Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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A Comprehensive Study of Tomato and Potato Leaf Disease Identification

Author(s) Samay Mody, Araveti Sarvesh, Vishnu. K
Country India
Abstract Detection of leaf diseases on tomato and potato plants represents a crucial yet labor-intensive task in agricultural practices. This paper presents an innovative approach for efficient disease identification using computer vision and machine learning tech niques. The proposed system exhibits the capability to accurately detect 20 different diseases affecting five common plant species, achieving a remarkable accuracy of 93%. Keywords encompass digital image processing, foreground detection, machine learning, and plant disease identification. India encounters substantial crop yield losses, amounting to 35% annually, due to plant diseases. The challenge of early disease detection persists due to the dearth of laboratory infrastructure and expert knowledge. In this study, we investigate the feasibility of computer vision methods for scalable and early plant disease identification. Notably, the scarcity of sufficiently large non-laboratory datasets hinders progress in vision-based plant disease detection. In response to this challenge, we introduce PlantDoc, a comprehensive dataset designed for visual plant disease detection. This dataset encom passes 2,598 data points across 13 plant species, featuring up to 17 distinct disease classes. Annotating internet-sourced images demanded around 300 human hours of effort. To demonstrate the effectiveness of our dataset, we trained three models for plant disease classification. Our findings reveal that utilizing our dataset can enhance classification accuracy by up to 31%. We believe that our dataset serves as a valuable resource to lower the barriers to entry for computer vision techniques in the field of plant disease detection.
Keywords Deep Learning, Digital image pro cessing, Foreground detection, Machine learning, Plant disease detection.
Field Computer Applications
Published In Volume 7, Issue 2, March-April 2026
Published On 2026-04-15
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E-ISSN 3048-7641
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