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Journal articles on the topic 'Leaf disease detection'
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Shendye, Yogeshwar. "Cassava Plant Leaf Disease Detection." International Journal of Science and Research (IJSR) 10, no. 7 (2021): 907–10. https://doi.org/10.21275/sr21716223603.
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Pore, Prof Yogita, Suraj Teli, Swaraj Ghuge, and Nikhil Patil. "Leaf Disease Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 1767–70. http://dx.doi.org/10.22214/ijraset.2023.51405.
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Abstract: Early disease identification is crucial for productive crop production in agriculture. illnesses such as bacterial spot, late blight, Septoria leaf spot, and yellow curved leaf the quality of the tomato harvest. Automatic classification techniques of plant diseases also assist in taking action once they are discovered diseased leaf symptoms Presented below is a Convolutional Learning Vector Quantization and Neural Network (CNN) model Method for detecting tomato leaf disease based on the (LVQ) algorithm and categorization. There are 500 tomato photos in the dataset. leaves that display four disease symptoms. We created a model of CNN for feature extraction and categorization automatically. Color Research on plant leaf diseases actively uses information. In our model, three channels based on RGB are subjected to filters.
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Akhtar, Afrin, and Mithlesh Kumar. "Plant Leaf Disease Detection: Review Report." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 450–52. https://doi.org/10.21275/sr25403132253.
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Mohan, Dr K. Madan. "LEAF DISEASE PREDICTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–6. http://dx.doi.org/10.55041/ijsrem27703.
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Leaf diseases are a major problem in agriculture, causing significant losses in crop yield and quality. Early detection of leaf diseases is essential for effective management, but it can be difficult and time- consuming to do manually. In recent years, there has been growing interest in the use of machine learning and computer vision techniques for leaf disease prediction. These techniques can be used to automatically extract features from leaf images that are indicative of disease, and then use these features to train a classifier that can distinguish between healthy and diseased leaves. Several studies have shown that machine learning-based methods can achieve high accuracy in leaf disease prediction. For example, one study reported an accuracy of 98% for detecting 10 different types of leaf diseases in tomato plants. The development of accurate and reliable leaf disease prediction methods has the potential to revolutionize the way that plant diseases are managed. By enabling early detection of diseases, these methods can help to reduce crop losses and improve crop yields Keywords CNN, Image processing, Convolution operations, Fully connected layer, Machine learning, Computer vision
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Gupta, Sahil, Vivek Pandey, Pravesh Pandey, Mukul Verma, and Hasib Shaikh. "Leaf Disease Detection System." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 1603–12. http://dx.doi.org/10.22214/ijraset.2023.50439.
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Abstract: Agricultural productivity is something on which economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. For instance a disease named little leaf disease is a hazardous disease found in pine trees in United States. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself it detects the symptoms of diseases i.e. when they appear on plant leaves. This paper introduces an efficient approach to identify healthy and diseased or an infected leaf using image processing and machine learning techniques. Various diseases damage the chlorophyll of leaves and affect with brown or black marks on the leaf area. These can be detected using image prepossessing, image segmentation. Support Vector Machine (SVM) is one of the machine learning algorithms is used for classification. The Convolutional Neural Network (CNN) resulted in a improved accuracy of recognition compared to the SVM approach.
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M, Megha. "Tomato Leaf Disease Detection and Monitoring System." International Journal of Science and Research (IJSR) 11, no. 7 (2022): 1746–49. http://dx.doi.org/10.21275/sr22719075340.
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Tharkar, Rushikesh. "PLANT LEAF DISEASE DETECTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31382.
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Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly Keywords: Plant leaf disease detection, leaf disease detection, convolutional neural network, deep learning
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Kamble, Ms D. R., Ms Snehal Krishna Gadale, Ms Dhanashri Nitin Pawar, Ms Gauri Dhanaji Shedage, and Ms Mukti Mahajan. "Leaf Disease Detection System." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 160–64. http://dx.doi.org/10.22214/ijraset.2024.58699.
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Abstract: The agricultural sector plays a crucial role in sustaining the world's growing population. However, the prevalence of plant leaf diseases can significantly impact crop yields and quality. This project leverages the power of deep learning techniques to develop an automated system for the early detection of plant leaf diseases. By utilizing a large dataset of annotated leaf images and state-of-the-art convolutional neural networks (CNNs), this research aims to accurately identify and classify various plant leaf diseases, including but not limited to fungal, bacterial, and viral infections. In this project, we propose an innovative approach to tackle this issue. Specifically, we utilize convolutional neural networks (CNNs) to analyze images of plant leaves and classify them into healthy or diseased categories. To ensure the robustness of our model, we curate an extensive dataset comprising images of various plant diseases and healthy leaves. This project stands to benefit the agriculture industry by offering a cost-effective, scalable, and timely solution for plant disease detection.
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Singh, Shivangi. "Leaf Disease Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 3324–29. http://dx.doi.org/10.22214/ijraset.2021.36836.
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Agriculture is a key source of livelihood. Agriculture provides employment opportunities for village people on a large scale in developing countries like India. India's agriculture consists of the many crops and consistent with survey nearly 70% population is depends on agriculture. Most of Indian farmers are adopting manual cultivation thanks to lagging of technical knowledge. Farmers are unaware of what quite crops that grows well on their land. When plants are suffering from heterogeneous diseases through their leaves which will effect on the production of agriculture and profitable loss, also reduction in both quality and quantity of agricultural production. Leaves are important for fast growing of plant and to extend production of crops. Identifying diseases in plant leaves is challenging for farmers and also for researchers. Currently farmers are spraying pesticides to the plants but it affects humans directly or indirectly by health or also economically. To detect these plant diseases many fast techniques got to be adopt. In this paper, we have done surveys on different leaf diseases and various advanced techniques to detect these diseases. As said by Mahatma Gandhi, "Agriculture is the backbone of the Indian Economy". Hence the detection of leaf diseases is an important aspect in increasing the yield of a crop. By detecting the leaf disease farmer can increase the crop yield which leads in growth of country’s economy.
