Prediction of productivity of papaya (Carica Papaya L.) by Artificial Neural Networks (Research, Society and Development, 2021)
The estimation of the productivity of papaya is important for the planning of rural producers, becoming an efficient and strategic tool in the decision making of agricultural production, especially with regard to post-harvest planning, storage and transportation. The objective of this study was to select Artificial Neural Network models with greater predictive capacity of the harvest for the papaya crop CNPMF-L78. A total of 352 plants were measured in a useful area of 2,112 m², with the plant descriptors and the characters related to fruit quality. The data were submitted to artificial neural network analysis using the R software. The best fits for estimating the productivity of papaya were considered for three layers of ANNs: input, intermediate, and output layers, testing the composition with one to 10 neurons in the intermediate layer. These models presented the lowest mean square errors, which corresponds to greater proximity between predicted and actual data, and therefore greater efficiency of the networks in predicting yields. By the coefficient of determination, the best fits were verified for reproductive characters R² = 0.99, vegetative characters provided R²= 0.54, while quality and yield characters enabled R²= 0.24. Productivity predictions for CNPMF-L78 papaya were obtained with high efficiency for reproductive characters by means of artificial neural networks.