• Pavan Kumar
• Rahul

To address the critical challenge of early crop disease detection, the integration of artificial intelligence (AI) and deep learning has become a cornerstone of modern precision agriculture. This study proposes an AI-based crop disease detection system that leverages deep learning and advanced image analysis techniques to enhance monitoring accuracy and agricultural productivity. Specifically, convolutional neural networks (CNNs) are employed to automatically learn and classify disease patterns from high-resolution images of crop leaves, thereby minimizing the need for manual inspection. Image preprocessing techniques such as contrast enhancement and segmentation are utilized to emphasize disease-specific features, further improving classification performance. Experimental evaluations conducted on benchmark datasets demonstrate high classification accuracy, robustness against environmental noise, and adaptability across multiple crop species. Integrating this system into smart farming environments enables real-time disease detection, optimized resource utilization, and informed decision-making. The results underscore the transformative potential of AI and deep learning in advancing sustainable agriculture by enhancing plant health monitoring and yield prediction.

Image analysis Precision agriculture, Agricultural automation AI-based crop disease detection, Smart farming, Plant pathology, Convolutional Neural Networks (CNNs).

"Enhancing Accuracy in Crop Disease Diagnosis Using Ensemble Deep Learning Models and High-Resolution Image Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.c197-c203, July-2025, Available :http://www.jetir.org/papers/JETIR2507222.pdf

"Enhancing Accuracy in Crop Disease Diagnosis Using Ensemble Deep Learning Models and High-Resolution Image Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. ppc197-c203, July-2025, Available at : http://www.jetir.org/papers/JETIR2507222.pdf