Image-Based Plant Disease Detection and Rectification

The automation of plant disease detection in agriculture is a critical concern globally, given the increasing demand for food due to population growth. The use of technology has significantly enhanced the accuracy of disease detection in both plants and animals, initiating a series of activities to combat and curb their spread. Despite years of research, there are still gaps in the detection and discovery process, leading to delayed responses and potential pandemics. This study focuses on leveraging artificial intelligence, specifically machine learning and deep learning, to automatically detect plant diseases. The transition from conventional machine learning to deep learning over the past five years is discussed, along with an in-depth exploration of various datasets related to plant diseases. The article also identifies problems and difficulties with the current systems. Although plants are essential to the world’s food supply, environmental variables can cause plant diseases, which can lead to significant losses in productivity. Manual detection is an unreliable method for identifying and preventing diseases since it takes a long time and is prone to errors. The current state of machine learning (ML) and deep learning (DL) techniques for the early detection of plant diseases is examined in this research. The study shows how ML and DL can be used to increase the precision and efficacy of plant disease identification. It tackles issues including data accessibility, image quality, and differentiating between good and unhealthy plants. The report presents a thorough grasp of the state of the field, offers answers to these problems, and offers insightful information.