An Innovative Techniques on Disease Recognition Approaches in Sugarcane Plants Using Conventional Neural Network

Abstract

In the current times, the number of contagions and infections in the sugarcane shops is widespread. However, we need to use artificial intelligence similar as CNN and RNN, if we want to duly correct these infections. thus, in this study, how to help the complaint in sugarcane using the CNN and RNN system is taken as a test. This model is trained on a different dataset of automatic opinion images, with a focus on addressing the essential class in discovery. The neural network armature is designed to capture intricate patterns reflective of sugarcane instantiations in automatic opinion images. Through an iterative training process, the model learns to discern subtle features associated with automatic opinion, achieving remarkable delicacy. The experimental results confirm the efficacy of our proposed methodology. It explores the numerous CNN infrastructures used for factory complaint discovery, including AlexNet, VGGNet, ResNet, InceptionNet, and DenseNet, as well as their pros and limitations. The check also discusses the significance of RNNs in factory complaint discovery, specifically in time- series data analysis, where RNNs have been shown to be useful in ratiocinating the spread of factory conditions over time. This report also provides a successful outgrowth for experimenters working on the creation of a recognition system for sugarcane conditions.