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Monigari, Vaishnavi. "Plant Leaf Disease Prediction." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1295–305. http://dx.doi.org/10.22214/ijraset.2021.36582.
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The Indian economy relies heavily on agriculture productivity. A lot is at stake when a plant is struck with a disease that causes a significant loss in production, economic losses, and a reduction in the quality and quantity of agricultural products. It is crucial to identify plant diseases in order to prevent the loss of agricultural yield and quantity. Currently, more and more attention has been paid to plant diseases detection in monitoring the large acres of crops. Monitoring the health of the plants and detecting diseases is crucial for sustainable agriculture. Plant diseases are challenging to monitor manually as it requires a great deal of work, expertise on plant diseases, and excessive processing time. Hence, this can be achieved by utilizing image processing techniques for plant disease detection. These techniques include image acquisition, image filtering, segmentation, feature extraction, and classification. Convolutional Neural Network’s(CNN) are the state of the art in image recognition and have the ability to give prompt and definitive diagnoses. We trained a deep convolutional neural network using 20639 images on 15 folders of diseased and healthy plant leaves. This project aims to develop an optimal and more accurate method for detecting diseases of plants by analysing leaf images.
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Jasani, Abhishek, Mehul Dholi, and Soham Purkar. "Tomato Leaf Disease Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 918–22. http://dx.doi.org/10.22214/ijraset.2022.41918.
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Abstract: Tomato is an important crop in India and affects India’s economy in many ways. It is observed that the development in agriculture is sluggish nowadays due to the attack of diseases. Many farmers detect diseases by their previous experience or some take help from experts. Traditional ways are often used to detect the diseases by the farmers. So, there is the possibility of an inaccurate diagnosis of diseases having very large similarity in their symptoms. So, it is essential to move towards the new strategies for automatic diagnosis and controlling of disease. So, there is a need for an automatic, accurate and less expensive machine vision system for detection of disease from tomato leaf images.
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Vinitha Mallikarjuna Nandi, M. "Plant Leaf Disease Detection Using Convolutional Neural Network." International Journal of Science and Research (IJSR) 13, no. 4 (2024): 1545–48. http://dx.doi.org/10.21275/sr24422151104.
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Saji, Alby. "Green Leaf Disease Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 5360–64. http://dx.doi.org/10.22214/ijraset.2023.52825.
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Abstract: Because it feeds humanity, creates jobs, and directly supports national economic progress, agriculture is the backbone of the country. Identification of plant diseases is very crucial in agriculture. The increasing use of pesticides and sprays nowadays has led to a wide range of diseases affecting plants. Early disease detection would help farmers save more harvests if the infections could be stopped. Plants can be saved if rotting spots are discovered early. Automatic plant disease detection not only saves time but also provides greater accuracy. Plant production is decreased by improper disease detection. Here, we use image processing techniques to identify a few common plant illnesses. First, we take the image of the plant anduse image processing to identify it. This project is being implemented using Python.
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Kalyankar, Dr Pratima A. "Leaf Scanning Disease Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29874.
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Crop diseases pose a significant threat to global food security, with rapid identification challenges in regions lacking essential infrastructure. The confluence of rising smartphone adoption worldwide and recent strides in computer vision through deep learning has opened avenues for smartphone-enabled disease diagnosis. Leveraging a public dataset containing 2,500 images of plant leaves in varied health conditions, acquired under controlled settings, we employed a deep convolutional neural network. This model successfully discerns crop species and identifies diseases or their absence, achieving an impressive 86.35% accuracy on a withheld test set. This underscores the viability of the proposed methodology. In essence, the strategy of training deep learning models on expansive and accessible image datasets signals a promising route for widespread smartphone-assisted crop disease diagnosis on a global scale. Keywords - crop diseases, machine learning, deep learning, convolutional neural network (CNN)
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Roopa, Ms, and Ayush C. "Tomato Leaf Disease Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem.spejss003.
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Tomato plants are particularly vulnerable to leaf diseases, which can take a serious toll on crop yield and quality if not caught early. Traditionally, farmers and agricultural experts rely on manual inspection to spot these diseases a method that can be both slow and prone to mistakes. To streamline this process, our project introduces an automated system that uses machine learning and image processing to detect tomato leaf diseases more accurately and efficiently. We worked with a dataset of tomato leaf images that includes both healthy leaves and those affected by diseases like Early Blight, Late Blight, Leaf Mold, and Target Spot. Using this data, we trained a Convolutional Neural Network (CNN) to classify the images with high accuracy. To improve the model’s learning, we applied standard preprocessing steps such as image augmentation, resizing, and normalization. Our experiments show that the system performs well, offering reliable early detection of various tomato leaf diseases. By providing timely insights, this tool can help farmers take preventive action sooner, potentially reducing crop loss and improving productivity. In the future, we hope to expand this work into mobile platforms to make real- time disease detection more accessible in the field. Key Words: Tomato leaf diseases, disease detection, deep learning, CNN, image processing, Late blight, Leaf mold.
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Md, Abu Bakar Laskar, Jinzhi Zhou, Mehedi Hasan Md, and Tanvin Ashan Md. "Plant Leaf Disease Detection Using Deep Learning." LC International Journal of STEM 5, no. 1 (2024): 59–79. https://doi.org/10.5281/zenodo.11173886.
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Plant leaf diseases pose a danger to food security, and their rapid identification is made more difficult in many areas by a lack of infrastructure. This thesis is a concentrated attempt to address this important problem by utilizing state-of-the-art deep learning techniques, with a focus on the YOLOv5 model, to offer a dependable and effective solution for plant leaf disease detection in agriculture. The introduction emphasizes the serious effects that plant diseases have on a global and financial level, underscoring the critical necessity for early detection to lessen these effects. Driven by the promise of technology to revolutionize agriculture, this work carefully investigates the complex use of deep learning techniques. YOLOv5 is trained to demonstrate its ability to distinguish between healthy and diseased plant leaves using a carefully chosen tomato dataset. The dataset contains nine different types of illnesses. The model achieves an impressive 92.6 percent average precision, indicating a high degree of disease detection accuracy. Plant leaf disease detection in agriculture faces many complicated obstacles, and the successful deployment of the trained model through the Flask framework represents a significant leap in the practical application of deep learning to address these issues. Our multimodal approach places our research at the forefront of efforts to improve agricultural technology and guarantee global food security while also making a significant contribution to the scientific understanding of disease identification and laying the foundation for future advances.
