Abstract
Paddy is a staple food for more than 93 countries and it will stay of life for future generations. Harvesting is one of the vital operations in crop production and timely harvesting is essential for getting maximum yield. Moisture content and forward speed are the two factors to overcome the post harvest losses and minimise the quantitative losses. In this paper, an artificial neural network is introduced to assess the grain losses in the field condition. The simulation result shows that the ANN method is appropriate and feasible to assess the grain losses. However, model results that, an error of (RMSE) 0.1582 for cutter bar loss, for threshing loss was 0.1299 and for separation loss was 0.1321. Hence, the ANN model help the operator / farmer to decide the time of harvest. It also minimizes the post harvest grain losses by considering crop and machine parameters.
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Hiregoudar, S., Udhaykumar, R., Ramappa, K.T., Shreshta, B., Meda, V., Anantachar, M. (2011). Artificial Neural Network for Assessment of Grain Losses for Paddy Combine Harvester a Novel Approach. In: Balasubramaniam, P. (eds) Control, Computation and Information Systems. ICLICC 2011. Communications in Computer and Information Science, vol 140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19263-0_27
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DOI: https://doi.org/10.1007/978-3-642-19263-0_27
Publisher Name: Springer, Berlin, Heidelberg
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