Artificial Intelligence for Plant Disease Detection: A High-Accuracy Approach
Creators: name: Rahman, Md Mostafizur
name: Chen Xiao, Hua
Publisher: name: Zenodo
Year: 2023

description: This paper reveals an innovative application of artificial intelligence (AI) to detect plant illnesses, which represents a significant advancement in agriculture. Utilizing the expansive and diverse "Plant Village Dataset," the research centers on applying the potent ResNet-50 deep learning framework to attain a remarkable 99.23% accuracy rate. Collect the Plant Village dataset, which consists of annotated images of both healthy and diseased plants, before using ResNet50 to identify plant diseases. One of the preprocessing steps is to resize and normalize the images. To further improve the pre-trained ResNet50 model, replace the top classification layer with a new output layer. Train the revised model on the dataset using transfer learning to leverage the pre-trained ResNet50 weights. During training, use categorical cross-entropy as the loss function and an appropriate optimizer. Following training, the model used to determine whether diseases are present in images of fresh plants; a probability score that is closer to 1 indicates the existence of diseases. Precision in disease identification is being improved using post-processing techniques. The management of plant diseases may be altered as a result of this achievement, enhancing crop health and yield. This study's central concept originated from the meticulous selection of a sizable collection of photos from the Plant Village Dataset. This dataset covers a broad spectrum of crop varieties and plant diseases, reflecting issues that farmers encounter in the real world. It is highly beneficial to have this kind of diversity in the data when rigorously training and assessing models. The use of AI to identify plant diseases has advanced significantly with this study. It accomplishes this by utilizing the potent ResNet-50 architecture, a well-known deep learning model for its capacity to identify intricate patterns and characteristics. The model undergoes extensive training, which includes techniques for enhancing its performance with additional data, optimizing its hyper parameters, and fine-tuning it. The study's findings, when applied to the Plant Village Dataset, are nothing short of groundbreaking. The remarkable 99.23% success rate of the ResNet-50 model in diagnosing plant diseases is impressive. Because of its exceptional precision, it is a potent tool that has the potential to significantly alter the way illnesses are treated in agriculture. The AI model's accuracy and dependability suggest that there may be simple, affordable, and long-lasting methods for identifying plant illnesses early on, enabling farmers to take prompt, informed action. This study highlights the significant role AI technologies play in resolving pressing issues and demonstrates how they could fundamentally alter agriculture. The ResNet-50 model, when utilized on the Plant Village Dataset, provides a compelling illustration of how artificial intelligence might alter the methods used in plant disease research. This achievement represents a significant advancement in the field and has ramifications for sustainable agriculture, crop loss reduction, and global food security.