Hybrid Deep Learning Approach for Automated Plant Disease Detection in Precision Agriculture
Rutika Pandurang Shinde
Student, Department of Computer Science, University of Florida, Gainesville, USA
Sagar Bharat Shah
Student, Department of Business Analytics, University of Cincinnati, Cincinnati, USA
Rishit Belani
Student, Department of Computer Engineering, Bharati Vidyapeeth College of Engineering, Pune, India
Naveen Chowdary Nune
Student, Department of Computer Science and Engineering, SRM University, Amaravati, India
Arihant Jha
Student, Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal, India
Harshal Pawar
Student, Department of Information Technology, Rajiv Gandhi Institute of Technology, Mumbai, India
Mohammed Mehataab Naazneen
Student, Department of Computer Science and Engineering, RVR & JC College of Engineering, Guntur, India
Jaskeerat Singh
Student, Department of Computer Science and Engineering, Ujjain Engineering College, Ujjain, India
ABSTRACT:
Plant pests and diseases pose a significant challenge to agricultural productivity and economic stability. Traditional detection methods, which rely on manual inspection by farmers or experts, are often time-consuming, expensive, and susceptible to human error. This paper introduces a novel automated plant disease detection system utilizing a hybrid machine learning (ML) approach based on deep convolutional neural networks (CNNs). Leveraging advancements in computer vision, our model demonstrates enhanced precision in plant protection and extends the application of computer vision to precision agriculture. The methodology encompasses image collection and database creation, with validation by agricultural experts, followed by the development and training of a deep CNN framework. The proposed system effectively distinguishes healthy leaves from diseased ones and separates leaves from their environmental background, achieving an overall accuracy of 96.77%. This solution offers a reliable, efficient, and scalable tool for plant disease recognition, catering to the needs of both amateur gardeners and professional agriculturists.
Published in: International Journal of Research in Engineering, Science and Management (Volume 7, Issue 12, December 2024)
Page(s): 114-121
Date of Publication: 26/12/2024
Publisher: IJRESM
DOI: https://doi.org/10.5281/zenodo.14569046
Cite as: Rutika Pandurang Shinde, Sagar Bharat Shah, Rishit Belani, Naveen Chowdary Nune, Arihant Jha, Harshal Pawar, Mohammed Mehataab Naazneen, Jaskeerat Singh, “Hybrid Deep Learning Approach for Automated Plant Disease Detection in Precision Agriculture,” in International Journal of Research in Engineering, Science and Management, vol. 7, no. 12, pp. 114-121, December 2024.