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

arXiv: https://arxiv.org/abs/2210.01272 · HTML: https://mlsanity.com/paper/2210.01272/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 256 references that are in the map.
- First posted on arXiv 3 Oct 2022.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Are we done with ImageNet?](https://mlsanity.com/paper/2006.07159/) (2020)
- [Self-attention for raw optical Satellite Time Series Classification](https://mlsanity.com/paper/1910.10536/) (2019)
- [Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention](https://mlsanity.com/paper/1911.07757/) (2019)
- [Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders](https://mlsanity.com/paper/1802.02080/) (2018)
- [Multi-Modal Temporal Attention Models for Crop Mapping from Satellite Time Series](https://mlsanity.com/paper/2112.07558/) (2021)
- [Crop mapping from image time series: deep learning with multi-scale label hierarchies](https://mlsanity.com/paper/2102.08820/) (2021)
- [BreizhCrops: A Time Series Dataset for Crop Type Mapping](https://mlsanity.com/paper/1905.11893/) (2019)
- [Deep learning on edge: extracting field boundaries from satellite images with a convolutional neural network](https://mlsanity.com/paper/1910.12023/) (2019)
- [TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation](https://mlsanity.com/paper/2111.02682/) (2021)
- [A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning](https://mlsanity.com/paper/2204.00951/) (2022)
- [Domain-Adversarial Training of Self-Attention Based Networks for Land Cover Classification using Multi-temporal Sentinel-2 Satellite Imagery](https://mlsanity.com/paper/2104.00564/) (2021)
- [Simultaneous Corn and Soybean Yield Prediction from Remote Sensing Data Using Deep Transfer Learning](https://mlsanity.com/paper/2012.03129/) (2020)
- [DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn](https://mlsanity.com/paper/1809.07589/) (2018)
- [Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations](https://mlsanity.com/paper/2012.02542/) (2020)
- [Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)](https://mlsanity.com/paper/2004.12880/) (2020)
- [Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations](https://mlsanity.com/paper/2012.12101/) (2020)
- [Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series](https://mlsanity.com/paper/2110.08187/) (2021)
- [A Deep Learning Approach to Mapping Irrigation: IrrMapper-U-Net](https://mlsanity.com/paper/2103.03278/) (2021)

## Built on by (most cited first, 4 of 4)

- [Sugar-Beet Stress Detection using Satellite Image Time Series](https://mlsanity.com/paper/2507.13514/) (2025)
- [A High-Level Survey of Optical Remote Sensing](https://mlsanity.com/paper/2602.17397/) (2026)
- [Efficient Spatio-Temporal Vegetation Pixel Classification with Vision Transformers](https://mlsanity.com/paper/2605.00296/) (2026)
- [Early Yield Prediction for Sugar Beet Fields using Satellite Data -- Learnings from Specialized Vision Transformers](https://mlsanity.com/paper/2607.17661/) (2026)

## Abstract (arXiv)

Agricultural research is essential for increasing food production to meet the requirements of an increasing population in the coming decades. Recently, satellite technology has been improving rapidly and deep learning has seen much success in generic computer vision tasks and many application areas which presents an important opportunity to improve analysis of agricultural land. Here we present a systematic review of 150 studies to find the current uses of deep learning on satellite imagery for agricultural research. Although we identify 5 categories of agricultural monitoring tasks, the majority of the research interest is in crop segmentation and yield prediction. We found that, when used, modern deep learning methods consistently outperformed traditional machine learning across most tasks; the only exception was that Long Short-Term Memory (LSTM) Recurrent Neural Networks did not consistently outperform Random Forests (RF) for yield prediction. The reviewed studies have largely adopted methodologies from generic computer vision, except for one major omission: benchmark datasets are not utilised to evaluate models across studies, making it difficult to compare results. Additionally, some studies have specifically utilised the extra spectral resolution available in satellite imagery, but other divergent properties of satellite images - such as the hugely different scales of spatial patterns - are not being taken advantage of in the reviewed studies.