfficientNetB3 could be preferred choices due to their high performance and relatively fewer training epochs.
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Ajay, Rasave, and Sunita R. Patil Dr. "Crop Disease Detection using CNN." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 1816–20. https://doi.org/10.5281/zenodo.7998414.
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Good health is a first priority of all human being. And to maintain health the first need is a healthy food, which we get from our agriculture resources. According to the United Nations’ World Population Prospects (WPP), India is projected to surpass China as the world’s most populous country in 2023. And to maintain the appetite and health of such huge population it’s very important for agriculture field to grow the more and healthy crops. Now a days there are number of diseases which are causing crop underproduction. Delay in identifying the actual disease will result into more loss and cost of remedy. So, it’s very important to have the knowledge of diseases for early detection. In today’s date Artificial intelligence technologies such as Classification algorithms using Deep Learning are so efficient and are capable of predicting the disease based on the images of the crops. Hence, in this paper have reviewed the most efficient classification algorithms and proposed few most suitable models for image-based disease detection.