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Prethika, D. Iruthaya Antony, and V. Revathy. "Plant Leaf Disease Detection using CNN." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28892.
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Abstract— Plants and crops that are infected by pests have an impact on the country's agricultural production. Usually, farmers or professionals keep a close eye on the plants in order to discover and identify diseases. However, this procedure is frequently time- consuming costly, andimprecise. Plant disease detection can be done by looking for a spot on the diseased plant's leaves. The goal of this paper is to create a Disease Recognition Model that is supported by leaf image classification. To detect plant diseases, we are utilizing image processing with a Convolution neural network (CNN). A convolutional neural network(CNN) is a form of artificial neural network that is specifically intended to process pixel input and is used in image recognition.Farmers do not expertise in leaf disease so they produce less production. Plant leaf diseases detection is the important because profit and loss are depends on production. Index Terms: Image processing,Crops,Support vector Machine,Plant disease,Classification. Keywords— Heart Disease, Diabetes, Machine Learning.
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B G, Jagadeesha, Ramesh Hegde, and Ajith Padyana. "Black pepper leaf disease detection using deep learning." International Journal of Innovative Research and Scientific Studies 8, no. 2 (2025): 897–907. https://doi.org/10.53894/ijirss.v8i2.5389.
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Advances in deep learning techniques have achieved spectacular success in the detection of plant diseases. A new method for detecting black pepper leaf disease using deep learning was proposed. In the proposed scheme, the SqueezeNet model is used, which is a Convolutional Neural Network (CNN), where the CNN is a subset of deep learning networks. The disease detection is based on the visual characteristics of the black pepper leaves. Thus, the proposed method is an image classification scheme using a trained SqueezeNet that detects whether the pepper leaves are healthy or diseased. The detection accuracy is found to be more than 99%. The early detection of defects, such as deformation and discoloration of pepper leaves, forewarns the onset of diseases, and the cultivator of pepper wines can undertake appropriate countermeasures.
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M. R., Dr Sanghavi. "Tomato Leaf Disease Detection System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33787.
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Tomato is a widely cultivated crop with significant economic importance in the agro based industry. However, tomato plants are susceptible to various diseases that can severely impact yield and quality. Early and accurate detection of these diseases is crucial for effective disease management and ensuring optimal production. In this study, we propose a novel approach that a convolutional Neural Network (CNN) for the automated detection of tomato leaf diseases. First, Convolutions is employed to reduce the dimensionality of the input data, extracting the most relevant features for disease detection. The CNN leverages its ability to learn complex patterns and features from the data, enabling accurate classification of various tomato leaf diseases. To evaluate the effectiveness of our approach, we conducted experiments using a diverse dataset of tomato leaf images with different disease manifestations. Key Words: Agriculture, Tomato diseases, Leaf disease detection, Deep learning, neural network, Disease Diagnosis.
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Somaiya, Kush Vijay. "PLANT LEAF DISEASE DETECTION." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem03128.
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ABSTRACT- Agriculture remains a fundamental pillar of many national economies, making the protection of crops from disease a top priority. Pathogens such as bacteria, fungi, and viruses can significantly reduce crop productivity, underscoring the need for timely and accurate disease detection. Recent innovations in computer vision and artificial intelligence have introduced powerful tools for recognizing plant diseases through image analysis, particularly using leaf imagery. This paper investigates the application of machine learning, deep learning, and few-shot learning models in automating disease identification to assist farmers in making informed, prompt decisions. By examining the use of advanced models—including convolutional networks and vision transformers—alongside imaging technologies like hyperspectral cameras, this study highlights both the technological advancements and their potential impact in the field. Furthermore, it touches on molecular-level diagnostic techniques aimed at minimizing the threat of pathogens. The review offers a thorough overview of current progress and identifies key opportunities for future research, with the goal of translating laboratory breakthroughs into practical solutions for sustainable agriculture. INDEX TERMS: Plant disease, deep learning, machine learning, shot learning, computer vision, folding networks (CNNS), vision trans, hyperspectral imaging, molecular diagnostics, sustainable agriculture detection.
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Pasalkar, Jayashree, Ganesh Gorde, Chaitanya More, Sanket Memane, and Vaishnavi Gaikwad. "Potato Leaf Disease Detection Using Machine Learning." Current Agriculture Research Journal 11, no. 3 (2024): 949–54. http://dx.doi.org/10.12944/carj.11.3.23.
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Potato is one of the most important crops worldwide, and its productivity can be affected by various diseases, including leaf diseases. Early detection and accurate diagnosis of leaf diseases can help prevent their spread and minimize crop losses. In recent years, Convolutional Neural Networks (CNNs) have shown great potential in image classification tasks, including disease detection in plants. In this study, we propose a CNN-based approach for the prediction of potato leaf diseases. The proposed method uses a pre-trained CNN model, which is fine-tuned on a dataset of potato leaf images. The dataset includes images of healthy leaves and leaves infected with different diseases such as early blight and late blight. The trained model is then used to classify new images of potato leaves into healthy or diseased categories. The proposed approach achieves 97.4% accuracy in the classification of potato leaf diseases such as early blight potato leaf disease and late blight potato leaf disease, and can be used as an effective tool for early detection and management of these diseases in potato crops.
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Bhujbal, Sahil, Pradnya Mandale, Vaishnavi Aher, and Rushikesh Wable. "Soybean Leaf Disease Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 1128–32. http://dx.doi.org/10.22214/ijraset.2023.47611.
