Cloud-Based Smart Agriculture System for Early Detection of Crop Diseases

International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 4, pp. 97-102, April 2026.

https://doi.org/10.58482/ijersem.v2i4.13

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

Cloud-Based Smart Agriculture System for Early Detection of Crop Diseases

N Babu, Pokala Varalakshmi, Balaraju Navya, D Varshitha, K Nithin

Department of CSE, Siddharth Institute of Engineering & Technology, Puttur, AP, India.

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Abstract: : Crop diseases significantly affect agricultural productivity and food security, particularly in regions where timely expert consultation is limited. A cloud-based smart agriculture framework is presented for early detection of crop diseases using image-based deep learning and Internet of Things (IoT)-enabled data acquisition. Field images captured through mobile devices or cameras are transmitted to a cloud platform where preprocessing, normalization, and augmentation improve image consistency. A convolutional neural network integrated with transfer learning enables accurate classification of diseased and healthy leaves, along with severity-level estimation. The cloud environment supports scalable storage, high-performance computation, and rapid inference, enabling near real-time response and decision support. The system further provides disease identification results, preventive guidance, and treatment recommendations through user-accessible interfaces. Experimental evaluation demonstrates classification accuracy exceeding 90% across multiple crop disease categories with reliable response time. The framework contributes to reducing crop losses, improving decision-making efficiency, and supporting sustainable agricultural practices through intelligent and scalable disease monitoring infrastructure.

Keywords: Crop disease detection, Smart agriculture, Cloud Computing, Deep learning, Convolutional Neural Networks.

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This article is part of Volume 2, Issue 4 (April 2026)