Deep Learning for Precision Agriculture: Tomato Leaf Disease Diagnosis Using Convolutional Neural Networks
Abstract
One of the large crops in Pakistan is tomato, which is cultivated on a significant area and yields approximately 0.31 million tons every year. It is a major income earner to agricultural communities, and it is a key contributor to nutritional requirements. Though not underestimated, diseases and unfriendly environmental conditions often lower tomatoes yields particularly at the initial stages of growth. The current research proposes a Convolutional Neural Network (CNN) architectural model of detecting and classifying significant tomato leaf diseases. The methodology involves taking of images, preparation of data, preprocessing, training of CNN, and measurement of performance by conventional accuracy and precision measures. Empirical evidence has shown that the model had a training and validation accuracies of about 90 and 85-88 respectively, showing its efficiency in the detection of diseased and healthy leaves. This suggested framework provides a feasible input to the improvement of the disease management system to enhance sustainable tomato production and to empower the lives of farmers in Pakistan.
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