• Deeksha GM
• Gnaneshwar JL
• Bhoomika G
• Mr Kiran Gaykwad

Understanding Ensuring timely detection of crop diseases remains a major challenge for modern agriculture, especially in large farmlands where manual monitoring is slow, inconsistent, and labor-intensive. This paper presents SmartCrop Vision, a deep learning–powered crop health and disease analysis system that leverages drone-captured aerial imagery to provide rapid, accurate, and automated crop diagnostics. The system integrates a custom Convolutional Neural Network (CNN) trained on diverse agricultural datasets to identify disease patterns, detect anomalies, and classify crop health conditions. Using drone imagery enables high-resolution field coverage, early diagnosis, and reduced dependency on manual inspection. SmartCrop Vision further generates intuitive visual outputs such as heatmaps and bounding boxes to highlight infected regions, making it easier for farmers and agronomists to take timely action. The proposed system bridges the gap between traditional crop monitoring and AI-driven precision agriculture, enabling improved yield prediction, reduced losses, and more sustainable farm management. Experimental results demonstrate the model’s ability to achieve robust disease detection accuracy, validating its practical applicability for real-world agricultural environments.

"SmartCrop Vision: Deep Learning–Driven Crop Health and Disease Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 12, page no.c3-c10, December-2025, Available :http://www.jetir.org/papers/JETIR2512202.pdf

"SmartCrop Vision: Deep Learning–Driven Crop Health and Disease Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 12, page no. ppc3-c10, December-2025, Available at : http://www.jetir.org/papers/JETIR2512202.pdf