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Abstract: With the continuous integration of computer technology into agricultural production, it also reduces personnel costs while improving agricultural production efficiency and quality. Crop disease control is an important part of agricultural production, and the use of computer vision technology to quickly and accurately identify crop diseases is an important means of ensuring a good harvest of agricultural products and promoting agricultural modernization. In this paper, a recognition method based on deep learning is proposed based on soybean brown spot. The method is divided into image pre- treatment and disease identification. Based on traditional threshold segmentation, the pre-processing process first uses the HSI colour space to filter the information of the normal area of the leaf, adopts OTSU to set the threshold to segment the original image under the Lab colour space, and then merges the segmented images. The final spot segmentation image is obtained. Compared with the renderings of several other commonly used methods of segmentation, this method can better separate the lesions from the leaves. In terms of disease identification, in order to adapt to the working conditions of large samples of farmland operations, a convolutional neural network (CNN) of continuous convolutional layers was constructed with the help of Caffe to extract more advanced features of the image. In the selection of activation functions, this paper selects the Max out unit with stronger fitting ability, and in order to reduce the parameters in the network and prevent the network from overfitting, the sparse Max out unit is used, which effectively improves the performance of the Max out convolutional neural network. The experimental results show that the algorithm is superior to the algorithm based on ordinary convolutional neural network in identifying large sample crop diseases.
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Oo, Yin Min, and Nay Chi Htun. "Plant Leaf Disease Detection and Classification using Image Processing." International Journal of Research and Engineering 5, no. 9 (2018): 516–23. http://dx.doi.org/10.21276/ijre.2018.5.9.4.
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Singh, Manoj Kumar, Ali Sher Khan, Abbas Akbar, Ananya Lamba, and Prakriti Gupta. "Plant Scan: Advanced CNN Model for Leaf Disease Detection." International Journal of Research Publication and Reviews 6, sp5 (2025): 338–45. https://doi.org/10.55248/gengpi.6.sp525.1948.
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Singh, Shanukumar, Zek Furtado, and Apurv Patil. "AI-Driven Plant Disease Detection: Leveraging Deep Learning for Accurate Plant Disease Detection from Leaf Images." International Journal of Science and Research (IJSR) 13, no. 8 (2024): 893–900. http://dx.doi.org/10.21275/sr24812105523.
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Shila Ananda Shinde. "Deep Learning Based Leaf Disease Detection." Journal of Information Systems Engineering and Management 10, no. 37s (2025): 1100–1107. https://doi.org/10.52783/jisem.v10i37s.6761.
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The rising prevalence of plant diseases poses a significant challenge to agricultural productivity and food security. This project introduces a deep learning-based solution for leaf disease detection, utilizing Convolutional Neural Networks (CNN) and transfer learning techniques to improve diagnostic accuracy. By leveraging pre-trained models, we reduce the reliance on large training datasets while maintaining high classification performance. A comprehensive dataset of both healthy and diseased leaf images is collected, with advanced image preprocessing and augmentation methods employed to enhance the model's robustness. Our experimental findings demonstrate that the proposed method effectively identifies a range of leaf diseases, showing substantial improvements in accuracy and efficiency over traditional diagnostic approaches. The use of transfer learning not only accelerates the training process but also boosts the model's ability to generalize across various plant species and disease conditions. This research underscores the importance of deep learning in precision agriculture, providing an innovative tool for early disease detection that enables farmers to take proactive action, thus reducing crop losses and promoting sustainable farming practices.
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Mahender, Mr. "Plant Leaf Disease Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 663–66. http://dx.doi.org/10.22214/ijraset.2024.59839.
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Abstract: The research presents an automated vision a system that makes use of the processing of images techniques to identify plant diseases in agricultural contexts. In order to monitor vast crop fields and automatically identify disease symptoms as soon that they occur on plant leaves, research on automated identification of plant infections is crucial to the agricultural industry. This method uses segmentation, colour modifications, and masking of green pixels to classify data based on learning from some training examples of that category. The simulated outcome, in the end, demonstrates that the network classifier in use offers reduced training error and increased classification accuracy
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Nagaveni, B. Nimbal, S. Namratha, K. Arshitha, and Shetty S. Chaithra. "Mulberry Leaf Disease Detection." International Journal of Innovative Science and Research Technology 8, no. 4 (2023): 1725–31. https://doi.org/10.5281/zenodo.7912030.
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Sericulture is an important domestic industry. In India,it is one of the eco-friendly industries. India is the only country where all five recognized commercial silks are made, namely mulberry, tropical tasar, oak tasar, eri and muga. Sericulture is labor intensive, providing jobs in India for over 8 million people, and serving Indians as a tremendous source of revenue. Silkworm is sericultural foundation. Commercial silk is developed through the production of different types of silkworms, of which BOMBYX MORI, originally from Asia, is the most widely and economically used, Mulberry is significant sole nourishment for mulberry silkworm, which exclusively benefits from the leaves of mulberry plant. These silkworms are totally domesticated and reared indoors. Other uses of mulberry leaves are seen in the fields of health and skin care. These mulberry plants include a high pace of yield disappointment and are over the top expensive for creation, so should be dealt quite well.Our goal is to overcome these problems using a farmerfriendly system where the result involves cure of the disease and the fertilizer or pesticide proportion to be used are displayed on the user interface.
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Nagaveni, B. Nimbal, S. Namratha, K. Arshitha, Shetty S. Chaithra, and KB Bhuvanashri. "Mulberry Leaf Disease Detection." International Journal of Innovative Science and Research Technology 7, no. 12 (2022): 704–7. https://doi.org/10.5281/zenodo.7495752.
