Precision Agriculture: Utilizing Machine Learning for Accurate Plant Disease Diagnosis

¹ Department of CS & IT, Kalinga University, Raipur, India
² Research Scholar, Department of CS & IT, Kalinga University, Raipur, India

^* Corresponding author: ku.divya@kalingauniversity.ac.in

Abstract

Precision agriculture, which is an emerging approach carried out to achieve improved crop productivity and sustainability by harnessing the use of integrated advanced technologies, has been an emerging approach. Among these, machine learning (ML) has been shown to have significant promise in the use case of plant disease diagnosis and transformation in order for it to become an accurate, timely, scalable solution. While ML has been applied to the precise diagnosis of plant diseases in this research, it was found to have a great potential to improve agricultural outcomes. Nowadays ML algorithms—both supervised and unsupervised—weeks used for analyzing series of datasets appropriated with plant health images, environmental condition data, and historical disease records. The efforts on the task of disease symptom identification have demonstrated high success with techniques like Convolutional Neural Networks (CNNs) and forms of ensembles. Furthermore, CNNs excel in dealing with high resolution images and detecting disease indicators that may not be visible through conventional means.In addition, ML is also integrated with remote sensing, and mobile platforms. Real time real data on crop conditions and stress factors is of great help to early detection of diseases through remote sensing. Mobile applications with ML algorithms also enable on the spot plant disease diagnosis from the smartphone camera, enabling site rather than later intervention. Current ML techniques in plant disease diagnosis and their impact on disease management and crop health are studied and a case study is made. The results highlight the benefits of ML application, such as improved accuracy of diagnostic, decreased reliance on manual inspections and increase in scalability, which will contribute to agricultural sustainable.