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Agrifood-Survey

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The official repo for [ACM CSUR'24] "Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities"

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<h1>Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities</h1>

Tao Chen<sup>1 ∗</sup>, Liang Lv<sup>1 ∗</sup>, Di Wang<sup>2 ∗</sup>, Jing Zhang<sup>23 †</sup>, Yue Yang<sup>1 </sup>, Zeyang Zhao<sup>1 </sup>, Chen Wang<sup>1 </sup>, Xiaowei Guo<sup>1 </sup>, Hao Chen<sup>1 </sup>, Qingye Wang<sup>1 </sup>, Yufei Xu<sup>3 </sup>, Qiming Zhang<sup>3 </sup>, Bo Du<sup>2 †</sup>, Liangpei Zhang<sup>2 </sup>, Dacheng Tao<sup>4 </sup>

<sup>1</sup> China University of Geosciences, <sup>2</sup> Wuhan University,  <sup>3</sup> The University of Sydney,  <sup>4</sup> Nanyang Technological University

<sup>∗</sup> Equal contribution, <sup>†</sup> Corresponding author

</div>

<p align="center">
  <a href="#update">Update</a> |
  <a href="#introduction">Introduction</a> |
  <a href="#architectures">Mindmaps</a> |
  <a href="#ai-methods-for-agrifood-system">Methods</a> |
  <a href="#reference">Reference</a>
</p >

# Update

**2024.09.26** The survey is accepted by ***ACM Computing Surveys***, and the arXiv is updated!

**2023.05.03** The manuscript is post on arXiv!

# Introduction

Artificial intelligence (AI) techniques such as deep learning (DL) have demonstrated their strong abilities in various areas, including language, vision, remote sensing (RS), and agrifood systems applications. In this study, we thoroughly review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry. Firstly, we summarize the data acquisition methods in agrifood systems, including acquisition, storage, and processing techniques. Secondly, we present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery, covering topics such as agrifood classification, growth monitoring, yield prediction, and quality assessment. We hope this survey could reveal the immense potential of AI in agrifood systems, stimulate discussions on the reasonable use of AI technologies in agriculture, and inspire further research and practical implementation of AI in agriculture, with the goal of enhancing the productivity, efficiency, safety, and sustainability of our agrifood systems.

<table>
<tr>
<td width="575">
  <img src=Figs/survey_relationships.png>
<br> 
<figcaption align = "left"><b>Fig.1 - The relationship between this (white lines) and other surveys, which are shown as black dots. </b></figcaption>
</td>
<td width="367">
  <img src=Figs/method_applications.png>
<br> 
<figcaption align = "left"><b>Fig.2 - Different AI methods are suitable to various agrifood applications. </b></figcaption> </td>
</tr>
</table>

The deployment of AI technology in agrifood systems has also activated many opportunities, where cutting-edge exploration experiences in AI fields, such as multimodal and scalable models can be leveraged by agriculture applications, improving software and hardware infrastructures simultaneously.

<figure>
<img align="center" src=Figs/agri_ar_text_shopping.png width="100%">
<figcaption align = "center"><b>Fig.3 - AI models enable reading agrifood product information. The results are obtained by <a href="https://github.com/ViTAE-Transformer/DeepSolo">DeepSolo</a> with <a href="https://github.com/ViTAE-Transformer/ViTAE-Transformer">ViTAEv2</a>. </b>
</figcaption>
</figure>

<br>

# Architectures

[Data Source, Storage and Preprocessing](https://Frenkie14.github.io/Agrifood-survey-mindmap/mindmap_dataset.html)

[Applications for Data and Method Selection](https://Frenkie14.github.io/Agrifood-survey-mindmap/mindmap_application.html)