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Sericulture is an important domestic industry. In India, it is one of the eco-friendly industries. India is the only country where all five recognized commercial silks are made, namely mulberry, tropical tasar, oak tasar, eri and muga. Sericulture is labor intensive, providing jobs in India for over 8 million people, and serving Indians as a tremendous source of revenue. Silkworm is sericultural foundation. Commercial silk is developed through the production of different types of silkworms, of which BOMBYX MORI, originally from Asia, is the most widely and economically used, Mulberry is significant sole nourishment for mulberry silkworm. It is also used for medicinal benefits like, treating diabetes. These mulberry plants include a high danger of yield disappointment and are over the highest expensive for production, so should be addressed well indeed. We will present an overview on various types of mulberry leaf diseases and different classification techniques in machine learning that are used for identifying diseases in different leaves and how to mange these diseases.
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Saxena, Niharika, and Neha Sharma. "TOMATO LEAF DISEASE PREDICTION USING TRANSFER LEARNING." International Journal of Engineering Technologies and Management Research 9, no. 6 (2022): 1–14. http://dx.doi.org/10.29121/ijetmr.v9.i6.2022.1177.
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Tomatoes are the most extensively planted vegetable crop in India's agricultural lands. Although the tropical environment is favorable for its growth, specific climatic conditions and other variables influence tomato plant growth. In addition to these environmental circumstances and natural disasters, plant disease is a severe agricultural production issue that results in economic loss. Therefore, early illness detection can provide better outcomes than current detection algorithms. As a result, deep learning approaches based on computer vision might be used to detect diseases early. This study thoroughly examines the disease categorization and detection strategies used to identify tomato leaf diseases. The pros and limitations of the approaches provided are also discussed in this study. Finally, employing hybrid deep-learning architecture, this research provides an early disease detection approach for detecting tomato leaf disease.
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Venkataramana, R. "LEAF DISEASE DETECTION AND REMEDY RECOMMENDATION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem28585.
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Agriculture impacts life and economic status of the people. Improper management of diseases results in annual loss of agricultural yield which will have serious effects on the quality, quantity and productivity if no proper care is taken. By using some automatic technique such as image processing, detection of leaf disease is quite significant and beneficial. The use of the most utilized deep learning classification mechanism, Convolutional Neural Network, helps in this regard. This paper proposes an innovative machine learning approach for automated leaf disease detection. By utilizing image processing and deep learning algorithms, the system analyzes leaf images taken with digital cameras or smart phones. Through training a convolutional neural network (CNN) on a comprehensive dataset containing healthy and diseased leaves, the system becomes adept at distinguishing between various disease types [1] . Leveraging tagged images of healthy and diseased leaves, our system showcases robustness and high accuracy. The automated image processing, particularly involving deep convolutional networks, ensures rapid and accurate results. The system's effectiveness will be gauged through extensive experimentation, comparing its performance against existing methods. Ultimately, this project contributes to the progress of precision agriculture and sustainable crop management practices. Key Words: Convolutional neural network (CNN), Pre- Processing, Deep Learning, Image Processing, Classification, Remedy Recommendation .
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Baig, Muhamamd Daniyal, Hafiz Burhan Ul Haq, Muhammad Asif, and Aqdas Tanvir. "Leaf diseases detection empowered with transfer learning model." Computer and Telecommunication Engineering 2, no. 3 (2024): 2358. http://dx.doi.org/10.54517/cte2358.
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<p>The detection of leaf diseases using modern technology has significant importance in agriculture and artificial intelligence. Deep learning, specifically, plays a crucial role in this field, as it enables accurate and efficient disease classification. Early detection of leaf diseases is vital to implementing timely treatments and preventing widespread damage to leaves. Leaf diseases can be caused by various factors, including bacteria, fungi, viruses, and other pathogens. Among them, bacteria and viruses are the most invasive and can lead to substantial yield losses if not identified and treated promptly. Bacterial and viral infections are common in agricultural settings, affecting leaves of all types and ages. Our research aims to propose a transfer learning-based model for predicting leaf diseases using a dataset of leaf images. The images will be classified into healthy or diseased leaves based on extracted features. The proposed model, named Leaf Disease Transfer Learning Algorithm (LDTLA), demonstrates promising results with an average accuracy of 97.37% on the dataset. Utilizing convolutional neural networks (CNN) and deep learning techniques, our LDTLA model outperforms previous quantitative and qualitative research studies in leaf disease detection. This advanced approach to leaf disease identification holds the potential to revolutionize agriculture by enabling farmers to make informed decisions, implement targeted treatments, and minimize leaf losses caused by diseases.<strong></strong></p>
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M Suleman Basha. "Paddy Leaf Disease Detection using Deep Learning." Power System Technology 49, no. 1 (2025): 960–70. https://doi.org/10.52783/pst.1644.
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Paddy leaf disease detection using deep learning refers to training the neural networks on images of paddy leaves to precisely identify diseases. It supports early detection, wrapped in an overall reduction of crop loss, providing windfall to agricultural productivity. The consequences of plant diseases are a significant limitation to agricultural productivity, and monitoring manually is usually cumbersome, unreliable, and time-consuming. The model ORB-DL is used to extract the key features for identifying plant diseases. Combined with advanced DL models, MobileNetV2, ResNet50 these features increase the accuracy and robustness of disease detection. Thermal Imaging is capable of detecting small alterations even before visible symptoms manifest and allows for event-driven management. Grad-CAM visualization techniques provide interpretability results that afford insight into model predictions and build up confidence in automated solutions. Our experiment will show that the combination of ORB-DL with these DL architectures outperforms existing methods while still providing superior accuracy and reliability. The objectives of this study are to employ some of the means of Artificial Intelligence, Deep Learning (DL), and Thermal Imaging in early disease detection and mitigation of shortcomings.
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Patil, Pragati, Priyanka Jadhav, Nandini Chaudhari, Nitesh Sureja, and Umesh Pawar. "Deep learning for grape leaf disease detection." International Journal of Informatics and Communication Technology (IJ-ICT) 14, no. 2 (2025): 653. https://doi.org/10.11591/ijict.v14i2.pp653-662.
