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PHYTOSIGHT

1st Dr. M. Vasuki, 2rd R. Kamaleeswari and 3rd S. Krishnapriya

1Associate Professor, Department of computer Applications, Sri Manakula Vinagayar Engineering college (Autonomus) Puducherry 605008, India

dheshna@gmail.com

2 post Graduate student, Department of computer Applications, Sri Manakula Vinagayar Engineering College (Autonomus) Puducherry 605008, India

rameshkamaleeswari@gmail.com

3post Graduate student, Department of computer Applications, Sri Manakula Vinagayar Engineering College (Autonomus) Puducherry 605008, India

krishnapriya2182@gmail.com

*Corresponding author’s email address: krishnapriya2182@gmail.com

 

 

 

ABSTRACT: An automated system is being developed to detect leaf diseases and suggest appropriate treatments by leveraging machine learning and image processing techniques. Agricultural productivity often suffers due to plant diseases, resulting in significant economic losses. The system addresses this issue by allowing users, primarily farmers, to upload images of plant leaves. OpenCV is utilized for image preprocessing, while a neural network built with Keras and TensorFlow handles disease classification. After analyzing the uploaded images, the system detects the presence of diseases and provides diagnosis and treatment recommendations. A graphical user interface (GUI) is created using Tkinter for desktop applications, complemented by a web-based platform developed with Flask to enhance accessibility. The aim is to improve crop yield by delivering a fast, reliable, and accessible solution for plant disease identification.

Keywords: precision agriculture, convolutional neural networks, deep learning, leaf disease detection, and agricultural production optimization.

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