Deep learning in agriculture: A survey

TL;DR

This survey reviews 40 research efforts applying deep learning to agriculture, highlighting its high accuracy and potential to improve image processing and data analysis in food production.

Contribution

It systematically analyzes the application of deep learning in agriculture, comparing models, data sources, and performance, and highlights its advantages over traditional techniques.

Findings

Deep learning achieves high accuracy in agricultural tasks.

It outperforms traditional image processing techniques.

Deep learning shows large potential for future agricultural applications.

Abstract

Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.