AgriScan: Next.js powered cross-platform solution for automated plant disease diagnosis and crop health management

• Published in: Journal of Electrical Systems and Information Technology Vol. 11; no. 1; pp. 45 - 23
• Main Authors: Seyam, Touhidul Alam, Pathak, Abhijit
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-12-2024; Springer Nature B.V; SpringerOpen
• Subjects: Agriculture; Artificial Intelligence; Artificial neural networks; Automation; Citrus fruits; Classification; Computer vision; Crop diseases; Crop production; Diagnosis; Disease control; Electrical Engineering; Electrical Machines and Networks; Engineering; Image enhancement; Information Systems and Communication Service; Machine learning; Mechanical Engineering; Performance evaluation; Plant diseases; Plant layout; Power Electronics; Real time
• ISSN: 2314-7172; 2314-7172
• DOI: 10.1186/s43067-024-00169-7

Plant diseases present a formidable challenge to the agricultural sector worldwide, leading to significant losses, with the US experiencing annual losses amounting to one-third of crop production. Diagnosis of crop diseases through optical observation of leaf symptoms is particularly daunting for farmers with limited resources. Therefore, there is an urgent need for enhanced detection, monitoring, and prediction methods to mitigate agricultural losses effectively. Harnessing the power of computer vision and deep learning, this paper introduces a cross-platform system designed to automate plant leaf disease diagnosis. The system employs convolutional neural networks to classify 46 disease categories, trained on a dataset comprising 96,206 images of healthy and infected plant leaves. The user interface, accessible across multiple platforms including Android, iOS, Windows, and Linux, allows farmers to capture photos of infected leaves and receive real-time disease classification along with confidence percentages. By empowering farmers to maintain crop health and prevent the application of incorrect fertilizers, the system aims to optimize crop productivity. Performance evaluation includes metrics such as classification accuracy and processing time, with the model achieving an impressive overall accuracy of 93.45% across 46 common disease classes spanning 16 crop species.