Spiking Neural Networks for Crop Yield Estimation Based on Spatiotemporal Analysis of Image Time Series

Pritam Bose, Nikola Kasabov, Lorenzo Bruzzone, Reggio Hartono

Research output: Contribution to journalArticlepeer-review

85 Citations (Scopus)


This paper presents spiking neural networks (SNNs) for remote sensing spatiotemporal analysis of image time series, which make use of the highly parallel and low-power-consuming neuromorphic hardware platforms possible. This paper illustrates this concept with the introduction of the first SNN computational model for crop yield estimation from normalized difference vegetation index image time series. It presents the development and testing of a methodological framework which utilizes the spatial accumulation of time series of Moderate Resolution Imaging Spectroradiometer 250-m resolution data and historical crop yield data to train an SNN to make timely prediction of crop yield. The research work also includes an analysis on the optimum number of features needed to optimize the results from our experimental data set. The proposed approach was applied to estimate the winter wheat (Triticum aestivum L.) yield in Shandong province, one of the main winter-wheat-growing regions of China. Our method was able to predict the yield around six weeks before harvest with a very high accuracy. Our methodology provided an average accuracy of 95.64%, with an average error of prediction of 0.236 t/ha and correlation coefficient of 0.801 based on a nine feature model.
Original languageEnglish
Pages (from-to)6563-6573
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number11
Early online date28 Jul 2016
Publication statusPublished (in print/issue) - 1 Nov 2016


  • crop yield forecasting
  • machine learning
  • Moderate Resolution Imaging
  • Spectroradiometer (MODIS)
  • normalised difference vegetation index (NDVI)
  • remote sensing
  • spiking neural networks


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