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Agricultural research is essential for increasing food production to meet therequirements of an increasing population in the coming decades. Recently,satellite technology has been improving rapidly and deep learning has seen muchsuccess in generic computer vision tasks and many application areas whichpresents an important opportunity to improve analysis of agricultural land.Here we present a systematic review of 150 studies to find the current uses ofdeep learning on satellite imagery for agricultural research. Although weidentify 5 categories of agricultural monitoring tasks, the majority of theresearch interest is in crop segmentation and yield prediction. We found that,when used, modern deep learning methods consistently outperformed traditionalmachine learning across most tasks; the only exception was that Long Short-TermMemory (LSTM) Recurrent Neural Networks did not consistently outperform RandomForests (RF) for yield prediction. The reviewed studies have largely adoptedmethodologies from generic computer vision, except for one major omission:benchmark datasets are not utilised to evaluate models across studies, makingit difficult to compare results. Additionally, some studies have specificallyutilised the extra spectral resolution available in satellite imagery, butother divergent properties of satellite images - such as the hugely differentscales of spatial patterns - are not being taken advantage of in the reviewedstudies.

A systematic review of the use of Deep Learning in Satellite Imagery for Agriculture