Abstract
Forecasting of crop yield is helpful in food management and growth of a nation, which has specially agriculture based economy. In the last few decades, Artificial Neural Networks have been used successfully in different fields of agricultural remote sensing especially in crop type classification and crop area estimation. The present work employed two types of Artificial Neural Networks i.e., a Generalized Regression Neural Network (GRNN) and a Radial Basis Function Neural Network (RBFNN) to predict the yield of potato crops, which have been sown differently (flat and rough). Crop parameters like leaf area index, biomass and plant height were used as input data, while the yield of potato fields as output dataset to train and test the Neural Networks. Both GRNN and RBNN predicted potato crop yield accurately. However based on quick learning capability and lower spread constant (0.5), the GRNN was found a better predictor than RBFNN. Furthermore, the rough surface field was found more productive than flat field.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.