Tomato is the most widely cultivated vegetable crop in Indian agricultural fields due to its suitability for growth in the tropical climate of the country. Its development, however, can be hampered by various climate conditions and other causes, which might result in lower yield. Plant diseases can also cause considerable financial losses and constitute a serious danger to agricultural productivity. Traditional disease detection techniques for tomato crops did not yield the desired results, and disease detection times were lengthy. Early illness detection can produce superior results compared to current detection models. Deep learning techniques could be applied to computer vision technology as a result of earlier disease detection. Convolutional neural networks (CNNs) are used in the article’s deep learning technique to identify tomato leaf diseases. The suggested method’s efficacy was proven by the studies, which produced an average illness classification accuracy of 82.4%. This method holds promise in facilitating early detection and prompt management of tomato leaf diseases, consequently enhancing the productivity and caliber of tomato crops.
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Detection of Diseases in Tomato Plants using Convolutional Neural Network
Semantic Scholar · Computer Science · 2023
Abstract
Tomato is the most widely cultivated vegetable crop in Indian agricultural fields due to its suitability for growth in the tropical climate of the country. Its development, however, can be hampered by various climate conditions and other causes, which might result in lower yield. Plant diseases can also cause considerable financial losses and constitute a serious danger to agricultural productivity. Traditional disease detection techniques for tomato crops did not yield the desired results, and disease detection times were lengthy. Early illness detection can produce superior results compared to current detection models. Deep learning techniques could be applied to computer vision technology as a result of earlier disease detection. Convolutional neural networks (CNNs) are used in the article’s deep learning technique to identify tomato leaf diseases. The suggested method’s efficacy was proven by the studies, which produced an average illness classification accuracy of 82.4%. This method holds promise in facilitating early detection and prompt management of tomato leaf diseases, consequently enhancing the productivity and caliber of tomato crops.