A Framework for Agriculture Plant Disease Prediction using Deep Learning Classifier

Abstract

The agricultural industry in Saudi Arabia suffers from the effects of vegetable diseases in the Central Province. The primary causes of death documented in this analysis were 32 fungal diseases, two viral diseases, two physiological diseases, and one parasitic disease. Because early diagnosis of plant diseases may boost the productivity and quality of agricultural operations, tomatoes, Pepper and Onion were selected for the experiment. The primary goal is to fine-tune the hyperparameters of common Machine Learning classifiers and Deep Learning architectures in order to make precise diagnoses of plant diseases. The first stage makes use of common image processing methods using ml classifiers; the input picture is median filtered, contrast increased, and the background is removed using HSV color space segmentation. After shape, texture, and color features have been extracted using feature descriptors, hyperparameter-tuned machine learning (ML) classifiers such as k-nearest neighbor, logistic regression, support vector machine, and random forest are used to determine an outcome. Finally, the proposed Deep Learning Plant Disease Detection System (DLPDS) makes use of Tuned ML models. In the second stage, potential Convolutional Neural Network (CNN) designs were evaluated using the supplied input dataset and the SGD (Stochastic Gradient Descent) optimizer. In order to increase classification accuracy, the best Convolutional Neural Network (CNN) model is fine-tuned using several optimizers. It is concluded that MCNN (Modified Convolutional Neural Network) achieved 99.5% classification accuracy and an F1 score of 1.00 for Pepper disease in the first phase module. Enhanced GoogleNet using the Adam optimizer achieved a classification accuracy of 99.5% and an F1 score of 0.997 for Pepper illnesses, which is much higher than previous models. Thus, proposed work may adapt this suggested strategy to different crops to identify and diagnose illnesses more effectively.

Revolutionizing Saudi Arabia's Agriculture: The IoT Transformation of Water Management

“…Further complicating efforts to maximise water efficiency is the demand for more precise data and information on water availability, agricultural needs, and weather patterns [4]. Besides, [5] investigated vegetable diseases in the Central Province in Saudi Arabia as early diagnosis of plant diseases may boost the productivity and quality of agricultural operations in the agricultural industry.…”

“…Reduced crop yields, lower profits, and an adverse effect on the environment are just some of the problems cited as a result of inefficient water management in the agricultural sector [5]. Reduced productivity and higher expenses for farmers are the results of traditional methods of water management that cause waterlogging, salinization, and soil degradation.…”