6533b827fe1ef96bd12867da

RESEARCH PRODUCT

Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks

Martin EngenErik SandøBenjamin Lucas Oscar SjølanderSimon ArenbergRashmi GuptaMorten Goodwin

subject

hybrid neural networkSVDP::Landbruks- og Fiskerifag: 900::Landbruksfag: 910farm-scale crop yield prediction; deep learning; hybrid neural network; convolutional neural network; recurrent neural network; Sentinel-2 satellite remote sensing datadeep learningconvolutional neural networkSentinel-2 satellite remote sensing datarecurrent neural networkAgriculturefarm-scale crop yield predictionAgronomy and Crop Science

description

Farm-scale crop yield prediction is a natural development of sustainable agriculture, producing a rich amount of food without depleting and polluting environmental resources. Recent studies on crop yield production are limited to regional-scale predictions. The regional-scale crop yield predictions usually face challenges in capturing local yield variations based on farm management decisions and the condition of the field. For this research, we identified the need to create a large and reusable farm-scale crop yield production dataset, which could provide precise farm-scale ground-truth prediction targets. Therefore, we utilise multi-temporal data, such as Sentinel-2 satellite images, weather data, farm data, grain delivery data, and cadastre-specific data. We introduce a deep hybrid neural network model to train this multi-temporal data. This model combines the features of convolutional layers and recurrent neural networks to predict farm-scale crop yield production across Norway. The proposed model could efficiently make the target predictions with the mean absolute error of 76 kg per 1000 m2. In conclusion, the reusable farm-scale multi-temporal crop yield dataset and the proposed novel model could meet the actual requirements for the prediction targets in this paper, providing further valuable insights for the research community.

yearjournalcountryeditionlanguage
2021-12-18Agronomy
https://doi.org/10.3390/agronomy11122576