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Agriculture is crucial to India's economy. Agriculture supports almost 75% of the world's population and much of its gross domestic product (GDP). Climate and environmental changes pose a threat to agriculture. India is recognized for its grapes, a commercially important fruit. Diseases reduce grape yields by 10-30%. If not recognized and treated early, grape diseases can cost farmers a lot. The main grape diseases include downy and powdery mildew, leaf blight, esca, and black rot. This work creates an Android grape disease detection app which uses machine learning. When a farmer submits a snapshot of a diseased grape leaf, the smartphone app identifies the ailment and offers grape plant disease prevention tips. In this research, an android app that detects grape plant illnesses use convolutional neural network (CNN) and AlexNet machine learning architectures. We investigated and compared CNN and AlexNet architecture's efficacy for grape disease detection using accuracy and other metrics. The dataset used comes from Kaggle. CNN and AlexNet architectures yielded 98.04% and 99.03% accuracy. AlexNet was more accurate than CNN in the final result.
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Rafay, Syed Abdul. "Leaf Disease Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 2086–89. http://dx.doi.org/10.22214/ijraset.2022.45667.
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Abstract: Diseases in plants cause major production and economic losses as well as reduction in both quality and quantity of agricultural products. Now a days, plant diseases detection has received increasing attention in monitoring large field of crops. Farmers experience great difficulties in switching from one disease control policy to another. The naked eye observation of experts is the traditional approach adopted in practice for detection and identification of plant diseases. In this project, we study the need of simple plant leaves disease detection system that would facilitate advancements in agriculture. Early information on crop health and disease detection can facilitate the control of diseases through proper management strategies. This technique will improves productivity of crops.
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Anitha, R., and A. Bazila Banu. "Leaf Disease Detection using Deep Learning." Journal of Artificial Intelligence and Capsule Networks 4, no. 2 (2022): 99–110. http://dx.doi.org/10.36548/jaicn.2022.2.002.
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Agriculture plays an important role in determining India's economy. So, the detection of disease that affects the plants is most important as it affects productivity. The proposed system is designed to detect the diseases that degrade the health of the leaves. The diseases may be of bacterial, viral and late blight. The diseases can be detected with the help of Convolutional Neural Network (CNN). It is composed of several layers that help in the prediction of diseases. The designed CNN classifies the disease into three major categories. An input leaf image is provided to test whether the leaf is healthy or not. The system has been trained with different input leaves. Once it is trained the new input leaves are given to the classifier, then the classifier identifies the label of the affected leaves. Based on the disease identified, the necessary remedies can be taken for curing the disease.
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Pasha.B, Asif. "Grape Leaf Disease Prediction and Management System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48188.
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I. ABSTRACT The grapevine industry faces numerous challenges, including the prevalence of diseases that can severely impact crop yield and quality. Timely identification of grapevine diseases is critical for effective management and prevention. Traditional methods of disease detection, relying on visual inspection by experts, are often time-consuming, inconsistent, and prone to human error. To address this challenge, we propose a state- of-the-art Grape Disease Detection System using YOLOv8 (You Only Look Once), an advanced deep learning-based object detection algorithm, for real-time identification and classification of grapevine diseases. The system leverages computer vision techniques to detect various grapevine diseases from images of grape leaves. It uses a dataset of labelled grapevine leaf images, collected under various grapevine leaves affected by diseases such as ESCA, Leaf Blight, and other common fungal infections. The dataset undergoes preprocessing steps such as resizing, normalization, and augmentation to ensure robustness and generalization of the model. The model's architecture enables it to detect and classify diseases in images captured by cameras or smartphones with high accuracy and speed. Once trained, YOLOv8 processes new images, detects diseased regions, and classifies them into disease categories based on learned features. The system provides visual feedback by drawing bounding boxes around diseased areas and labels them with the disease type and the model's confidence score. It also generates real-time alerts and recommendations for treatment based on the detected disease. The system's workflow begins with the acquisition of images, followed by preprocessing, disease detection, classification, and results display. It can be deployed as a standalone mobile application or integrated with existing vineyard monitoring systems.
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P.K, Rajani, Vaidehi Deshmukh, Sheetal U. Bhandari, Roshani Raut, and Reena Kharat. "Rice Leaf Disease Detection Using Convolutional Neural Network." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10s (2023): 512–17. http://dx.doi.org/10.17762/ijritcc.v11i10s.7687.
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In the agriculture sector, the rice crops getting diseased has become a significant concern recently, especially in India, where rice is one of the primary meals. Precise and early-stage detection of various diseases observed in the rice crops can help farmers to provide proper treatment to the crops. This paper presents a Convolutional Neural Network (CNN) based approach is used to detect rice plant leaf disease. CNN is one of the deep learning algorithms that help in image processing and classification with significant accuracy. The proposed algorithm is used for an image dataset of the diseased rice plant leaves, available on Kaggle. Two types of rice leaf diseases are considered for the analysis: brown spot and bacterial leaf blight. The images of these two diseases were pre-processed, segmented, and classified to identify the caused disease. The proposed model can also be used for the detection of the diseases present in other types of crops, faces recognition system, classifying animals, and car models. The overall accuracy of the developed model is nearly 67%.
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Tagare, Mohammad M., Urmila R. Pol, Parashuram S. Vadar, and Tejashree T. Moharekar. "Detection of Jackfruit Leaf Disease Using Machine Learning and Deep Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1871–76. https://doi.org/10.22214/ijraset.2025.68592.
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Abstract: The detection of diseases in agricultural crops plays a critical role in maintaining healthy yields. Jackfruit (Artocarpus heterophyllus), a tropical fruit, is susceptible to various diseases that impact its leaves. Timely disease detection can significantly reduce crop loss and improve the quality of the harvest. This paper proposes a system for detecting jackfruit leaf diseases using machine learning (ML) and deep learning (DL) techniques. A dataset of healthy and diseased jackfruit leaf images is used to train both traditional ML algorithms (Random Forest) and DL models (Convolutional Neural Networks). The results indicate that deep learning models, particularly CNNs, outperform traditional ML models in terms of classification accuracy, precision, and recall. This system serves as an effective tool for early detection and management of jackfruit leaf diseases, offering an automated solution for farmers.
