Crop yield prediction using a CMAC neural network

George Simpson

doi:10.1117/12.196712

30 December 1994 Crop yield prediction using a CMAC neural network

George Simpson

Proceedings Volume 2315, Image and Signal Processing for Remote Sensing; (1994) https://doi.org/10.1117/12.196712
Event: Satellite Remote Sensing, 1994, Rome, Italy

Abstract

This paper presents the results of a short study to investigate the use of a fast cerebellar model articulation controller (CMAC) neural network for crop yield prediction. It goes on to explore the possibility of combining crop classification and yield prediction into a single network component, suitable for large-scale crop management. In the first part of the work, a small feasibility study of crop classification performance was carried out in two steps. First, prediction performance was evaluated using only monthly agro-met data (soil moisture, temperature, sunshine). Then the improvement in prediction performance after incorporating remote sensing data (Landsat TM) was measured. The standard error was 5% when TM data were included, versus 6% when TM was ignored. The CMAC neural network applied in this study has previously been successfully applied in two similar domains: real-time cloud classification on Meteosat data and mineral identification with airborne visible and infrared imaging spectrometer (AVIRIS) data. Two features peculiar to this classifier are that it handles mixtures naturally and that it is capable of returning a `don't know' response. It is natural therefore to consider the possibility of performing crop classification and yield prediction in a single step. We find that it is feasible to perform weekly combined classification and yield predictions for all of Europe, on a 1 km grid, for 100 different crops, using a cluster of five workstations.

Citation Download Citation

George Simpson "Crop yield prediction using a CMAC neural network", Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); https://doi.org/10.1117/12.196712

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