AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock

TL;DR

This survey comprehensively reviews over 200 AI research works in agriculture, highlighting deep learning applications for crop, fisheries, and livestock management, and discusses future research directions.

Contribution

It provides a systematic overview of recent deep learning techniques and challenges in agricultural AI, including vision transformers and vision-language models.

Findings

Deep learning improves crop disease detection accuracy.

Vision transformers and CLIP are emerging tools in agriculture.

Challenges include data variability and deployment on edge devices.

Abstract

Crops, fisheries and livestock form the backbone of global food production, essential to feed the ever-growing global population. However, these sectors face considerable challenges, including climate variability, resource limitations, and the need for sustainable management. Addressing these issues requires efficient, accurate, and scalable technological solutions, highlighting the importance of artificial intelligence (AI). This survey presents a systematic and thorough review of more than 200 research works covering conventional machine learning approaches, advanced deep learning techniques (e.g., vision transformers), and recent vision-language foundation models (e.g., CLIP) in the agriculture domain, focusing on diverse tasks such as crop disease detection, livestock health management, and aquatic species monitoring. We further cover major implementation challenges such as data variability and experimental aspects: datasets, performance evaluation metrics, and geographical focus. We finish the survey by discussing potential open research directions emphasizing the need for multimodal data integration, efficient edge-device deployment, and domain-adaptable AI models for diverse farming environments. Rapid growth of evolving developments in this field can be actively tracked on our project page: https://github.com/umair1221/AI-in-Agriculture