Relevant bibliographies by topics / Crop Classification Models

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Crop Classification Models'

Author: Grafiati
Published: 7 June 2025
Last updated: 17 July 2025

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Journal articles on the topic "Crop Classification Models"

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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 (
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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 lear
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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, e
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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)
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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 suc
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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 lear
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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 la
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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, i
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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 p
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Dissertations / Theses on the topic "Crop Classification Models"

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Al-Shammari, Dhahi Turki Jadah. "Remote sensing applications for crop type mapping and crop yield prediction for digital agriculture." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29771.

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This thesis addresses important topics in agricultural modelling research. Chapter 1 describes the importance of land productivity and the pressure on the agricultural sector to provide food. In chapter 2, a summer crop type mapping model has been developed to map major cotton fields in-season in the Murray Darling Basin (MDB) in Australia. In chapter 3, a robust crop classification model has been designed to classify two major crops (cereals and canola) in the MDB in Australia. chapter 4 focused on exploring changes in prediction quality with changes in the spatial resolution of predictors an
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Dinh, Thi Lan Anh. "Crop yield simulation using statistical and machine learning models. From the monitoring to the seasonal and climate forecasting." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS425.

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La météo et le climat ont un impact important sur les rendements agricoles. De nombreuses études basées sur différentes approches ont été réalisées pour mesurer cet impact. Cette thèse se concentre sur les modèles statistiques pour mesurer la sensibilité des cultures aux conditions météorologiques sur la base des enregistrements historiques. Lors de l'utilisation d'un modèle statistique, une difficulté critique survient lorsque les données sont rares, ce qui est souvent le cas pour la modélisation des cultures. Il y a un risque élevé de sur-apprentissage si le modèle n'est pas développé avec c
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Mathieu, Jordane. "Modèles d'impact statistiques en agriculture : de la prévision saisonnière à la prévision à long terme, en passant par les estimations annuelles." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE006/document.

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En agriculture, la météo est le principal facteur de variabilité d’une année sur l’autre. Cette thèse vise à construire des modèles statistiques à grande échelle qui estiment l’impact des conditions météorologiques sur les rendements agricoles. Le peu de données agricoles disponibles impose de construire des modèles simples avec peu de prédicteurs, et d’adapter les méthodes de sélection de modèles pour éviter le sur-apprentissage. Une grande attention a été portée sur la validation des modèles statistiques. Des réseaux de neurones et modèles à effets mixtes (montrant l’importance des spécifici
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Mathieu, Jordane. "Modèles d'impact statistiques en agriculture : de la prévision saisonnière à la prévision à long terme, en passant par les estimations annuelles." Electronic Thesis or Diss., Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE006.

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En agriculture, la météo est le principal facteur de variabilité d’une année sur l’autre. Cette thèse vise à construire des modèles statistiques à grande échelle qui estiment l’impact des conditions météorologiques sur les rendements agricoles. Le peu de données agricoles disponibles impose de construire des modèles simples avec peu de prédicteurs, et d’adapter les méthodes de sélection de modèles pour éviter le sur-apprentissage. Une grande attention a été portée sur la validation des modèles statistiques. Des réseaux de neurones et modèles à effets mixtes (montrant l’importance des spécifici
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Spank, Uwe, Barbara Köstner, Uta Moderow, Thomas Grünwald, and Christian Bernhofer. "Surface Conductance of Five Different Crops Based on 10 Years of Eddy-Covariance Measurements." Schweizerbart Science Publishers, 2016. https://tud.qucosa.de/id/qucosa%3A29981.

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The Penman-Monteith (PM) equation is a state-of-the-art modelling approach to simulate evapotranspiration (ET) at site and local scale. However, its practical application is often restricted by the availability and quality of required parameters. One of these parameters is the canopy conductance. Long term measurements of evapotranspiration by the eddy-covariance method provide an improved data basis to determine this parameter by inverse modelling. Because this approach may also include evaporation from the soil, not only the ‘actual’ canopy conductance but the whole surface conductance (gc)
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Gadedjisso-Tossou, Agossou. "Impact of Climate and Soil Variability on Crop Water Productivity and Food Security of Irrigated Agriculture in Northern Togo (West Africa)." 2019. https://tud.qucosa.de/id/qucosa%3A38704.