# AI Methods for Agrifood System

## Agrifood Classification
<table>
    <thead>
        <tr>
            <th>Year</th>
            <th>Input</th>
            <th>Methods</th>
            <th>Paper Title</th>
            <th>Pub.</th>
        </tr>
    </thead>
    <tbody align='center'>        
        <tr>
            <td>2014</td>
            <td>ASTER</td>
            <td>SVM+SVM</td>
            <td><a href="https://doi.org/10.3390/rs6065019">Object-based image classification of summer crops with machine learning methods</a></td>
            <td>Remote Sensing</td>
        </tr>
        <tr>
            <td>2014</td>
            <td>Hyperion sensor</td>
            <td>ELM, OP-ELM, BayesNet, SVM, 1-NN, C4.5</td>
            <td><a href="https://doi.org/10.1016/j.neucom.2013.03.057">Extreme learning machines for soybean classification in remote sensing hyperspectral images</a></td>
            <td>Neurocomputing</td>
        </tr> 
        <tr>
            <td>2017</td>
            <td>Landsat 8, Sentinel-1</td>
            <td>MLP, ENN, 1D-CNNs, 2D-CNNs</td>
            <td><a href="https://doi.org/10.1109/LGRS.2017.2681128">Deep learning classification of land cover and crop types using remote sensing data</a></td>
            <td>IEEE Geoscience And Remote Sensing Letters</td>
        </tr> 
        <tr>
            <td>2017</td>
            <td>MODIS, Landsat TM, Landsat ETM+</td>
            <td>Improved algorithm to identify flooding and transplanting of paddy rice fields</td>
            <td><a href="https://doi.org/10.1016/j.scitotenv.2016.10.223">Spatiotemporal patterns of paddy rice croplands in China and India from 2000 to 2015</a></td>
            <td>Science of the Total Environment</td>
        </tr> 
        <tr>
            <td>2018</td>
            <td>Onsite devices, AVIRIS</td>
            <td>ND-SVM, RoSVM, RF</td>
            <td><a href="https://doi.org/10.1109/JSTARS.2017.2788426">Feature-ensemble-based novelty detection for analyzing plant hyperspectral datasets</a></td>
            <td>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</td>
        </tr>
        <tr>
            <td>2018</td>
            <td>Sentinel-1, Sentinel-2</td>
            <td>Hierarach RF</td>
            <td><a href="https://doi.org/10.3390/rs10101642">Synergistic use of radar Sentinel-1 and optical Sentinel-2 imagery for crop mapping: A case study for Belgium</a></td>
            <td>Remote Sensing</td>
        </tr>
        <tr>
            <td>2019</td>
            <td>Sentinel-1</td>
            <td>1D CNNs, LSTM RNNs, GRU RNNs, RF</td>
            <td><a href="https://doi.org/10.3390/rs11222673">Evaluation of three deep learning models for early crop classification using sentinel-1A imagery time series 
              — A case study in Zhanjiang, China</a></td>
            <td>Remote sensing</td>
        </tr>
        <tr>
            <td>2019</td>
            <td>Sentinel-2</td>
            <td>Polynomial-SVM, RBF-SVM, RF, ANN, CART-DT</td>
            <td><a href="https://doi.org/10.1016/j.landusepol.2019.104190">Mapping sugar cane in complex land scapes by integrating multi-temporal Sentinel-2 images and 
              machine learning algorithms</a></td>
            <td>Land Use Policy</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>Landsat Analysis Ready Data</td>
            <td>DCM, Transformer, MLP, RF</td>
            <td><a href="https://doi.org/10.1016/j.rse.2020.111946">Deep Crop Mapping: A multi-temporal deep learning approach with improved spatial generalizability for 
              dynamic corn and soybean mapping</a></td>
            <td>Remote Sensing of Environment</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>Onsite devices, RADARSAT-2, VENµS</td>
            <td>Conv1D, MLP, LSTM, XGBoost, RF, SVM</td>
            <td><a href="https://doi.org/10.3390/rs12050832">Synergistic use of multi-temporal RADARSAT-2 and VEN𝜇S data for crop classification based on 1D convolutional neural network</a></td>
            <td>Remote Sensing</td>
        </tr>
        <tr>
            <td>2021</td>
            <td>Zhuhai-1, Sentinel-2, Sentinel-1</td>
            <td>DOCC</td>
            <td><a href="https://doi.org/10.1016/j.jag.2021.102598">DOCC: Deep one-class crop classification via positive and unlabeled learning for multi-modal satellite imagery</td>
            <td>International Journal of Applied Earth Observations and Geoinformation</a></td>
        </tr>
        <tr>
            <td>2021</td>
            <td>UAV</td>
            <td>DeeplabV3+, PSPNet, SegNet, U-Net</td>
            <td><a href="https://doi.org/10.1155/2021/6687799">Depth semantic segmentation of tobacco planting areas from unmanned aerial vehicle remote sensing images 
              in plateau mountains</a></td>
            <td>Journal of Spectroscopy</td>
        </tr>
        <tr>
            <td>2022</td>
            <td>Landsat 8, Sentinel-2, Onsite devices</td>
            <td>KNN, RF, SVM, GBDT</td>
            <td><a href="https://doi.org/10.1109/ACCESS.2022.3148691">Remote Sensing and Machine Learning Modeling to Support the Identification of Sugarcane Crops</a></td>
            <td>IEEE Access</td>
        </tr>
        <tr>
            <td>2022</td>
            <td>UAV</td>
            <td>ViT B-16, ViT B-32, EfficientNet B0, EfficientNet B1, ResNet 50</td>
            <td><a href="https://doi.org/10.3390/rs14030592">Transformer neural network for weed and crop classification of high resolution UAV images</a></td>
            <td>Remote sensing</td>
        </tr>
        <tr>
            <td>2022</td>
            <td>UAV</td>
            <td>HSI-transunet, SegNet, SETR, UNet, TransUNet</td>
            <td><a href="https://doi.org/10.1016/j.compag.2022.107297">HSI-TransUNet: a transformer based semantic segmentation model for crop mapping from 
              UAV hyperspectral imagery</a></td>
            <td>Computers and Electronics in Agriculture </td>
        </tr>
    </tbody>
</table>

