Plant Disease Detection through the Implementation of Diversified and Modified Neural Network Algorithms

Abstract

In the era of artificial systems, disease detection is becoming easier. For detecting disease, monitoring the plants 24 hours, visiting the agricultural office, or asking for help from a specialist seem difficult. This situation demands a user-friendly plant disease detection system, which allows people to detect whether the plant is diseased or not in an easier way. If the plant is diseased, a treatment plan will also be notified. In this way, people can easily save time, money, and, most importantly, plants. In this study, the researchers have collected data of vegetables from a field and applied multiple diversified Neural Network Algorithms such as CNN, MCNN, FRCNN, and, along with that, also proposed a new modified neural network architecture (ModCNN), which has produced 97.69% accuracy. The authors have also classified the bean leaf diseases into four categories according to their symptoms, which will help to identify diseases accurately.

Advanced Hybrid Model for Multi Paddy diseases detection using Deep Learning

“…The suggested model performs better than the most complex deep learning models with less parameters. F. Nihar et al [33] employed numerous NN Algorithms, including other algorithms, and created a novel updated neural network architecture that produced 97.69% accuracy. M. Sardogan et al [34] to analyze and categorize tomato leaf disease, they have suggested a CNN and LVQ algorithm-based technique.…”

Image-based Onion Disease (Purple Blotch) Detection using Deep Convolutional Neural Network

“…Nihar et al [28] suggested a neural network-based system for detecting plant disease that will aid in the development of the agricultural system which can properly determine whether a plant is infected or healthy and has a 97.7% accuracy. This technology enables the user to detect diseases faster allowing them to take acceptable precautionary steps and save crops.…”