Anna X. Wang
ABSTRACT
Accurate prediction of crop yields in developing countries in advance of harvest time is central to preventing famine, improving food security, and sustainable development of agriculture. Existing techniques are expensive and difficult to scale as they require locally collected survey data. Approaches utilizing remotely sensed data, such as satellite imagery, potentially provide a cheap, equally effective alternative. Our work shows promising results in predicting soybean crop yields in Argentina using deep learning techniques. We also achieve satisfactory results with a transfer learning approach to predict Brazil soybean harvests with a smaller amount of data. The motivation for transfer learning is that the success of deep learning models is largely dependent on abundant ground truth training data. Successful crop yield prediction with deep learning in regions with little training data relies on the ability to fine-tune pre-trained models.
- 2013. MCD12Q1 MODIS/Land Cover Type Yearly L3 Global 500m SIN Grid. (2013).Google Scholar
- Douglas K. Bolton and Mark A. Friedl. 2013. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agricultural and Forest Meteorology 173 (2013), 74--84.Google ScholarCross Ref
- Neal Jean, Marshall Burke, Michael Xie, W Matthew Davis, David B Lobell, and Stefano Ermon. 2016. Combining satellite imagery and machine learning to predict poverty. Science 353, 6301 (2016), 790--794.Google ScholarCross Ref
- David B. Lobell, David Thau, Christopher Seifert, Eric Engle, and Bertis Little. 2015. A scalable satellite-based crop yield mapper. Remote Sensing of Environment 164 (7 2015), 324--333.Google Scholar
- Michael McPhaden and Aaron Levine. 2016. NOAA: How the failed 2014-15 El Nino fueled the strong 2015-16 El Nino. https://www.pmel.noaa.gov/elnino/news-story/how-failed-2014-15-el-nino-fueled-strong-2015-16-el-ninoGoogle Scholar
- Reid Pryzant, Stefano Ermon, and David Lobell. 2017. Monitoring Ethiopian Wheat Fungus with Satellite Imagery and Deep Feature Learning. In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 1524--1532.Google Scholar
- N. A. Quarmby, M. Milnes, T. L. Hindle, and N. Silleos. 1993. The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. International Journal of Remote Sensing 14, 2 (1993), 199--210.Google ScholarCross Ref
- Brasil Sistema IBGE de Recuperacao Automatica, Instituto Brasileiro de Geografia e Estatistica. {n. d.}. Producao Agricola Municipal: producao das lavouras temporarias. https://sidra.ibge.gov.br/tabela/1612Google Scholar
- Delegaciones y Estudios Economicos Argentina Subsecretaria de Agricultura, Direccion Nacional de Estimaciones. {n. d.}. Datos Argentina: Estimaciones agricolas. http://datos.gob.ar/dataset/estimaciones-agricolasGoogle Scholar
- Eric Vermote. 2015. MOD09A1 MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V006. (2015).Google Scholar
- Zhengming Wan and S. Hook. 2015. MYD11A2 MODIS/Aqua Land Surface Temperature/Emissivity 8-Day L3 Global 1km SIN Grid V006. (2015).Google Scholar
- Michael Xie, Neal Jean, Marshall Burke, David Lobell, and Stefano Ermon. 2016. Transfer learning from deep features for remote sensing and poverty mapping. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. AAAI Press, 3929--3935. Google ScholarDigital Library
- Jiaxuan You, Xiaocheng Li, Melvin Low, David Lobell, and Stefano Ermon. 2017. Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data. 2017 Association for the Advancement of Articial Intelligence (2017).Google Scholar
Index Terms
Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data
Recommendations
Prediction of summer grain crop yield with a process-based ecosystem model and remote sensing data for the northern area of the Jiangsu Province, China
Geoinformatics 2007Yield prediction is important for agricultural management, food security warning and food trade policy. Remote sensing has been a useful tool for predicting crop yields. In this study, a modified daily process-based ecosystem model (the Boreal Ecosystem ...
Monitoring crop phenology with street-level imagery using computer vision
Highlights- Method to automatically collect side looking imagery for agriculture parcels.
- ...
AbstractStreet-level imagery holds a significant potential to scale-up in-situ data collection. This is enabled by combining the use of cheap high-quality cameras with recent advances in deep learning compute solutions to derive relevant ...
Remote sensing crop group-specific indicators to support regional yield forecasting in Europe
Highlights- A comparison of crop masks to spatially aggregate NDVI for yield forecasting in EU.
AbstractOperational crop yield forecasting services typically provides crop yield forecasts based on regression models between official yields and agro-environmental variables, among which meteorological data, crop simulation model or ...
Comments