Work

A Comprehensive Review of Deep Learning in Agricultural Visual Perception: Progress, Bottlenecks, and Emerging Trends

Year: 2026

Type: article

Abstract: This paper provides a comprehensive review of deep learning in agricultural visual perception. Climate change and land degradation demand a shift from experience-driven to data-driven intelligent agriculture. Traditional manual inspections remain subjective and unscalable. Computer vision offers a non-invasive solution for precision crop and livestock management, while challenges also exist. Its application in actual agricultural scenarios faces specific challenges such as severe occlusion, drastic changes in lighting, and non-rigid deformation of biological targets. To systematically summarize how these perception bottlenecks are being resolved, this review explores deep learning architectures featuring spatial extraction and spatio-temporal modeling. This comprehensive review first constructs a progressive analytical framework from low-level data augmentation and static spatial cognition to high-level dynamic spatio-temporal reasoning, and then systematically classifies existing literature. In-depth analysis reveals that these three core challenges can be computationally resolved using three unified technologies. Domain drift and label scarcity, rather than baseline accuracy, are the main obstacles to practical deployment. We further identified four priority research directions, i.e., architectural efficiency, data-level annotation efficiency, deployment-level privacy and simulation, and trust-level security, to guide future research.

Source: Agriculture

DOI: 10.3390/agriculture16171826

Authors: Chen Chen, Runlin Liu, Leijun Xu

Institution: Jiangsu University

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,137