## Agrifood Growth Monitoring
<table>
    <thead>
        <tr>
            <th>Year</th>
            <th>Input</th>
            <th>Methods</th>
            <th>Paper Title</th>
            <th>Pub.</th>
        </tr>
    </thead>
    <tbody align='center'>        
          <tr>
            <td>2012</td>
            <td>Manned aircraft</td>
            <td>LME regression, RF, SVR, Cu</td>
            <td><a href="https://doi.org/10.1016/j.rse.2012.07.006">Forest biomass estimation from airborne LiDAR data using machine learning approaches</a></td>
            <td>Remote Sensing of Environment</td>
        </tr>
        <tr>
            <td>2012</td>
            <td>Laboratory conditions</td>
            <td>MLR, PLSR</td>
            <td><a href="https://doi.org/10.1016/j.compag.2012.03.006">Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements</a></td>
            <td>Computers and Electronics in Agriculture</td>
        </tr>
        <tr>
            <td>2013</td>
            <td>Onsite devices</td>
            <td>DDA</td>
            <td><a href="https://doi.org/10.1002/wrcr.20495">Development of a deterministic downscaling algorithm for remote sensing soil moisture 
              footprint using soil and vegetation classifications</a></td>
            <td>Water Resources Research</td>
        </tr>
        <tr>
            <td>2014</td>
            <td>UAV</td>
            <td>PROSAIL</td>
            <td><a href="https://doi.org/10.1016/j.jag.2013.05.007">Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data</a></td>
            <td>International Journal of Applied Earth Observation and Geoinformation</td>
        </tr>
        <tr>
            <td>2015</td>
            <td>UAV</td>
            <td>K-means, Rk-means, SSSVM, KNN, LINSVM, SVM</td>
            <td><a href="https://doi.org/10.1016/j.asoc.2015.08.027">A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method</a></td>
            <td>Applied Soft Computing</td>
        </tr>
        <tr>
            <td>2016</td>
            <td>HJ satellite</td>
            <td>SVR, ANN, RF</td>
            <td><a href="https://doi.org/10.1016/j.cj.2016.01.008">Estimation of biomass in wheat using random forest regression algorithm and remote sensing data</a></td>
            <td>The Crop Journal</td>
        </tr>
        <tr>
            <td>2017</td>
            <td>Sentinel-2, Landsat-8</td>
            <td>OPTRAM, TOTRAM</td>
            <td><a href="https://doi.org/10.1016/j.rse.2017.05.041">The optical trapezoid model: A novel approach to remote sensing of soil moisture 
              applied to Sentinel-2 and Landsat-8 observations</a></td>
            <td>Remote Sensing of Environment</td>
        </tr>
        <tr>
            <td>2017</td>
            <td>Onsite devices</td>
            <td>SDA, FDA, kNN</td>
            <td><a href="https://doi.org/10.1016/j.compag.2017.01.017">Field detection of anthracnose crown rot in strawberry using spectroscopy technology</a></td>
            <td>Computers and Electronics in Agriculture</td>
        </tr>
        <tr>
            <td>2018</td>
            <td>Onsite devices</td>
            <td>SVR, MLR</td>
            <td><a href="https://doi.org/10.1016/j.compag.2018.10.009">Evaluation of citrus gummosis disease dynamics and predictions with weather and inversion based leaf optical model</a></td>
            <td>Computers and Electronics in Agriculture</td>
        </tr>
        <tr>
            <td>2018</td>
            <td>Onsite devices</td>
            <td>ND-SVM, RoSVM</td>
            <td><a href="https://doi.org/10.1109/JSTARS.2017.2788426">Feature-ensemble-based novelty detection for analyzing plant hyperspectral datasets</a></td>
            <td>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</td>
        </tr>
        <tr>
            <td>2018</td>
            <td>Manned aircraft</td>
            <td>LM, RF, NN, SVMR, SVML, GBM, CU</td>
            <td><a href="https://doi.org/10.1016/j.compag.2018.07.016">Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield</a></td>
            <td>Computers and Electronics in Agriculture</td>
        </tr>
        <tr>
            <td>2019</td>
            <td>UAV, Onsite devices</td>
            <td>ANN, SVM, RF</td>
            <td><a href="https://doi.org/10.1016/j.isprsjprs.2018.11.015">Modeling alpine grassland forage phosphorus based on hyperspectral remote sensing
              and a multi-factor machine learning algorithm in the east of Tibetan Plateau,China</a></td>
            <td>ISPRS Journal of Photogrammetry and Remote Sensing</td>
        </tr>
        <tr>
            <td>2019</td>
            <td>Landsat-8</td>
            <td>SLR, PLSR, SVM, ANN, OK</td>
            <td><a href="https://doi.org/10.3390/rs11141683">Prediction of soil organic carbon based on Landsat 8 monthly NDVI data for the Jianghan Plain in Hubei Province, China</a></td>
