Agriculture plays a crucial role in India’s economy, supporting the livelihoods of 58 percent of the population and contributing 17-18 percent of the GDP. However, plant pests anddiseases pose significant challenges, leading to biotic stress that hampers yield potential and diminishes the quality and quantity of food. Safeguarding crops against diseases is imperative to meet the increasing food demand. Globally, the losses caused by pathogens, pests, and weeds account for 20-40 percent of agricultural productivity. The detection of diseases in cultivated plants is a vital and complex task in agricultural practices. Conventional methods of disease detection and classification are time-consuming and labor-intensive, making it difficult to find optimized solutions. This issue is particularly problematic as farmers and professionals in developing countries require efficient methods to monitor and identify diseases affecting their crops. The implementation of program-based identification for plant diseases offers advantages such as improved detection, reduced human effort, and time savings. In this article, a smart and efficient technique is proposed to detect and classify plant diseases with higher accuracy than existing methods. The pro- posed technique employs Convolutional Neural Networks (CNNs)and focuses on leaf diseases as the main area of interest.
Infection discovery in plants is a significant task thathas to be done in agriculture. To recognize the diseases in leaves, a continuous observation of the plants is required. Program- based identification of diseases in plants makes it easier to detect and reduces human efforts and time-saving. The proposed algorithm distinguishing sickness in plants and classifying them more accurately as compared to existing. Traditional approach for disease detection and classification requires huge amount of time and effort. In the last few years, advancement in technology and researchers’ focus makes it impossible to obtain optimized solution for it. A convolutional neural network is a form of artificial neural network specifically intended to process pixel input and is used for image recognition
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.