This study builds a crop disease detection system and applies it to apple scab, tomato early blight, late blight, and leaf mold. The system first collects apple and tomato crop photos and crop disease data from the Plantvillage dataset on the Kaggle platform. ...
The real-time crop disease diagnosis is based on a convolutional neural network (CNN) that was trained, validated, and tested on a dataset of 87,860 leaf images split into 38 classes. To design an optimal CNN, 16 different CNNs were designed and tested. MobileNetV2 using the Canny Edge ...
Timely and precise detection is essential for effective disease management. The proposed model offers a solution to enhance detection accuracy while simplifying the process, utilizing a dataset of rice leaf images obtained from Kaggle.com. The dataset, though valuable, presented challenges due to image...
The second problem specific to plant disease detection is the lack of high-quality, labeled data. Solid data are the backbone of any machine learning classifier, and the availability of a large and varied training dataset would go a long way to solving the first challenge mentioned above. The...
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Early disease detection: Farmers can take preventative measures to stop the spread of illness and reduce crop loss by identifying early indicators of crop diseases with machine learning. Once the model is sufficiently trained, it can detect anomalies such as discoloration on growth size in the early...
For crop disease prediction, plant dataset efficiency improves the accuracy of the proposed model. Agriculture plays a vital role in the economic growth of a country with food source items using advanced farming techniques. Disease detection focuses on context-based filtering techniques used for ...
Meanwhile, considering the problem of harsh environmental disturbance in disease recognition in the field, two additional crop leaf disease datasets with complex backgrounds were prepared. The original images of Dataset1 (containing apple, cassava, cotton) were obtained from Kaggle [28], and leaf ...
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