DETECTION OF PLANT DISEASES AND PESTS USING DEEP LEARNING MODELS: A RECENT RESEARCH

Abstract

Crop yield and quality are significantly impacted by plant diseases and pests. Many plant diseases and pests can be identified with the help of digital photographs of the affected plants. In the field of digital image processing, deep learning has recently set a new, exceptionally high standard. Researchers have shown considerable enthusiasm for the prospect of using deep learning methods in the study of plant diseases and the detection of pests. Through a definition of the problem and a comparison to more conventional approaches, this study defines the topic of plant disease and pest detection. In this research, we will analyse previous deep learning-based studies on plant pest identification and detail the benefits and drawbacks of various network architectures utilised for classification, observation, and segmentation. Everyone has access to the same resources, and studies are compared. The goal of this research is to identify potential challenges associated with using deep learning to detect plant diseases and pests in the field. Solutions and areas for further research are recommended. In conclusion, this research provides a thoughtful analysis based on existing information, as well as future directions for plant disease and pest identification.

Keywords: Convolutional neural network, Deep learning, Classification, Plant diseases and pests, Object detection, Segmentation.