A Research Investigation on Conventional Neural Network-Based Disease Detection Techniques for Sugarcane Plants

Abstract

In the current times, the number of viruses and infections in the sugarcane plants is widespread. If we want to properly correct these infections, we need to use artificial intelligence such as CNN and RNN. Therefore, in this study, how to prevent the disease in sugarcane using the CNN and RNN system is taken as a test. This model is trained on a diverse dataset of automatic diagnosis images, with a focus on addressing the inherent class in detection. The neural network architecture is designed to capture intricate patterns indicative of sugarcane manifestations in automatic diagnosis images. Through an iterative training process, the model learns to discern subtle features associated with automatic diagnosis, achieving remarkable accuracy. The experimental results confirm the efficacy of our proposed methodology. It explores the many CNN architectures used for plant disease detection, including AlexNet, VGGNet, ResNet, InceptionNet, and DenseNet, as well as their pros and limitations. The survey also discusses the importance of RNNs in plant disease detection, specifically in time-series data analysis, where RNNs have been shown to be useful in forecasting the spread of plant diseases over time. This report also provides a successful outcome for researchers working on the creation of a recognition system for sugarcane diseases. Keywords: convolutional neural networks (cnn), recurrent neural networks (rnn), Sugarcane plants disease detection Accuracy, Genetic algorithm

Code and data availability

The article is a review-style paper on CNN/RNN-based sugarcane disease detection. It describes UAV multispectral imagery and ground-truth tagging of WLD-infected plants but provides no public dataset, image, code, model, or supplement with availability language or URLs. The only external links are to cited prior work (

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