            <td>Remote Sensing</td>
        </tr>
        <tr>
            <td>2019</td>
            <td>UAV</td>
            <td>MLR, SVM, ANN, RF</td>
            <td><a href="https://doi.org/10.1186/s13007-019-0394-z">Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data</a></td>
            <td>Plant Methods</td>
        </tr>
        <tr>
            <td>2019</td>
            <td>UAV</td>
            <td>OLS, SVM, BP, RF</td>
            <td><a href="https://doi.org/10.3390/rs11222678">Estimating maize above-ground biomass using 3D point clouds of multi-source unmanned aerial vehicle data at multi-spatial scales</a></td>
            <td>Remote Sensing</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>Onsite devices</td>
            <td>HOG, SURF, GLCM, ANN, SVM</td>
            <td><a href="https://doi.org/10.1016/j.biosystemseng.2020.03.016">Effect of directional augmentation using supervised machine learning technologies: A case study of strawberry powdery mildew detection</a></td>
            <td>Biosystems Engineering</td>
        </tr>
        <tr>
            <td>2021</td>
            <td>UAV</td>
            <td>NN, GLM, GBM, DRF</td>
            <td><a href="https://doi.org/10.1016/j.rse.2021.112434">Estimation of root zone soil moisture from ground and remotely sensed soil information 
              with multi sensor data fusion and automated machine learning</a></td>
            <td>Remote Sensing of Environment</td>
        </tr>
        <tr>
            <td>2021</td>
            <td>RADARSAT-2</td>
            <td>PCC, SVM-RFE, RF, SVR, GBRT</td>
            <td><a href="https://doi.org/10.1109/JSTARS.2021.3067890">Estimating soil moisture over winter wheat fields during growing season using machine-learning methods</a></td>
            <td>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</td>
        </tr>
        <tr>
            <td>2021</td>
            <td>UAV</td>
            <td>PLSR, ANN, RF, SVM</td>
            <td><a href="https://doi.org/10.3390/rs13152956">Estimation of paddy rice nitrogen content and accumulation both at leaf and plant levels from UAV hyperspectral imagery</a></td>
            <td>Remote Sensing</td>
        </tr>
        <tr>
            <td>2021</td>
            <td>SMAPVEX-12</td>
            <td>SVR, RF, GBDT, XGBoost</td>
            <td><a href="https://doi.org/10.3390/agronomy11071363">Deep learning-based estimation of crop biophysical parameters using multi-source and multi-temporal remote sensing observations</a></td>
            <td>Agronomy</td>
        </tr>
        <tr>
            <td>2021</td>
            <td>Onsite devices</td>
            <td>LDA, K-NN, SVM</td>
            <td><a href="https://doi.org/10.1016/j.rse.2021.112350">Spectroscopic detection of rice leaf blast infection from asymptomatic to mild stages with integrated machine learning and feature selection</a></td>
            <td>Remote Sensing of Environment</td>
        </tr>
        <tr>
            <td>2022</td>
            <td>Onsite devices</td>
            <td>RFR, SLR</td>
            <td><a href="https://doi.org/10.3389/fpls.2022.890892">Improving Estimation of Winter Wheat Nitrogen Status Using Random Forest by Integrating 
              Multi-Source Data Across Different Agro-Ecological Zones</a></td>
            <td>Frontiers in Plant Science</td>
        </tr>
        <tr>
            <td>2022</td>
            <td>UAV</td>
            <td>PLSR, kNN, RFR, BPNN</td>
            <td><a href="https://doi.org/10.1016/j.agwat.2022.107530">Estimation of soil moisture content under high maize canopy coverage from UAV multimodal 
              data and machine learning</a></td>
            <td>Agricultural Water Management</td>
        </tr>
        <tr>
            <td>2022</td>
            <td>UAV</td>
            <td>SVR, ELM, RF</td>
            <td><a href="https://doi.org/10.1007/s11119-022-09901-7">An assessment of multi-view spectral information from UAV-based color-infrared images for improved estimation of nitrogen nutrition status in winter wheat</a></td>
            <td>Precision Agriculture</td>
        </tr>
        <tr>
            <td>2022</td>
            <td>UAV</td>
            <td>CNN+BA, CNN+PSO, CNN+MAML</td>
            <td><a href="https://doi.org/10.3389/fpls.2021.735230">Weed density extraction based on few-shot learning through UAV remote sensing RGB and multispectral images in ecological irrigation area</a></td>
            <td>Frontiers in Plant Science</td>
        </tr>
        <tr>
            <td>2022</td>
            <td>UAV</td>
            <td>EfficientNet, ResNet, ViT</td>
            <td><a href="https://doi.org/10.3390/rs14030592">Transformer neural network for weed and crop classification of high resolution UAV images</a></td>
            <td>Remote Sensing</td>
        </tr>
    </tbody>
</table>