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Abqa, Javed Salheen Bakhet Taliah Tajammal Iqra Javed. "Plant Leaf Disease Detection Using CNN." Dialogue Social Science Review (DSSR) 3, no. 2 (2025): 678–94. https://doi.org/10.5281/zenodo.15185006.
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Agriculture is the major area in several regions, including Pakistan and India, where roughly 55% to 60% of the inhabitant relies on it explicitly and implicitly. In agricultural countries, plant disease is a serious issue. Each year farmer faces loss due to the plant diseases and it is a difficult task to detect disease by a naked eye. The suggested approach intends to decrease agricultural losses. An automated plant identification and diagnosis is required for this. The proposed system helps to find and recognize disease at initial phase or at least diagnose the disease to avoid further degradation. AgriCure is a plant disease detector, an android application to detect disease in apple and tomatoes. This method overcomes the issues of cost, time, efficiency, restricted precision, and area for plant diagnosis because of traditional manual methods or naked eye inspections. The proposed system includes these modules i.e. disease detection, disease information, learn about diseases, add reminders and notes to remind tasks and show the weather forecast system. The system utilizes deep CNN method to diagnose and classify defect and show their descriptions. Such automated systems have been constructed in the past, but they generally have low accuracy or can’t manage a large range of plants. The system mainly focuses on the detection of diseases in apples into predefined categories like healthy apple leaf, apple scab, apple black rot, apple cedar rust, and in tomatoes predefined categories like healthy tomato leaf, bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, tomato spider mites, tomato target spot, tomato yellow leaf curl virus, tomato mosaic virus. As a result, the suggested system is limited to some restrictions and difficulties. Team members of the project, on the other hand, would be working diligently to meet major milestones of the planned system. An automated and accurate plant disease detection system like AgriCure addresses a significant social problem by helping farmers reduce crop losses, ensuring food security, and promoting sustainable agriculture.
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Ibrahim, Shafaf, Nur Afiqah Mohd Fuad, Nor Azura Md Ghani, Raihah Aminuddin, and Budi Sunarko. "Support Vector Machine (SVM) for Tomato Leaf Disease Detection." AGRIVITA Journal of Agricultural Science 47, no. 2 (2025): 338. https://doi.org/10.17503/agrivita.v47i2.3746.
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<p>Tomatoes rank among the top five most globally demanded crops and serve as a key ingredient in numerous dishes. However, productivity may decline due to challenges such as diseases, pest infestations, and climate change. Therefore, automatic disease detection is essential to identify early signs of illness during the growth period. This study proposes a method for detecting tomato leaf diseases using image processing techniques. The approach involves image enhancement, feature extraction, and classification. Initially, leaf disease images were enhanced using the Contrast Adjustment technique. Subsequently, color and texture features were extracted using Color Moments and the Gray-Level Co-occurrence Matrix (GLCM), respectively. Disease detection was carried out using a Support Vector Machine (SVM). The method was tested on 50 images each for healthy leaves and four types of tomato leaf diseases: Bacterial Spot, Yellow Leaf Curl Virus, Early Blight, and Late Blight. The performance of the disease detection system was evaluated using a confusion matrix, achieving an overall accuracy, sensitivity, and specificity of 96%, 90%, and 97.5%, respectively. These results demonstrate the effectiveness of the proposed SVM-based approach for tomato leaf disease detection.</p>
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Tummala, Sai Vivek Reddy. "Rice Leaf Disease Detection Using CNN." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 480–85. http://dx.doi.org/10.22214/ijraset.2023.54638.
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Abstract: This paper presents a deep learning project that addresses Rice Leaf Disease Detection using convolutional neural Networks (CNN). We develop a novel deep learning model using a computer vision by image processing which detect the diseases like leaf smut, Brown spot, Bacterial leaf blight and with ReLU activation function by using adam optimization algorithm which evaluate its performance on rice leaf disease which shows the confidence.
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., Shishira. "Plant Disease Detection Using Leaf Images." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (2021): 600–602. http://dx.doi.org/10.22214/ijraset.2021.37429.
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Identification of the plant diseases is that the key to prevent the losses within the yield and quantity of the agricultural product. The studies of the plant diseases mean the studies of visually observable patterns seen on the plant. Health monitoring and disease detection on plant is incredibly critical for sustainable agriculture. It’s very difficult to watch the plant diseases manually. It requires tremendous amount of labor, expertise within the plant diseases, and also require the excessive quantity. Hence, image processing is used for the detection of plant diseases by capturing the pictures of the leaves and comparing it with the data sets. The data sets comprise of different plant within the image format. Except detection users are directed to an e-commerce website where different pesticides with its rate and usage directions are displayed. This website is efficiently used for comparing the MRP’s of varied pesticides and buy the desired one for the detected disease. This paper aims to support and help the green house farmers in an efficient way.
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Sangeetha, P. "Leaf Disease Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 3995–99. http://dx.doi.org/10.22214/ijraset.2023.54349.
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Abstract: Agriculture is the most important sector among all, as it supplies food to people. As the food demand is increasing in our daily life and the population is increasing there is need of increasingin the food production also. The main issue nowadays is that diseases that are causing the food production to reduce. So our main aim is identification of the disease of the plants in early stage so that we can do the early diagnosis and protect crops from the harmful diseases. By doing this we can avoid the damage of crops and also can reduce the loses that farmers suffer from damage of crops. Here we are using image processing technique to identify the disease of the crops so that we can cure the diseases of the crops in early stage. Here we are using Deep learning algorithms like Convolutional neural networks (CNN) for our project that is leaf disease detection. CNN is the best algorithm for image processing as it greatly increases the performance of our model. Nowadays deep learning is the current trend as it solves the most complicated problems easily and can get the highest accuracy.
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KUNAL KASHINATH, MAHADIK. "Leaf Disease Detection and Classification." International Journal of Innovative Research in Science, Engineering and Technology 4, no. 11 (2015): 10507–11. http://dx.doi.org/10.15680/ijirset.2015.0411024.
