A Hyper parameter Tuning and Image Enhancement of Tomato Leaf Diseases by Augmented Classification WSN based Identification

Abstract

Tomato is the maximum profitable horticulture crops grown in many countries like India, the United States of America, Italy, China, and Africa. One more reason for giving so much attention to the tomato crop, as it forms a vital part of the food and a source of vitamin for a vast vegetarian population of the country. A mobile telephone with a reasonably good camera becoming affordable with low price, size, and weight. With the help of captured video and images, the computer-based demonstration and knowledge may be provided to the rural farmers. Eleven-layer deep CNN was projected in this paper to handle the plant leaf disease detection challenges. The designing, training, and testing scripts of the eleven-layer deep convolutional neural network were written in the python programming language. we used African Buffalo Optimization algorithm for hyperparameter tuning of Eleven-Layer Deep Convolutional Neural Network Model (CNN) classifier, in this experimentation, we mainly dataset is collected from raw based to implement some high-quality camera to capture the picture for train and test process. A rolling-based image taking scheme was made to capture four borders of every tomato plant cultivation area to detect and identify leaf disease. We described Data Augmentation as a strategy to prevent overfitting via regularization. And this also used to multiple the images by using Generative Adversarial Networks (GANs). And the image noise is clear by using Image Processing and Image Enhancement by using the methodology of Moving Average (MA) Filter. The classifier model can easily indent to classify the diseases. And also additionally, feature extraction and segmentation methods are implemented to predict crop diseases. In the prediction of plants, diseases are recognized at that time alert message is stored in the cloud-based Wireless Sensor Network (WSN) based cloud storage, on next thing the (Global System for Mobile communication) GSM model is used to send the diseases affected message to the farmer's mobile.