Retracted: Development of Tomato Leaf Disease Prediction System to the Farmers by using Artificial Intelligent Network
K. Muthamil Sudar, P. Nagaraj, Bhanu Prakash, M. Madhusudhan Reddy, M. Mallikarjuna Naidu, Hemanth Kumar
Abstract
Tomato leaf diseases have a big impact on yield and quality. The early-stage analysis and categorization of tomato leaf diseases based on past knowledge may help to limit pathogen transmission throughout the field while also increasing crop yield. Using a Deep Convolutional Neural Network, this paper proposes developing a prediction system that classifies tomato leaf diseases. The images present in the Taiwan and Plant-Village dataset are preprocessed using the image processing approach, and features are extracted and classified using ResNet and fine-tuned ResNet-50 model. The chemical, organic control measures are recommended to the farmers based on the classification measures. The interpretation was made over the ResNet and fine-tuned ResNet-50 model. The resultant number indicates that fine-tuned ResNet has a higher accuracy of 97.6 than the other standard models. The proposed system produces improved accuracy with a lower error rate of 0.090 in the classification of tomato leaf diseases, according to this research.