Applications of Image Recognition in Intelligent Agricultural Engineering: A Comprehensive Review

Abstract

Confronted with the severe imperatives to food security posed by a growing population and the urgent need for sustainable development amid climate change, traditional agricultural models face significant resource-intensive efficiency bottlenecks. Deep learning-based image recognition is driving a future-oriented intelligent agricultural revolution by enabling high-throughput phenotyping and autonomous decision-making across the production chain. This paper systematically reviews key advancements in image recognition within modern agriculture, mapping the fundamental paradigm shift from traditional hand-crafted feature engineering to adaptive deep feature learning. We critically analyze technological implementation and performance across five core application scenarios: high-precision pest and disease diagnosis, spatio-temporal growth monitoring and yield prediction through multi-source image fusion, agricultural robots for automated harvesting, non-destructive quality inspection of products, and intelligent precision management of farmland. The review further identifies critical challenges hindering large-scale technology adoption, primarily centered on the high costs of constructing high-quality agricultural datasets and model robustness in complex field environments. Consequently, this study provides a comprehensive and forward-looking reference for advancing the deep integration of vision technology, thereby offering a strategic path toward achieving more intelligent, efficient, and sustainable global agricultural production systems in the digital era.

Plant phenotyping relevance

画像認識による高スループット表現型解析を含む農業画像技術を、実装・性能・課題の観点から体系的にレビューしており、植物フェノタイピング手法のレビューが中心です。

Code and data availability

The supplied blocks are from a review article on image recognition in agriculture. No public phenotype/trait datasets, plant images, author analysis code, or trained models specific to this paper are described with availability statements or URLs. The only supplementary material mentioned (Supplementary Table S1, a 'a'

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