Abstract :

The Evolving Climatic Condition And The Existing Agricultural Techniques Such As Grey Soils Have Negative Effects On Incorporation Of Sustainable Agricultural Systems In The World. Timely And Appropriate Diagnosis Of Plant Disease Results In The Reduction Of Crop Wastage And Maximum Production. The Comparative Research Design Appeared In This Work Because We Used Artificial Intelligence To Identify Crop Diseases And The Recommended Type Of Fertilizers With The Help Of Leaf Images And The Content Of Soil Nutrients. We Collected The Crop Leafs And Surface Analysis Nitrogen, Phosphorus And Potassium In Soil And Used Deep And Machine Learning. Process Algorithms With The Data. Deep Learning Techniques Used On. Match Diseases And Classify Fertilizers Convolutional. Neural Networks (CNNs) And Randomly Forested.The Performance Indicators That We Compared The Models Based On Include. As Recovery, Accurateness, Recall And F-measure. Findings Indicated That CNNs Detected Diseases More Successfully Than Traditional Approaches And Random Forest Made Excellent Suggestions Of Fertilizers. An Online Tool Of Identifying Real-time Crop Diseases Based On A Flask-based Application Was Created With The Focus On Demonstratingthe Significance Of AI-based Decision Support DSS (Systems) In Enhancing Crop Productivity And Efficient Resources Distribution.

Published:

25-6-2026

Issue:

Vol. 26 No. 6 (2026)

Page Nos:

1498-1503

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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