## Agrifood Yield Prediction
<table>
    <thead>
        <tr>
            <th>Year</th>
            <th>Input</th>
            <th>Methods</th>
            <th>Paper Title</th>
            <th>Pub.</th>
        </tr>
    </thead>
    <tbody align='center'>        
        <tr>
            <td>2013</td>
            <td>Onsite devices</td>
            <td>SMF</td>
            <td><a href="https://doi.org/10.1016/j.rse.2012.12.017">MODIS-based corn grain yield estimation model incorporating crop phenology information</a></td>
            <td>Remote Sensing of Environment</td>
        </tr>
        <tr>
            <td>2016</td>
            <td>AVHRR, MODIS</td>
            <td>MLR, BNN, MOB</td>
            <td><a href="https://doi.org/10.1016/j.agrformet.2015.11.003">Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods</a></td>
            <td>Agricultural and Forest Meteorology</td>
        </tr>
        <tr>
            <td>2019</td>
            <td>USDA-NASS, SMAP, MODIS</td>
            <td>PLR, KRR</td>
            <td><a href="https://doi.org/10.1016/j.rse.2019.111460">Synergistic integration of optical and microwave satellite data for crop yield estimation</a></td>
            <td>Remote Sensing of Environment</td>
        </tr>
        <tr>
            <td>2019</td>
            <td>Onsite devices</td>
            <td>LR, SVR, ANN, RF, SGB</td>
            <td><a href="https://doi.org/10.3389/fpls.2019.00809">California almond yield prediction at the orchard level with a machine learning approach</a></td>
            <td>Frontiers in Plant Science</td>
        </tr>
        <tr>
            <td>2019</td>
            <td>Onsite devices, NASA LPDAAC, BMO</td>
            <td>STC</td>
            <td><a href="https://doi.org/10.1007/s11119-018-09628-4">An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning</a></td>
            <td>Precision Agriculture</td>
        </tr>
        <tr>
            <td>2019</td>
            <td>Onsite devices, Sentinel-2</td>
            <td>MR, RF, SVM</td>
            <td><a href="https://doi.org/10.3390/rs11232873">Monitoring within-field variability of corn yield using Sentinel-2 and machine learning techniques</a></td>
            <td>Remote Sensing</td>
        </tr>
        <tr>
            <td>2019</td>
            <td>MODIS</td>
            <td>LASSO, RF, XGBoost, LSTM</td>
            <td><a href="https://doi.org/10.3390/rs12010021">Combining optical, fluorescence, thermal satellite, and environmental data to predict county-level maize yield in China using machine learning approaches</a></td>
            <td>Remote Sensing</td>
        </tr>
        <tr>
            <td>2019</td>
            <td>UAV</td>
            <td>CNN</td>
            <td><a href="https://doi.org/10.1016/j.compag.2019.104859">Crop yield prediction with deep convolutional neural networks</a></td>
            <td>Computers and Electronics in Agriculture</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>UAV</td>
            <td>PLSR, RFR, SVM, DNN-F1, DNN-F2</td>
            <td><a href="https://doi.org/10.1016/j.rse.2019.111599">Soybean yield prediction from UAV using multimodal data fusion and deep learning</a></td>
            <td>Remote Sensing of Environment</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>USDA NASA, MODIS, PRISM</td>
            <td>OLS, LASSO, SVM, RF, AdaBoost, DNN</td>
            <td><a href="https://doi.org/10.3390/rs12081232">Combining multi-source data and machine learning approaches to predict winter wheat yield in the conterminous United States</a></td>
            <td>Remote Sensing</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>Onsite devices, UAV</td>
            <td>LR, MLR, SMLR, PLSR, ANN, RF</td>
            <td><a href="https://doi.org/10.3390/rs12030508">Wheat growth monitoring and yield estimation based on multi-rotor unmanned aerial vehicle</a></td>
            <td>Remote Sensing</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>MODIS, Onsite devices, SILO</td>
            <td>RF, CU, XB, SVMl, SVMr, MLP, MARS, GP, kNN</td>
            <td><a href="https://doi.org/10.1016/j.isprsjprs.2019.11.008">Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods</a></td>
            <td>ISPRS Journal of Photogrammetry and Remote Sensing</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>MOD13Q1</td>
            <td>GPR, SVM, RF</td>
            <td><a href="https://doi.org/10.3390/rs12020236">Prediction of winter wheat yield based on multi-source data and machine learning in China</a></td>
            <td>Remote Sensing</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>MODIS</td>
            <td>OLS, RF, LSTM</td>
            <td><a href="https://doi.org/10.1016/j.agrformet.2019.107886">Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil</a></td>
            <td>Agricultural and Forest Meteorology</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>UAV</td>
            <td>Pretrained CNN, CNN-LSTM, ConvLSTM, 3D-CNN</td>
            <td><a href="https://doi.org/10.3390/rs12234000">Crop yield prediction using multitemporal UAV data and spatio-temporal deep learning models</a></td>
            <td>Remote Sensing</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>AVHRR, SoilGrids</td>
            <td>LSTM-CNN, RF, SVM, LR</td>
            <td><a href="https://doi.org/10.3390/rs12111744">Winter wheat yield prediction at county level and uncertainty analysis in main wheat-producing regions of China with deep learning approaches</a></td>
            <td>Remote Sensing</td>
        </tr>
        <tr>
            <td>2021</td>
            <td>MODIS</td>
            <td>DT, RF, SVM, LSTM, 2D-CNN, SSTNN</td>
            <td><a href="https://doi.org/10.1016/j.jag.2021.102436">Crop yield prediction from multi-spectral, multi-temporal remotely sensed imagery using recurrent 3D convolutional neural networks</a></td>
            <td>International Journal of Applied Earth Observation and Geoinformation</td>
        </tr>
        <tr>
            <td>2021</td>
            <td>DSASI-MADR, ASAP</td>
            <td>LASSO, RF, MLP, SVR, GBR</td>
            <td><a href="https://doi.org/10.1016/j.agrformet.2021.108555">Yield forecasting with machine learning and small data: What gains for grains?</a></td>
            <td>Agricultural and Forest Meteorology</td>
        </tr>
        <tr>
            <td>2021</td>
            <td>Onsite devices, Landsat, MODIS, CMA</td>
            <td>LASSO, LightGBM, LSTM</td>
            <td><a href="https://doi.org/10.1016/j.agrformet.2021.108666">Integrating satellite-derived climatic and vegetation indices to predict smallholder maize yield using deep learning</a></td>
            <td>Agricultural and Forest Meteorology</td>
        </tr>
        <tr>
            <td>2021</td>
            <td>Sentinel-2, MODIS</td>
            <td>LIN, RID, SVR, GPR, CNN-2D, CNN-3D</td>
            <td><a href="https://doi.org/10.3390/rs13071391">Rice-yield prediction with multi-temporal sentinel-2 data and 3D CNN: A case study in Nepal</a></td>
            <td>Remote Sensing</td>
        </tr>
        <tr>
            <td>2021</td>
            <td>Onsite devices, UAV</td>
            <td>SPAD, CCC, LAI, AGB</td>
            <td><a href="https://doi.org/10.1016/j.fcr.2021.108148">Remote estimation of grain yield based on UAV data in different rice cultivars under contrasting climatic zone</a></td>
            <td>Field Crops Research</td>
        </tr>
        <tr>
            <td>2021</td>
            <td>MODIS</td>
            <td>DT, RF, SVM, LSTM, 2D-CNN, 3DMKGP</td>
            <td><a href="https://doi.org/10.1109/JSTARS.2021.3073149">Exploiting hierarchical features for crop yield prediction based on 3-d convolutional neural networks and multikernel gaussian process</a></td>
            <td>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</td>
        </tr>
        <tr>
            <td>2022</td>
            <td>MODIS, COMS MI, RDAPS, IRRI</td>
            <td>RSCM, FFNN, 1D-CNN, LSTM, 1D-CNN+LSTM, LSTM+1D-CNN</td>
            <td><a href="https://doi.org/10.1016/j.scitotenv.2021.149726">Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea</a></td>
            <td>Science of The Total Environment</td>
        </tr>
    </tbody>
</table>

