Optimized Deep Neural Network for Tomato Leaf Diseases Identification

Abstract

AbstractSmart Farming with the transformative potential of Artificial Intelligence has emerged as a promising breakthrough in enhancing the agricultural productivity. The rapid in supercomputing facilities has redefined Deep Learning and its applications are seemingly limitless. It involves the use of deeper neural networks for greater learning capabilities eventually resulting in higher performance and precision. Its ability to discern patterns at higher calibre made it penetrate into almost all applications of computer vision including the field of Agriculture for Plant leaf disease detection, fruit counting, water management, weed and pest control. This research work deals with the prediction of Tomato leaf diseases based on image classification, using pre-trained deep convolutional networks. The accuracy of the model is evaluated using standard classification metrics namely Accuracy, Precision, Recall and F1-score. Furthermore, this paper makes a recommendation on the best optimizing function among three standard optimizers for classification of tomato leaf diseases based on performance metrics.KeywordsDeep learningConvolutional Neural NetworkOptimizer functionTomatoPlant leaf diseases