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Nikith, B. V., N. K. S. Keerthan, M. S. Praneeth, and Dr T. Amrita. "Leaf Disease Detection and Classification." Procedia Computer Science 218 (2023): 291–300. http://dx.doi.org/10.1016/j.procs.2023.01.011.
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Shetty, Anilkumar. "Coffee Leaf Disease Detection Using CNN." International Journal for Research in Applied Science and Engineering Technology 12, no. 8 (2024): 439–44. http://dx.doi.org/10.22214/ijraset.2024.63940.
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Abstract: The project presents a comprehensive approach to detecting coffee leaf diseases through deep learning techniques, specifically utilizing Convolutional Neural Networks (CNNs). The system is built upon the VGG16 architecture, which has been fine-tuned to recognize and classify four types of coffee leaf conditions: Minor, Nodisease, Phoma, and Rust. A comprehensive preprocessing pipeline has been established to improve image quality, guaranteeing that the model is trained on well-optimized data. Data augmentation is used during training to avoid overfitting and enhance the model's ability to generalize to new data. The model is validated on a separate test set, where it achieves an accuracy of approximately 88%, indicating a high level of reliability in disease detection. Additionally, the project includes tools for evaluating the model's performance through metrics like accuracy, True Positive Rate(TPR), True Negative Rate(TNR), and False Positive Rate(FPR), providing a detailed analysis of its effectiveness. The final system is also equipped with a user-friendly interface for real-time disease detection, which can significantly benefit coffee farmers by enabling early intervention and reducing the impact of diseases on crop production. This project underscores the potential of CNNs in precision agriculture, paving the way for more intelligent and automated farming solutions
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Patil, Ninad, Sudarshan Mahendrakar, and Sarvesh Patil. "Leaf Disease Detection and Prevention Application." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 4232–37. http://dx.doi.org/10.22214/ijraset.2022.45971.
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Abstract: Agriculture is the backbone of an economy. Farmers play a vital role in strengthening the economy of any country. Due to the diseases a crop undergoes due to various reasons, Food security is compromised and affects the crop production. A crop disease not only affects a single crop but tends to spread to the neighboring crops as well. Detection and cure of such diseases becomes very difficult for a farmer. This problem can be fixed by using an application that uses a Machine Learning system, which takes leaf image as an input and detects the disease suffered by any particular crop and shows the possible cure. Also considering the farmer’s location, Crops that are suitable to grow in the particular location can be displayed.
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Xu, Kang, Yan Hou, Wenbin Sun, et al. "A Detection Method for Sweet Potato Leaf Spot Disease and Leaf-Eating Pests." Agriculture 15, no. 5 (2025): 503. https://doi.org/10.3390/agriculture15050503.
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Traditional sweet potato disease and pest detection methods have the limitations of low efficiency, poor accuracy and manual dependence, while deep learning-based target detection can achieve an efficient and accurate detection. This paper proposed an efficient sweet potato leaf disease and pest detection method SPLDPvB, as well as a low-complexity version SPLDPvT, to achieve accurate identification of sweet potato leaf spots and pests, such as hawk moth and wheat moth. First, a residual module containing three depthwise separable convolutional layers and a skip connection was proposed to effectively retain key feature information. Then, an efficient feature extraction module integrating the residual module and the attention mechanism was designed to significantly improve the feature extraction capability. Finally, in the model architecture, only the structure of the backbone network and the decoupling head combination was retained, and the traditional backbone network was replaced by an efficient feature extraction module, which greatly reduced the model complexity. The experimental results showed that the mAP0.5 and mAP0.5:0.95 of the proposed SPLDPvB model were 88.7% and 74.6%, respectively, and the number of parameters and the amount of calculation were 1.1 M and 7.7 G, respectively. Compared with YOLOv11S, mAP0.5 and mAP0.5:0.95 increased by 2.3% and 2.8%, respectively, and the number of parameters and the amount of calculation were reduced by 88.2% and 63.8%, respectively. The proposed model achieves higher detection accuracy with significantly reduced complexity, demonstrating excellent performance in detecting sweet potato leaf pests and diseases. This method realizes the automatic detection of sweet potato leaf pests and diseases and provides technical guidance for the accurate identification and spraying of pests and diseases.
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Dai, Dikang, Peiwen Xia, Zeyang Zhu, and Huilian Che. "MTDL-EPDCLD: A Multi-Task Deep-Learning-Based System for Enhanced Precision Detection and Diagnosis of Corn Leaf Diseases." Plants 12, no. 13 (2023): 2433. http://dx.doi.org/10.3390/plants12132433.
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Corn leaf diseases lead to significant losses in agricultural production, posing challenges to global food security. Accurate and timely detection and diagnosis are crucial for implementing effective control measures. In this research, a multi-task deep learning-based system for enhanced precision detection and diagnosis of corn leaf diseases (MTDL-EPDCLD) is proposed to enhance the detection and diagnosis of corn leaf diseases, along with the development of a mobile application utilizing the Qt framework, which is a cross-platform software development framework. The system comprises Task 1 for rapid and accurate health status identification (RAHSI) and Task 2 for fine-grained disease classification with attention (FDCA). A shallow CNN-4 model with a spatial attention mechanism is developed for Task 1, achieving 98.73% accuracy in identifying healthy and diseased corn leaves. For Task 2, a customized MobileNetV3Large-Attention model is designed. It achieves a val_accuracy of 94.44%, and improvements of 4–8% in precision, recall, and F1 score from other mainstream deep learning models. Moreover, the model attains an area under the curve (AUC) of 0.9993, exhibiting an enhancement of 0.002–0.007 compared to other mainstream models. The MTDL-EPDCLD system provides an accurate and efficient tool for corn leaf disease detection and diagnosis, supporting informed decisions on disease management, increased crop yields, and improved food security. This research offers a promising solution for detecting and diagnosing corn leaf diseases, and its continued development and implementation may substantially impact agricultural practices and outcomes.