## Agrifood Quality Assessment
<table>
    <thead>
        <tr>
            <th>Year</th>
            <th>Input</th>
            <th>Methods</th>
            <th>Paper Title</th>
            <th>Pub.</th>
        </tr>
    </thead>
    <tbody align='center'>        
        <tr>
            <td>2018</td>
            <td>Onsite devices</td>
            <td>SVM</td>
            <td><a href="https://doi.org/10.1109/IC3INA.2018.8629534">Comparison of features for strawberry grading classification with novel dataset</a></td>
            <td>IC3INA</td>
        </tr>
        <tr>
            <td>2018</td>
            <td>Onsite devices</td>
            <td>CNN, AlexNet, GoogLeNet, VGGNet, Xception, MobileNet</td>
            <td><a href="https://doi.org/10.14419/ijet.v7i4.40.24080">Evaluation of deep convolutional neural network architectures for strawberry quality inspection</a></td>
            <td>International Journal of Engineering & Technology</td>
        </tr>
    </tbody>
</table>

## Pasture Monitoring and Evaluation
<table>
    <thead>
        <tr>
            <th>Year</th>
            <th>Input</th>
            <th>Methods</th>
            <th>Paper Title</th>
            <th>Pub.</th>
        </tr>
    </thead>
    <tbody align='center'>        
        <tr>
            <td>2019</td>
            <td>Onsite devices, UAV</td>
            <td>ANN, SVM, RF</td>
            <td><a href="https://doi.org/10.1016/j.isprsjprs.2018.11.015">Modeling alpine grassland forage phosphorus based on hyperspectral remote sensing and a multi-factor machine learning algorithm 
              in the east of Tibetan Plateau, China</a></td>
            <td>ISPRS Journal of Photogrammetry and Remote Sensing</td>
        </tr>
        <tr>
            <td>2021</td>
            <td>UAV</td>
            <td>GAM, RF</td>
            <td><a href="https://doi.org/10.1016/j.compag.2020.105880">Predicting pasture biomass using a statistical model and machine learning algorithm implemented with remotely sensed imagery</a></td>
            <td>Computers and Electronics in Agriculture</td>
        </tr>
        <tr>
            <td>2021</td>
            <td>Sentinel-2, Onsite devices</td>
            <td>MLP</td>
            <td><a href="https://doi.org/10.3390/rs13040603">Estimating pasture biomass using sentinel-2 imagery and machine learning</a></td>
            <td>Remote Sensing</td>
        </tr>
        <tr>
            <td>2021</td>
            <td>Public datasets</td>
            <td>DeepPaSTL</td>
            <td><a href="https://doi.org/10.3390/agronomy11112245">DeepPaSTL: Spatio-temporal deep learning methods for predicting long-term pasture terrains using synthetic datasets</a></td>
            <td>Agronomy</td>
        </tr>
        <tr>
            <td>2022</td>
            <td>Onsite devices</td>
            <td>PLS, RF</td>
            <td><a href="https://doi.org/10.1016/j.compag.2021.106614">Estimating pasture quality of Mediterranean grasslands using hyperspectral narrow bands from field spectroscopy by Random Forest and PLS regressions</a></td>
            <td>Computers and Electronics in Agriculture</td>
        </tr>
    </tbody>
</table>

