A Systematic Review of the Use of Deep Learning in Satellite Imagery for Agriculture

Agricultural research is essential for increasing food production to meet the needs of a rapidly growing human population. Collecting large quantities of agricultural data helps to improve decision making for better food security at various levels: from international trade and policy decisions, down to individual farmers. At the same time, deep learning has seen a wave of popularity across many different research areas and data modalities. And satellite imagery has become available in unprecedented quantities, driving much research from the wider remote sensing community. The data hungry nature of deep learning models and this huge data volume seem like a perfect match. But has deep learning been adopted for agricultural tasks using satellite images? This systematic review of 193 studies analyses the tasks that have reaped benefits from deep learning algorithms, and those that have not. It was found that while Land Use / Land Cover research has embraced deep learning algorithms, research on other agricultural tasks has not. This poor adoption appears to be due to a critical lack of labelled datasets for these other tasks. Thus, we give suggestions for collecting larger datasets. Additionally, satellite images differ from ground-based images in a number of ways, resulting in a proliferation of interesting data interpretations unique to satellite images. So, this review also introduces a taxonomy of data input shapes and how they are interpreted in order to facilitate easier communication of algorithm types and enable quantitative analysis.