FRUIT CROP DISEASE CLASSIFICATION USING QUANTUM MACHINE LEARNING: A PILOT STUDY
DOI:
https://doi.org/10.55197/qjoest.v6i4.257Keywords:
crop stress detection, nematodes, deep learning, MobileNetV2, quantum-inspired AI, chlorophyll and nutrient estimationAbstract
Crop losses are often caused by factors like plant diseases, nematode attacks, and nutrient deficiencies. The problem is that many of these stresses look alike on the leaves, which makes early detection difficult in the field. In this pilot study, we built and tested part of an AI system that uses leaf photos together with simple soil information to spot crop stress early. Our approach combines MobileNetV2 with a quantum-inspired feature layer, creating a hybrid deep learning model. Trained on about 92379 labeled leaf images, the model was able to tell apart healthy leaves and nematode-infested leaves with strong accuracy. In addition to classification, the system also estimates chlorophyll and key nutrient levels (N, P, K) directly from RGB leaf images, while soil pH values are added manually as an extra input. Looking ahead, we plan to extend this framework with hyperspectral imaging and richer soil data to give more complete insights. The ultimate goal is to create an affordable and scalable decision-support tool that guides farmers with simple “step–check–action” advice to protect yields.
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Copyright (c) 2025 ABHISHEK CHANDRAKANT NIKAM, RAHUL BORATE, MASIRA KULKARNI, AAYUSH PATIL, SADAF SHAIKH
This work is licensed under a Creative Commons Attribution 4.0 International License.