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West Africa is subject to frequent yield losses due to erratic rainfall and degraded soils. At the same time, its population is expected to double by 2050. This situation is alarming in northern Togo, a West African dry savannah area, where rainfed maize is a staple food. Thus, it is necessary to improve agricultural productivity, e.g., by evaluating and introducing alternative irrigation management strategies, which may be implemented in this region. For this purpose, the present investigation focused on evaluating the potential of deficit and supplemental irrigation, as well as assessing the
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Books on the topic "Crop Classification Models"

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1927-, Hanks R. J., and Ritchie J. T. 1937-, eds. Modeling plant and soil systems. American Society of Agronomy, 1991.

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Book chapters on the topic "Crop Classification Models"

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Tiwari, Vivek, Himanshu Patel, Ritvik Muttreja, et al. "Real-Time Soybean Crop Insect Classification Using Customized Deep Learning Models." In Data Management, Analytics and Innovation. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2934-1_9.

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Dheeraj, Akshay, and Satish Chand. "Using Deep Learning Models for Crop and Weed Classification at Early Stage." In Advances in Intelligent Systems and Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5443-6_69.

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Houetohossou, Sèton Calmette Ariane, Castro Gbêmêmali Hounmenou, Vinasetan Ratheil Houndji, and Romain Glèlè Kakaï. "Empirical Performance of Deep Learning Models with Class Imbalance for Crop Disease Classification." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-66705-3_8.

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Lopez-Sanchez, J. M., J. D. Ballester-Berman, F. Vicente-Guijalba, et al. "Agriculture and Wetland Applications." In Polarimetric Synthetic Aperture Radar. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56504-6_3.

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AbstractBased on experimental results, this chapter describes applications of SAR polarimetry to extract relevant information on agriculture and wetland scenarios by exploiting differences in the polarimetric signature of different scatterers, crop types and their development stage depending on their physical properties. Concerning agriculture, crop type mapping, soil moisture estimation and phenology estimation are reviewed, as they are ones with a clear benefit of full polarimetry over dual or single polarimetry. For crop type mapping, supervised or partially unsupervised classification schemes are used. Phenology estimation is treated as a classification problem as well, by regarding the different stages as different classes. Soil moisture estimation makes intensive use of scattering models, in order to separate soil and vegetation scattering and to invert for soil moisture from the isolated ground component. Then, applications of SAR polarimetry to wetland monitoring are considered that include the delineation of their extent and their characterisation by means of polarimetric decompositions. In the last section of the chapter, the use of a SAR polarimetric decomposition is shown for the assessment of the damages consequential to earthquakes and tsunamis.
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Rana, Abhishek, and Neelam Goel. "Detection and Classification of Tomato Crop Disease Using Deep Learning Models with Varied Optimization Techniques." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2697-7_16.

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Justina Michael, J., and M. Thenmozhi. "Evaluation of Deep Learning CNN Models with 24 Metrics Using Soybean Crop and Broad-Leaf Weed Classification." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5166-6_6.

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Menne, Hubert, and Helmut Köcher. "HRAC Classification of Herbicides and Resistance Development." In Modern Crop Protection Compounds. Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527644179.ch1.

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Kuck, Karl-Heinz, Andy Leadbeater, and Ulrich Gisi. "FRAC Mode of Action Classification and Resistance Risk of Fungicides." In Modern Crop Protection Compounds. Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527644179.ch14.

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Nauen, Ralf, Alfred Elbert, Alan McCaffery, Russell Slater, and Thomas C. Sparks. "IRAC: Insecticide Resistance, and Mode of Action Classification of Insecticides." In Modern Crop Protection Compounds. Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527644179.ch27.

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Hermann, Dietrich, and Klaus Stenzel. "FRAC Mode-of-action Classification and Resistance Risk of Fungicides." In Modern Crop Protection Compounds. Wiley-VCH Verlag GmbH & Co. KGaA, 2019. http://dx.doi.org/10.1002/9783527699261.ch14.

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Conference papers on the topic "Crop Classification Models"

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Kovačević, Vladimir, Branislav Pejak, and Oskar Marko. "Enhancing Machine Learning Crop Classification Models through SAM-Based Field Delineation Based on Satellite Imagery." In 2024 12th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). IEEE, 2024. http://dx.doi.org/10.1109/agro-geoinformatics262780.2024.10661028.

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Perantoni, Gianmarco, Giulio Weikmann, and Lorenzo Bruzzone. "Bayesian Modelling of Multi-Year Crop Type Classification Using Deep Neural Networks and Hidden Markov Models." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642432.

