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

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.

Plant phenotyping relevance

作物病害を画像から検出する深層学習手法をレビューしており、植物の病徴・病害状態を推定するフェノタイピング手法が主要対象です。肥料推薦も扱いますが、病害検出の方法論的レビューとして適格です。

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