## Animal Individual Monitoring
<table>
    <thead>
        <tr>
            <th>Year</th>
            <th>Input</th>
            <th>Methods</th>
            <th>Paper Title</th>
            <th>Pub.</th>
        </tr>
    </thead>
    <tbody align='center'>        
        <tr>
            <td>2015</td>
            <td>Laboratory conditions</td>
            <td>PNN</td>
            <td><a href="https://doi.org/10.1016/j.meatsci.2014.09.001">Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis</a></td>
            <td>Meat Science</td>
        </tr>
        <tr>
            <td>2016</td>
            <td>GeoEye, SWISSTOPO</td>
            <td>CNN</td>
            <td><a href="https://doi.org/10.1109/TGRS.2016.2545919">Detection of fragmented rectangular enclosures in very high resolution remote sensing images</a></td>
            <td>IEEE Transactions on Geoscience and Remote Sensing</td>
        </tr>
        <tr>
            <td>2018</td>
            <td>Onsite devices</td>
            <td>Fisherface, VGG-Face+SVM, CNN</td>
            <td><a href="https://doi.org/10.1016/j.compind.2018.02.016">Towards on-farm pig face recognition using convolutional neural networks</a></td>
            <td>Computers in Industry</td>
        </tr>
        <tr>
            <td>2018</td>
            <td>Onsite devices</td>
            <td>Fully automatic computer vision system</td>
            <td><a href="https://doi.org/10.1016/j.biosystemseng.2017.08.011">Implementation of an automatic 3D vision monitor for dairy cow locomotion in a commercial farm</a></td>
            <td>Biosystems Engineering</td>
        </tr>
        <tr>
            <td>2018</td>
            <td>Onsite devices</td>
            <td>FCN, SDS</td>
            <td><a href="https://doi.org/10.1016/j.biosystemseng.2018.10.005">High-accuracy image segmentation for lactating sows using a fully convolutional network</a></td>
            <td>Biosystems Engineering</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>Onsite devices</td>
            <td>Fast R-CNN</td>
            <td><a href="https://doi.org/10.1016/j.biosystemseng.2019.11.013">Automatic recognition of lactating sow postures by refined two-stream RGB-D faster R-CNN</a></td>
            <td>Biosystems Engineering</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>ImageNet, NADIS, Pixabay, Flickr, Gettyimages</td>
            <td>AlexNet, LeNet, VGG16, DenseNet-201, Inception-v3, ResNet-50, DarkNet</td>
            <td><a href="https://doi.org/10.1016/j.compag.2020.105528">Automated sheep facial expression classification using deep transfer learning</a></td>
            <td>Computers and Electronics in Agriculture</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>Onsite devices</td>
            <td>VGG-16</td>
            <td><a href="https://doi.org/10.1088/1742-6596/1453/1/012054">Cattle face recognition method based on parameter transfer and deep learning</a></td>
            <td>Journal of Physics: Conference Series</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>Onsite devices</td>
            <td>YOLOV3+LSTM, YOLOV3+BLSTM, YOLOV3+GRU, YOLOV3+Stacked LSTM, YOLOV3+SVM, YOLOV3+KNN, YOLOV3+DTC</td>
            <td><a href="https://doi.org/10.1016/j.biosystemseng.2019.11.017">Lameness detection of dairy cows based on the YOLOv3 deep learning algorithm and a relative step size characteristic vector</a></td>
            <td>Biosystems Engineering</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>Onsite devices</td>
            <td>EFMYOLOv3, SSD, YOLOv3</td>
            <td><a href="https://doi.org/10.1016/j.compag.2020.105754">Automatic recognition of dairy cow mastitis from thermal images by a deep learning detector</a></td>
            <td>Computers and Electronics in Agriculture</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>Onsite devices</td>
            <td>Fast-RCNN-VGG16, YOLOv3-Darknet53</td>
            <td><a href="https://doi.org/10.1016/j.compag.2020.105627">Deep learning-based hierarchical cattle behavior recognition with spatio-temporal information</a></td>
            <td>Computers and Electronics in Agriculture</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>Onsite devices</td>
            <td>CNN</td>
            <td><a href="https://doi.org/10.1016/j.biosystemseng.2020.03.013">Assessment of dairy cow heat stress by monitoring drinking behaviour using an embedded imaging system</a></td>
            <td>Biosystems Engineering</td>
        </tr>
        <tr>
            <td>2021</td>
            <td>Onsite devices</td>
            <td>AlexNet, VGG16, ResNet50, MobilNet V2, GoogLeNet</td>
            <td><a href="https://doi.org/10.1371/journal.pone.0307252">Individual dairy cow identification based on lightweight convolutional neural network</a></td>
            <td>Plos One</td>
        </tr>
        <tr>
            <td>2021</td>
            <td>Public datasets</td>
            <td>AP-10K</td>
            <td><a href="https://doi.org/10.3390/rs13040603">Ap-10k: A benchmark for animal pose estimation in the wild</a></td>
            <td>Arxiv Preprint</td>
        </tr>
        <tr>
            <td>2022</td>
            <td>MS COCO datasets</td>
            <td>ViTPose</td>
            <td><a href="https://github.com/ViTAE-Transformer/ViTPose">Vitpose: Simple vision transformer baselines for human pose estimation</a></td>
            <td>NeurIPS</td>
        </tr>
        <tr>
            <td>2022</td>
            <td>Public datasets</td>
            <td>GMFlow</td>
            <td><a href="https://github.com/haofeixu/gmflow">GMflow: Learning optical flow via global matching</a></td>
            <td>CVPR</td>
        </tr>
        <tr>
            <td>2022</td>
            <td>Onsite devices, MODIS</td>
            <td>GLM, GAM, RF, GBM, NNET, MARS, FDA, CT, SVM, NB, ADA</td>
            <td><a href="https://doi.org/10.1016/j.actatropica.2022.106542">Exploration of machine learning models to predict the environmental and remote sensing risk factors of haemonchosis in sheep flocks of Rajasthan, India</a></td>
            <td>Acta Tropica</td>
        </tr>
    </tbody>
</table>

## Fishing Area Identification And Prediction
<table>
    <thead>
        <tr>
            <th>Year</th>
            <th>Input</th>
            <th>Methods</th>
            <th>Paper Title</th>
            <th>Pub.</th>
        </tr>
    </thead>
    <tbody align='center'>        
        <tr>
            <td>2017</td>
            <td>Sentinel-1</td>
            <td>Connected Component Segmentation</td>
            <td><a href="https://doi.org/10.3390/rs9050440">Large-scale assessment of coastal aquaculture ponds with Sentinel-1 time series data</a></td>
            <td>Remote Sensing</td>
        </tr>
        <tr>
            <td>2018</td>
            <td>GF-1</td>
            <td>DS-HCN, AT, DeepLab</td>
            <td><a href="https://doi.org/10.3390/rs10071130">Automatic raft labeling for remote sensing images via dual-scale homogeneous convolutional neural network</a></td>
            <td>Remote Sensing</td>
        </tr>
        <tr>
            <td>2019</td>
            <td>Landsat TM, OLI, GF-1</td>
            <td>SVM</td>
            <td><a href="https://doi.org/10.1016/j.jag.2019.03.019">Extracting aquaculture ponds from natural water surfaces around inland lakes on medium resolution multispectral images</a></td>
            <td>International Journal of Applied Earth Observation and Geoinformation</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>GF-2</td>
            <td>HDCUNet, FCN-8s, SegNet, U-Net, TS</td>
            <td><a href="https://doi.org/10.1080/01431161.2019.1706009">Research on a novel extraction method using Deep Learning based on GF-2 images for aquaculture areas</a></td>
            <td>International Journal of Remote Sensing</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>Landsat, GF-1, ALOS, ZY-3</td>
            <td>RCSANet</td>
            <td><a href="https://doi.org/10.3390/rs13010092">RCSANet: A full convolutional network for extracting inland aquaculture ponds from high-spatial-resolution images</a></td>
            <td>Remote Sensing</td>
        </tr>
        <tr>
            <td>2021</td>
            <td>GF-1, GF-2</td>
            <td>Semi-SSN</td>
            <td><a href="https://doi.org/10.3390/rs13061083">Semi-/Weakly-supervised semantic segmentation method and its application for coastal aquaculture areas based on multi-source remote sensing
              images—taking the Fujian coastal area (mainly Sanduo) as an example</a></td>
            <td>Remote Sensing</td>
        </tr>
        <tr>
            <td>2022</td>
            <td>NOAA, METOP-1, METOP-2, MODIS</td>
            <td>HE-DFNETS</td>
            <td><a href="https://doi.org/10.1155/2022/5081541">HE‐DFNETS: a novel hybrid deep learning architecture for the prediction of potential fishing zone areas in Indian Ocean using remote sensing images</a></td>
            <td>Computational Intelligence and Neuroscience</td>
        </tr>
    </tbody>
</table>