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Pinkaeo, Montree, Phaisarn Jeefoo, Jirabhorn Chaiwongsai, Surachai Chantee, Pornthep Rojanavasu, and Prattana Deeprasertkul. "Assessing the Performance of Machine Learning Models Using Satellite Dataset for Classification of Various Crop Types." In 2024 Geoinformatics for Spatial-Infrastructure Development in Earth and Allied Sciences (GIS-IDEAS). IEEE, 2024. https://doi.org/10.1109/gis-ideas63212.2024.10991049.

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Tiwari, Raj Gaurang, Alok Misra, Krishan Dutt, Ravinder Singh, and Amninder Kaur. "Transfer Learning for Classification of Horticultural Maladies: Leveraging Pre-Trained Models for Disease Detection in Crop Plants." In 2024 2nd World Conference on Communication & Computing (WCONF). IEEE, 2024. http://dx.doi.org/10.1109/wconf61366.2024.10692234.

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Yun, Dong Kyu, Seong Ju Joe, Ki Tae Park, Seok Kee Lee, and Sang Hyun Choi. "Comparison of Crop Image Classification Model Performance According to Image Augmentation Techniques." In 2025 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2025. https://doi.org/10.1109/bigcomp64353.2025.00074.

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Singh, Shashank, Pallav Prakash, Gulshan Baghel, Anuj Singh, Dharm Raj, and Amrit Kumar Agrawal. "Banana Crop Health: A Deep Learning-Based Model for Disease Detection and Classification." In 2024 27th International Symposium on Wireless Personal Multimedia Communications (WPMC). IEEE, 2024. https://doi.org/10.1109/wpmc63271.2024.10863138.

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Bhairannawar, Satish, Kalmeshwar Hosur, Shubham P. Patil, and Siddharth Bilagi. "Early Detection and Classification of Multi Crop Leaf Diseases using Custom Based CNN Model." In 2024 IEEE Conference on Engineering Informatics (ICEI). IEEE, 2024. https://doi.org/10.1109/icei64305.2024.10912289.

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Gupta, Vivek, Jhankar MoolChandani, Ankit Saxena, and Savita Kolhe. "A Review of Soybean Crop Classification and Disease Identification Using AI Learning and IoT-Based Model." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10726238.

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Madhumitaa, P. S., Chagamreddy Ragavi, Chinthamani Kiranmayi, Venkatesan M, and P. Prabhavathy. "Drought and Salinity Stress Classification in Soyabean Crops: Comparative Analysis of Machine Learning Models." In 2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT). IEEE, 2024. http://dx.doi.org/10.1109/iconscept61884.2024.10627854.

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Prasad Ch, Venkata Siva, Malarvizhi C, Porkodi M, Vaikash K G, N. Sunheriya, and Mohammad R. Al-Mousa. "A Deep Learning Assisted Crop Pest Classification Model by Using Enhanced Light Gradient Boost Machine (ELGBM) Logics." In 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63760.2024.10910912.

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Reports on the topic "Crop Classification Models"

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Bonfil, David J., Daniel S. Long, and Yafit Cohen. Remote Sensing of Crop Physiological Parameters for Improved Nitrogen Management in Semi-Arid Wheat Production Systems. United States Department of Agriculture, 2008. http://dx.doi.org/10.32747/2008.7696531.bard.

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To reduce financial risk and N losses to the environment, fertilization methods are needed that improve NUE and increase the quality of wheat. In the literature, ample attention is given to grid-based and zone-based soil testing to determine the soil N available early in the growing season. Plus, information is available on in-season N topdressing applications as a means of improving GPC. However, the vast majority of research has focused on wheat that is grown under N limiting conditions in sub-humid regions and irrigated fields. Less attention has been given to wheat in dryland that is water
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Miles, Gaines E., Yael Edan, F. Tom Turpin, et al. Expert Sensor for Site Specification Application of Agricultural Chemicals. United States Department of Agriculture, 1995. http://dx.doi.org/10.32747/1995.7570567.bard.

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In this work multispectral reflectance images are used in conjunction with a neural network classifier for the purpose of detecting and classifying weeds under real field conditions. Multispectral reflectance images which contained different combinations of weeds and crops were taken under actual field conditions. This multispectral reflectance information was used to develop algorithms that could segment the plants from the background as well as classify them into weeds or crops. In order to segment the plants from the background the multispectrial reflectance of plants and background were st
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