A Deep Learning Framework for Early Detection of Potato Plant Diseases

Abstract

In our rapidly evolving world, technology has become an inseparable aspect of daily life, with digital tools serving as essential aids for various tasks. Despite this, individuals and farmers encounter persistent challenges arising from sluggish internet connectivity and needing external assistance to manage online resources. These obstacles significantly hinder their capacity to oversee crops and promptly address potential plant diseases. A noteworthy predicament farmers face involves sluggishly identifying diseases within their crops, often compounded by a lack of knowledge about appropriate remedies. Traditional techniques for pinpointing plant diseases tend to be time-intensive and demand specialised expertise. Such circumstances present formidable obstacles for farmers lacking access to expert guidance or those grappling with limited time for effective crop management.

We have developed a Deep Convolutional Neural Network (DCNN) model to address this predicament to detect disease. The core purpose of our application is to expedite and enhance disease recognition for burgeoning plants, ultimately benefiting farmers. The mobile/web interface provides users with a user-friendly means of querying plant diseases and their potential treatments by capturing images of their plants. Basic functionalities encompass disease identification, enabling users to ascertain afflictions swiftly. Furthermore, the application includes provisions for storing crop-related data and tracking progress, thereby empowering farmers to make well-informed decisions concerning crop management.

At its essence, our project seeks to empower individuals and farmers with tools indispensable for proficient crop oversight. The application is a conduit for expedited disease detection and prompt mitigation, fostering healthier plants and elevated yields. Ultimately, our innovation holds the potential to substantially contribute to sustainable agricultural practices, effectively addressing the modern challenges encountered by farmers within our technology-driven milieu.