IN AND END OF SEASON SOYBEAN YIELD PREDICTION WITH HISTOGRAM BASED DEEP LEARNING


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Erik E., DURMAZ M., OK A. Ö.

39th International Symposium on Remote Sensing of Environment, ISRSE 2023, Antalya, Turkey, 24 - 28 April 2023, vol.48, pp.95-100 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 48
  • Doi Number: 10.5194/isprs-archives-xlviii-m-1-2023-95-2023
  • City: Antalya
  • Country: Turkey
  • Page Numbers: pp.95-100
  • Keywords: Crop Yield Prediction, Deep Learning, Google Earth Engine, Histogram, QGIS Plugin
  • Hacettepe University Affiliated: Yes

Abstract

One sector that feels the effects of global warming and climate change on all levels is agriculture. In order to prepare for possible yield loss, as well as market, storage, and import planning challenges brought on by climate change, businesses can utilise agricultural decision support applications. Within the scope of this study, a crop yield prediction module has been developed that can provide in and end of season estimation of crop yields to be obtained from the determined regions. The Python programming language was used in the creation of the module as a QGIS plugin. The area for which crop yield predictions are to be made is covered by retrieving MODIS SR, MODIS LST, and Daymet data from the Google Earth Engine data catalogue. Histograms obtained from remotely sensed images are used as input data to two deep learning methods (CNN-LSTM and HistCNN). As a result, the HistCNN model outperformed CNN-LSTM for in season soybean yield prediction, with an R2 of 0.72, while the CNN-LSTM model outperformed it for in end of season soybean yield prediction, with an R2 of 0.67.