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Proceedings Paper

Crop yield prediction using a CMAC neural network
Author(s): George Simpson
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Paper 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.

Paper Details

Date Published: 30 December 1994
PDF: 12 pages
Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); doi: 10.1117/12.196712
Show Author Affiliations
George Simpson, Earth Observation Sciences (United Kingdom)


Published in SPIE Proceedings Vol. 2315:
Image and Signal Processing for Remote Sensing
Jacky Desachy, Editor(s)

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