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This work is licensed under a Creative Commons Attribution 4.0 International License.
AI-DRIVEN CROP DISEASE PREDICTION AND MANAGEMENT SYSTEM
Miss.Radhika Sugdev Gavhale, Asst. Prof. Arsalan A. Shaikh
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Abstract: Agriculture is very important for providing food, creating employment, and supporting the economy. However, crop diseases are a major problem for farmers because they can reduce crop production, lower the quality of crops, and increase farming costs. Finding a disease at an early stage is important because it allows farmers to take the right action before the disease spreads.
Traditionally, farmers identify crop diseases by looking at the leaves and other parts of the plant. This method can take time, and the accuracy of the diagnosis often depends on the farmerβs or expertβs experience and knowledge.
To overcome these problems, the proposed AI-Driven Crop Disease Prediction and Management System uses Artificial Intelligence, Image Processing, and Machine Learning to automatically identify crop diseases. In this system, the farmer can take a picture of a crop leaf or upload an existing image through the application. The image is first processed to make it easier for the system to analyze. A trained Convolutional Neural Network (CNN) model then examines the image, identifies important patterns, and predicts the possible disease affecting the crop.
After detecting the disease, the system provides information about the disease along with suitable management and preventive measures. The main purpose of the system is to help farmers identify crop diseases quickly and take appropriate action at an early stage. It can reduce the time needed for initial disease identification and make AI-based agricultural support easier to access.
However, the accuracy of the system depends on the quality and variety of the images used for training, as well as the quality of the images uploaded by users. Overall, the proposed system can serve as a useful decision-support tool for modern and smart agriculture.
Keywords: Artificial Intelligence, Machine Learning, Crop Disease Detection, CNN, Image Processing, Deep Learning, Smart Agriculture, Disease Prediction, Crop Management.
Traditionally, farmers identify crop diseases by looking at the leaves and other parts of the plant. This method can take time, and the accuracy of the diagnosis often depends on the farmerβs or expertβs experience and knowledge.
To overcome these problems, the proposed AI-Driven Crop Disease Prediction and Management System uses Artificial Intelligence, Image Processing, and Machine Learning to automatically identify crop diseases. In this system, the farmer can take a picture of a crop leaf or upload an existing image through the application. The image is first processed to make it easier for the system to analyze. A trained Convolutional Neural Network (CNN) model then examines the image, identifies important patterns, and predicts the possible disease affecting the crop.
After detecting the disease, the system provides information about the disease along with suitable management and preventive measures. The main purpose of the system is to help farmers identify crop diseases quickly and take appropriate action at an early stage. It can reduce the time needed for initial disease identification and make AI-based agricultural support easier to access.
However, the accuracy of the system depends on the quality and variety of the images used for training, as well as the quality of the images uploaded by users. Overall, the proposed system can serve as a useful decision-support tool for modern and smart agriculture.
Keywords: Artificial Intelligence, Machine Learning, Crop Disease Detection, CNN, Image Processing, Deep Learning, Smart Agriculture, Disease Prediction, Crop Management.
How to Cite:
[1] Miss.Radhika Sugdev Gavhale, Asst. Prof. Arsalan A. Shaikh, βAI-DRIVEN CROP DISEASE PREDICTION AND MANAGEMENT SYSTEM,β International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI: 10.17148/IJIREEICE.2026.14913
This work is licensed under a Creative Commons Attribution 4.0 International License.