Abstract
Agriculture is foundational to livelihoods and national economies, particularly in India. This paper presents AgroMind Grow, an end-to-end smart agriculture platform that consolidates weather intelligence, market analytics, crop calendar and planning, AI-powered plant disease detection and guidance, equipment tracking, expert consultation, farm planning, government scheme access, and an educational knowledge base into a unified web system. We focus on a deployable plant disease subsystem that enhances practical performance without retraining the base model by combining: crop pre-selection, class-space filtering of logits, test-time augmentation (TTA), aggressive but bounded confidence boosting, a rule-based generic status detector (healthy, chlorosis, fungal rot, powdery mildew), and a disease knowledge base covering 38 classes with symptoms, causes, and treatments (chemical, organic, prevention). Using EfficientNet-B2 (260×260), we report 99.74% validation accuracy (PlantVillage). In deployment, crop-aware post-processing and knowledge integration improve perceived correctness, interpretability, and decision readiness. Platform-level benefits include potential increases in farmer income (up to 25%), operational cost reduction (40%), and risk mitigation (50%), contingent on adoption and local context.
Keywords
Smart Agriculture, Plant Disease Detection, EfficientNet-B1, Confidence Calibration, Test-Time Augmentation, Knowledge Base, FastAPI, React.
Citations
IRE Journals:
Shantu Dhami, Mayank Yadav, Gopal Ji, Sourav, Mohammad Haris "AgroMind Grow Plant Disease Detection" Iconic Research And Engineering Journals Volume 9 Issue 8 2026 Page 412-419 https://doi.org/10.64388/IREV9I8-1714193
IEEE:
Shantu Dhami, Mayank Yadav, Gopal Ji, Sourav, Mohammad Haris
"AgroMind Grow Plant Disease Detection" Iconic Research And Engineering Journals, 9(8) https://doi.org/10.64388/IREV9I8-1714193
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