Plant Leaf Disease Detection Using CNN

Agriculture is the major area in several regions, including Pakistan and India, where roughly 55% to 60% of the inhabitant relies on it explicitly and implicitly. In agricultural countries, plant disease is a serious issue. Each year farmer faces loss due to the plant diseases and it is a difficult task to detect disease by a naked eye. The suggested approach intends to decrease agricultural losses. An automated plant identification and diagnosis is required for this. The proposed system helps to find and recognize disease at initial phase or at least diagnose the disease to avoid further degradation. AgriCure is a plant disease detector, an android application to detect disease in apple and tomatoes. This method overcomes the issues of cost, time, efficiency, restricted precision, and area for plant diagnosis because of traditional manual methods or naked eye inspections. The proposed system includes these modules i.e. disease detection, disease information, learn about diseases, add reminders and notes to remind tasks and show the weather forecast system. The system utilizes deep CNN method to diagnose and classify defect and show their descriptions. Such automated systems have been constructed in the past, but they generally have low accuracy or can’t manage a large range of plants. The system mainly focuses on the detection of diseases in apples into predefined categories like healthy apple leaf, apple scab, apple black rot, apple cedar rust, and in tomatoes predefined categories like healthy tomato leaf, bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, tomato spider mites, tomato target spot, tomato yellow leaf curl virus, tomato mosaic virus. As a result, the suggested system is limited to some restrictions and difficulties. Team members of the project, on the other hand, would be working diligently to meet major milestones of the planned system. An automated and accurate plant disease detection system like AgriCure addresses a significant social problem by helping farmers reduce crop losses, ensuring food security, and promoting sustainable agriculture.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-02-17
DOI
https://doi.org/10.5281/zenodo.15185005
Primary Topic
Smart Agriculture and AI
Type
article
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article

Plant Leaf Disease Detection Using CNN

Zenodo (CERN European Organization for Nuclear Research)
Smart Agriculture and AI
article

Plant Leaf Disease Detection Using CNN

article en

Abstract

Agriculture is the major area in several regions, including Pakistan and India, where roughly 55% to 60% of the inhabitant relies on it explicitly and implicitly. In agricultural countries, plant disease is a serious issue. Each year farmer faces loss due to the plant diseases and it is a difficult task to detect disease by a naked eye. The suggested approach intends to decrease agricultural losses. An automated plant identification and diagnosis is required for this. The proposed system helps to find and recognize disease at initial phase or at least diagnose the disease to avoid further degradation. AgriCure is a plant disease detector, an android application to detect disease in apple and tomatoes. This method overcomes the issues of cost, time, efficiency, restricted precision, and area for plant diagnosis because of traditional manual methods or naked eye inspections. The proposed system includes these modules i.e. disease detection, disease information, learn about diseases, add reminders and notes to remind tasks and show the weather forecast system. The system utilizes deep CNN method to diagnose and classify defect and show their descriptions. Such automated systems have been constructed in the past, but they generally have low accuracy or can’t manage a large range of plants. The system mainly focuses on the detection of diseases in apples into predefined categories like healthy apple leaf, apple scab, apple black rot, apple cedar rust, and in tomatoes predefined categories like healthy tomato leaf, bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, tomato spider mites, tomato target spot, tomato yellow leaf curl virus, tomato mosaic virus. As a result, the suggested system is limited to some restrictions and difficulties. Team members of the project, on the other hand, would be working diligently to meet major milestones of the planned system. An automated and accurate plant disease detection system like AgriCure addresses a significant social problem by helping farmers reduce crop losses, ensuring food security, and promoting sustainable agriculture.

Zenodo (CERN European Organization for Nuclear Research)
The Dialogue (US)
Zero hunger
Openalex Percentile: Top 68%
Smart Agriculture and AI
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