Plant Disease Detection.pdf

Plant disease detection techniques based on deep learning models are reviewed, focusing on image processing, segmentation, feature extraction, and classification. Plant diseases are classified into various groups based on frequency, severity, and cause, and can be detected through visual observation, which is time-consuming and requires costly expertise. Automated disease detection systems are needed to speed up the process.

Key techniques include image retrieval, segmentation, feature extraction, and classification. Segmentation detects the diseased portion, and feature extraction classifies the functionality using different classifiers. Various parameters are examined, including segmentation, feature removal, and classification approaches.

Examples of plant diseases include potato late blight, citrus canker, and soybean diseases, which can cause significant damage and economic losses. Disease diagnosis is crucial, and environmental factors such as climate change can influence disease incidence and distribution.

Deep learning models can be used to detect plant diseases, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These models can be trained on large datasets of images of healthy and diseased plants to learn patterns and features that distinguish between them.

The use of deep learning models for plant disease detection has several advantages, including high accuracy, speed, and scalability. However, there are also challenges, such as the need for large datasets and computational resources. Overall, deep learning models have the potential to revolutionize plant disease detection and diagnosis, enabling early detection and treatment, and reducing economic losses.

Key parameters examined in the study include segmentation, feature removal, and classification approaches, with a focus on image processing, image acquisition, segmentation, feature extraction, and classification. The study aims to support agricultural growth by analyzing the possibility of technologies for the identification of pest leaf diseases in plants.