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Deep Learning Based Recognition of Plant Diseases

2023; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-981-19-7169-3_8

1876-1119

Swetha Parthiban, Sneha Moorthy, Sribalaji Sabanayagam, Shobana Shanmugasundaram, Athishwaran Naganathan, Mohan Annamalai, Sabitha Balasubramanian,

Greenhouse Technology and Climate Control

Every country's primary requirement is agricultural products. Infected plants have an impact on the agricultural production and economic resources of a country. Early detection of plant diseases minimizes the risk of crop loss by allowing farmers to adopt curative and preventative actions to avoid further damage. Citrus is a large plant that is primarily grown in tropical areas of the world because of its high vitamin C and other key nutrient content. Citrus plant diseases play a vital role in causing a great financial loss to the farmers and affect the economy of the country. The tomato ranks among the most economically important vegetable crops, with a global production rate of around 200 million tonnes. But due to diseases occurring in the tomato plants, the farmers are facing a great loss. To prevent these losses and provide an immediate cure, we create a model to detect plant diseases. To get a better performance model, the data augmentation process is used, and we got 18,392 images. In our model, we used a convolutional neural network (CNN) to classify the citrus and tomato leaf diseases. The diseases are Mancha Graxa, Citrus Canker, Tomato Early Blight, Tomato Septoria Leafspot, Tomato Spider Mite Two-spotted spider mite (Tetranychus urticae), Tomato mosaic virus (Tobamovirus), Tomato yellow leaf curl virus (Begomovirus), Tomato Target Spot (Corynespora cassiicola). Convolution Neural Network (CNN) is stacked by multiple convolutions and pooling layers to detect plant leaf diseases. We train a model with augmented images. After the model gets trained, it is thoroughly tested to ensure that the results are accurate. Thereby we were able to achieve high validation accuracy with a rate of (97.02%) for this dataset used. As the results showed, applying the deep CNN model to identify diseases could greatly impact the accurate identification of diseases, and could even prove useful in detecting diseases in real-time agricultural systems.