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A Review on Deep Learning-Based Crop Disease Detection and Fertilizer Recommendation Systems for Smart Agriculture

Ediga Amarnath Goud *and V. Rathikarani

Department of CSE, Annamalai University, Chidambaram, Tamilnadu, India

Corresponding Author E-mail:amar4goud@gmail.com

DOI : http://dx.doi.org/10.12944/CARJ.13.3.4

Article Publishing History

Received: 01 Sep 2025
Accepted: 11 Dec 2025
Published Online: 22 Dec 2025

Review Details

Reviewed by: Dr. Mohammad Usman
Second Review by: Dr. Ashish Kumar Gupta
Final Approval by: Dr. Surendra Singh Bargali

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Abstract:

The agricultural sector is rapidly evolving through digital technologies, creating significant opportunities to apply Artificial Intelligence (AI) for improving crop productivity, reducing losses, and optimizing resource utilization. This review specifically examines two key challenges in modern agriculture: the timely and accurate detection of crop diseases and the generation of precise fertilizer recommendations. We present a structured analysis of recent deep learning advancements, focusing on computer vision techniques such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Generative Adversarial Networks (GANs) for image-based disease diagnosis, as well as NLP and knowledge-graph approaches for integrating agronomic information. Additionally, we evaluate data-driven fertilizer recommendation frameworks that incorporate soil characteristics, climatic factors, and crop growth patterns using hybrid deep learning and ensemble models. The review also explores the role of multimodal learning, IoT-based sensing, and cloud–edge computing in enabling real-time agricultural decision-making. Finally, we highlight current limitations—including dataset scarcity, generalization issues, explainability gaps, and scalability concerns—and outline future research directions for building intelligent, interpretable, and adaptive AI systems for sustainable agriculture.

Keywords:

AI in Agriculture; Computer Vision; Crop Disease Detection; Deep Learning; Fertilizer Recommendation; IoT-enabled Agriculture; Multimodal Learning; Precision Agriculture; Smart Farming; Vision Transformers

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Copy the following to cite this article:

Goud E. A, Rathikarani V. A Review on Deep Learning-Based Crop Disease Detection and Fertilizer Recommendation Systems for Smart Agriculture. Curr Agri Res 2025; 13(3).. doi : http://dx.doi.org/10.12944/CARJ.13.3.4

Copy the following to cite this URL:

Goud E. A, Rathikarani V. A Review on Deep Learning-Based Crop Disease Detection and Fertilizer Recommendation Systems for Smart Agriculture. Curr Agri Res 2025; 13(3). Available from: https://bit.ly/44IRM6K

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