The Convolutional Neural Networks Model of Algorithm Is Used to Identify Diseases in the Leaves of Sweet Lemon Plants-A Comprehensive Review

India is an agriculture-based country; the majority of the families depend on agriculture throughout the nation. Plant diseases cause significant yield losses in agriculture and cause significant damage. Crop diseases must be identified and prevented as soon as possible in order to improve yield. Deep convolutional neural network (CNN) models are used in this article to recognise and diagnose diseases in plants based on their leaves, as CNNs have demonstrated outstanding results in machine vision. Standard CNN models have a huge number of parameters and a significant computational cost. To increase citrus fruit yield and quality, sweet lemon leaf diseases must be found early. Deep learning approaches have recently demonstrated promising results in the detection of numerous plant diseases. The purpose of this investigation is to determine how well deep learning techniques can identify diseases of the sweet lemon leaf. Using a variety of preprocessing methods, a dataset of pictures of both healthy and sick sweet lemon leaves was gathered. A convolutional neural network (CNN) model was trained on the preprocessed dataset, and its hyperparameters were optimised for best performance. Deep learning methods can be used to detect and stop diseases in sweet lemon leaves early, enabling farmers to take preventative action.