# Deep learning in agriculture: A survey

arXiv: https://arxiv.org/abs/1807.11809 · HTML: https://mlsanity.com/paper/1807.11809/

## Key facts

- Cited by 160 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2018.

## Built on by (most cited first, 30 of 160)

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- [Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2105.07789/) (2021)
- [A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks](https://mlsanity.com/paper/2206.06544/) (2022)
- [GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models](https://mlsanity.com/paper/2310.06225/) (2023)
- [A Robust Illumination-Invariant Camera System for Agricultural Applications](https://mlsanity.com/paper/2101.02190/) (2021)
- [Context-self contrastive pretraining for crop type semantic segmentation](https://mlsanity.com/paper/2104.04310/) (2021)
- [Position-Agnostic Autonomous Navigation in Vineyards with Deep Reinforcement Learning](https://mlsanity.com/paper/2206.14155/) (2022)
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- [GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive Recognition of Cereal Grains](https://mlsanity.com/paper/2203.05306/) (2022)
- [Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review](https://mlsanity.com/paper/2303.02632/) (2023)
- [AgroLLM: Connecting Farmers and Agricultural Practices through Large Language Models for Enhanced Knowledge Transfer and Practical Application](https://mlsanity.com/paper/2503.04788/) (2025)
- [Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection](https://mlsanity.com/paper/2106.07770/) (2021)
- [Virtual Temporal Samples for Recurrent Neural Networks: applied to semantic segmentation in agriculture](https://mlsanity.com/paper/2106.10118/) (2021)
- [Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs](https://mlsanity.com/paper/2210.13648/) (2022)
- [Geometry-Aware Fruit Grasping Estimation for Robotic Harvesting in Orchards](https://mlsanity.com/paper/2112.04363/) (2021)
- [Monitoring crop phenology with street-level imagery using computer vision](https://mlsanity.com/paper/2112.09190/) (2021)
- [Spatial Monitoring and Insect Behavioural Analysis Using Computer Vision for Precision Pollination](https://mlsanity.com/paper/2205.04675/) (2022)
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## Abstract (arXiv)

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.