Year: 2026
Type: article
Abstract: In agriculture, detection of plant diseases is a crucial activity that is important for ensuring crop health and increasing productivity. In this project, we provide an in-depth analysis of the use of a Convolutional Neural Network (CNN) for plant disease detection. This paper utilizes a dataset that includes a sizable number of photos showing both healthy plants and plants with various diseases. The dataset is enhanced and pre-processed to improve the ability to be generalized the model. Convolutional layers, pooling layers, and fully connected layers are used in the creation and training of a CNN architecture to extract useful characteristics from the images. The model's parameters are optimized during the training phase using stochastic gradient descent and a properly selected learning rate. Over-fitting is avoided by using regularization techniques like dropout and weight decay. The proposed model demonstrates strong performance, achieving an accuracy of 96.84% in classifying plant diseases from leaf images. The dataset used for training and evaluation was obtained from Kaggle and includes images representing multiple plant disease categories. By leveraging deep learning techniques, the model effectively distinguishes between healthy and diseased leaves, addressing limitations associated with traditional visual inspection methods, which are often time-consuming and prone to human error. The findings highlight the potential of automated disease detection systems in agriculture, enabling early diagnosis and timely intervention to reduce crop losses and enhance overall crop management practices.
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W7147247482