Artificial neural network model in predicting yield of mechanically transplanted rice from transplanting parameters in Bangladesh

• Published in: Journal of agriculture and food research 2021-09, Vol.5, p.100186, Article 100186
• Main Authors: Basir, Md Samiul, Chowdhury, Milon, Islam, Md Nafiul, Ashik-E-Rabbani, Muhammad
• Format: Article
• Language: English
• Subjects: ANN; Mechanical rice transplantation; Regression; Transplanting parameters; Yield prediction model
• ISSN: 2666-1543; 2666-1543
• DOI: 10.1016/j.jafr.2021.100186

The yield of rice largely depends on transplanting techniques. Mechanical transplanting is gaining popularity as a cost-saving and on-time operation with less labor orientation in rice cultivation. This experiment was performed to investigate the relation of rice yield (g m−2) with four mechanical rice transplanting parameters (i.e., seedling density in the tray (nos.cm−2), missing hill percentage, floating hill percentage, and seedling number per hill), and to develop an Artificial Neural Networks (ANN) model to predict the yield from the transplanting parameters. A regression analysis was also conducted to validate the accuracy of the trained ANN model. This study reveals that the dependency of yield on these four parameters is not ignorable and significant. The ANN model performed an accurate match in predicting yield from transplanting parameters with an R2 value of 0.994 and adjusted R2 of 0.993. The ANN model possessed an RMSE of 4.577 in predicting yield which lied in the allowable range of 10 %. The ANN model showed better accuracy in predicting yield than the regression model and pretended to be an alternative to numerical models in predicting yield. The findings of this study showed that ANN-based models would be an alternative to the regression model and a more accurate method of yield prediction based on the transplanting field parameters. [Display omitted] •Yield of mechanically transplanted rice has a strong relation with transplanting parameters.•Rice yield can be predicted from transplanting parameters.•Artificial neural network can predict the yield from transplanting parameters accurately.