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Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data

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Stefano ErmonAuthors Info & Claims

COMPASS '18: Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies

Article No.: 50, Pages 1 - 5

https://doi.org/10.1145/3209811.3212707

Published: 20 June 2018 Publication History

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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.

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Cited By

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Huber FInderka ASteinhage V(2024)Leveraging Remote Sensing Data for Yield Prediction with Deep Transfer LearningSensors10.3390/s2403077024:3(770)Online publication date: 24-Jan-2024
https://doi.org/10.3390/s24030770
Yang CMao KGuo ZShi JBateni SYuan Z(2024)Review of GNSS-R Technology for Soil Moisture InversionRemote Sensing10.3390/rs1607119316:7(1193)Online publication date: 28-Mar-2024
https://doi.org/10.3390/rs16071193
de França e Silva NChaves MLuciano ASanches Ide Almeida CAdami M(2024)Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic ReviewRemote Sensing10.3390/rs1605086316:5(863)Online publication date: 29-Feb-2024
https://doi.org/10.3390/rs16050863
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Index Terms

Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data
1. Applied computing
  1. Physical sciences and engineering
    1. Earth and atmospheric sciences
      1. Environmental sciences

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Published In

COMPASS '18: Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies

June 2018

472 pages

ISBN:9781450358163

DOI:10.1145/3209811

Conference Chair:
Ellen Zegura
Georgia Tech

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 June 2018

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COMPASS '18

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SIGCAS

COMPASS '18: ACM SIGCAS Conference on Computing and Sustainable Societies

June 20 - 22, 2018

CA, Menlo Park and San Jose, USA

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Overall Acceptance Rate 25 of 50 submissions, 50%

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Cited By

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Huber FInderka ASteinhage V(2024)Leveraging Remote Sensing Data for Yield Prediction with Deep Transfer LearningSensors10.3390/s2403077024:3(770)Online publication date: 24-Jan-2024
https://doi.org/10.3390/s24030770
Yang CMao KGuo ZShi JBateni SYuan Z(2024)Review of GNSS-R Technology for Soil Moisture InversionRemote Sensing10.3390/rs1607119316:7(1193)Online publication date: 28-Mar-2024
https://doi.org/10.3390/rs16071193
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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

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