AI-Based Plant Disease Detection: Advancing Smart Agriculture

Abstract

Recent developments in artificial intelligence (AI), along with scientific know-how, have spurred a number of potential paths for the advancement of agriculture research. As a science-based discipline, plant pathology is a very important field in ensuring crop safety and security. Traditionally, the detection and identification of diseases in plants have remained a purely manual process based on expert observation.

This paper proposes an AI-based plant diseases identification framework that utilizes a combination of machine learning approaches, image processing techniques, and biological information pertaining to plants. The proposed system can effectively and accurately carry out leaf image processing through the usage of deep learning technologies such as Convolutional Neural Network models and transfer learning models.

In fact, the proposed methodology promotes environmentally friendly agricultural activities and helps in the early discovery and optimal use of resources in farming, with less dependency on chemicals. In addition, the proposed method shows the ability to apply AI concepts in converting the symptoms of diseases into useful information or in ensuring connections between scientific ideas and technological applications in controlling diseases in plants. Experimental outcomes confirm that AI helps in early disease detection for effective precision agriculture.