### Fish Production Forecast
<table>
    <thead>
        <tr>
            <th>Year</th>
            <th>Input</th>
            <th>Methods</th>
            <th>Paper Title</th>
            <th>Pub.</th>
        </tr>
    </thead>
    <tbody align='center'>        
        <tr>
            <td>2020</td>
            <td>Onsite devices</td>
            <td>Faster MSSDLite, YOLOv3, Faster RCNN, HOG+SVM</td>
            <td><a href="https://doi.org/10.1016/j.compag.2020.105339">Real-time robust detector for underwater live crabs based on deep learning</a></td>
            <td>Computers and Electronics in Agriculture</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>Onsite devices</td>
            <td>YOLOv3</td>
            <td><a href="https://doi.org/10.1093/icesjms/fsz223">Automatic detection of Western rock lobster using synthetic data</a></td>
            <td>ICES Journal of Marine Science</td>
        </tr>
        <tr>
            <td>2021</td>
            <td>UAV</td>
            <td>YOLOv3</td>
            <td><a href="https://doi.org/10.3390/drones5020028">Seecucumbers: Using deep learning and drone imagery to detect sea cucumbers on coral reef flats</a></td>
            <td>Drones</td>
        </tr>
        <tr>
            <td>2022</td>
            <td>Onsite devices</td>
            <td>MaskRCNN</td>
            <td><a href="https://doi.org/10.1016/j.ecss.2022.107815">Coastal fisheries resource monitoring through A deep learning-based underwater video analysis</a></td>
            <td>Estuarine, Coastal and Shelf Science</td>
        </tr>
        <tr>
            <td>2022</td>
            <td>Onsite devices, MODIS, Landsat, GSW JRC</td>
            <td>XGBoost</td>
            <td><a href="https://doi.org/10.3389/fenvs.2022.944319">Remote sensing modeling of environmental influences on lake fish resources by machine learning: A practice in the largest freshwater lake of China</a></td>
            <td>Frontiers in Environmental Science</td>
        </tr>
        <tr>
            <td>2023</td>
            <td>Onsite devices</td>
            <td>MobileCenterNet, ResNet-CenterNet</td>
            <td><a href="https://doi.org/10.1016/j.compag.2022.107522">Real-time detection of underwater river crab based on multi-scale pyramid fusion image enhancement and MobileCenterNet model</a></td>
            <td>Computers and Electronics in Agriculture</td>
        </tr>
        <tr>
            <td>2024</td>
            <td>Onsite devices</td>
            <td>FMRFT</td>
            <td><a href="https://doi.org/10.48550/arXiv.2409.01148">FMRFT: Fusion Mamba and DETR for Query Time Sequence Intersection Fish Tracking</a></td>
            <td>arXiv</td>
        </tr>
      </tbody>
</table>

## Fish Product Classification
<table>
    <thead>
        <tr>
            <th>Year</th>
            <th>Input</th>
            <th>Methods</th>
            <th>Paper Title</th>
            <th>Pub.</th>
        </tr>
    </thead>
    <tbody align='center'> 
        <tr>
            <td>2020</td>
            <td>Laboratory conditions</td>
            <td>Mask-RCNN</td>
            <td><a href="https://doi.org/10.1016/j.foodcont.2020.107184">An application of Convolutional Neural Network to lobster grading in the Southern Rock Lobster supply chain</a></td>
            <td>Food Control</td>
        </tr>
        <tr>
            <td>2020</td>
            <td>Onsite devices</td>
            <td>CNN</td>
            <td><a href="https://doi.org/10.1007/s43674-022-00048-6">Fish recognition model for fraud prevention using convolutional neural networks</a></td>
            <td>Advances in Computational Intelligence</td>
        </tr>
    </tbody>
</table>

# Reference

If you find this repo is helpful, please consider giving this repo a ⭐ and citing:

```
@article{agrifoodsurveys,
author = {Chen, Tao and Lv, Liang and Wang, Di and Zhang, Jing and Yang, Yue and Zhao, Zeyang and Wang, Chen and Guo, Xiaowei and Chen, Hao and Wang, Qingye and Xu, Yufei and Zhang, Qiming and Du, Bo and Zhang, Liangpei and Tao, Dacheng},
title = {Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities},
year = {2024},
volume = {57},
number = {2},
journal = {ACM Computing Surveys},
doi = {10.1145/3698589}
}
```

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