Jyoti Grover

About

Jyoti Grover is a researcher at the forefront of applying computational intelligence to agricultural challenges, with a primary focus on deep learning and optimization techniques for plant disease detection. Her work bridges the gap between computer vision and sustainable farming, particularly through the development of image-based identification models that leverage convolutional neural networks (CNNs) and genetic algorithms (GA). In her most-cited study, "Performance Evaluation of Image-Based Diseased Leaf Identification Model Using CNN and GA" (2022), Grover systematically evaluates how combining CNNs with GA enhances the accuracy and efficiency of diagnosing plant diseases from leaf images—a critical step toward automated, real-time crop monitoring. This contribution has garnered attention in the precision agriculture community, with 2 citations to date, reflecting its emerging relevance. Grover’s research not only advances machine learning methodologies but also offers practical solutions for reducing crop loss and pesticide overuse. Her work stands out for its integration of evolutionary algorithms with deep learning, a synergistic approach that optimizes model performance while minimizing computational costs. As a researcher, Grover is dedicated to making AI-driven agriculture accessible and effective, positioning her as a promising voice in the intersection of technology and food security.

Research Focus

Key Achievements

Top Papers

1. 1
   Performance Evaluation of Image-Based Diseased Leaf Identification Model Using CNN and GA

   2 citations · 2022

Key Collaborators

Contact & Links