Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networks

ABSTRACT Estimating cactus pear yield is important for the planning of small and medium rural producers, especially in environments with adverse climatic conditions, such as the Brazilian semi-arid region. The objective of this study was to evaluate the potential of artificial neural networks (ANN) for predicting yield of ‘Gigante’ cactus pear, and determine the most important morphological characters for this prediction. The experiment was conducted in the Instituto Federal Baiano, Guanambi campus, Bahia, Brazil, in 2009 to 2011. The area used is located at 14° 13’ 30” S and 42° 46’ 53” W, and its altitude is 525 m. Six vegetative agronomic characters were evaluated in 500 plants in the third production cycle. The data were subjected to ANN analysis using the R software. Ten network architectures were trained 100 times to select the one with the lowest mean square error for the validation data. The networks with five neurons in the middle layer presented the best results. Neural networks with coefficient of determination (R2) of 0.87 were adjusted for sample validation, assuring the generalization potential of the model. The morphological characters with the highest relative contribution to yield estimate were total cladode area, plant height, cladode thickness and cladode length, but all characters were important for predicting the cactus pear yield. Therefore, predicting the production of cactus pear with high precision using ANN and morphological characters is possible. RESUMO A estimativa da produtividade em palma forrageira é fundamental ao planejamento rural dos pequenos e médios produtores, sobretudo, em condições de adversidades climáticas como no Semiárido Brasileiro. Objetivou-se avaliar o potencial de Redes Neurais Artificiais (RNAs) na predição da produtividade em palma forrageira, e determinar os caracteres morfológicos mais importantes neste estudo. O experimento foi conduzido no Instituto Federal Baiano, Campus Guanambi, Bahia, Brasil, no período agrícola de 2009 a 2011. A região localiza-se nas seguintes coordenadas geográficas: latitude 14° 13’ 30” Sul, longitude de 42° 46’ 53” Oeste de Greenwich, altitude de 525 m. Avaliaram-se em 500 plantas seis caracteres agronômicos de natureza vegetativa no terceiro ciclo de produção. Os dados foram submetidos à análise no software R por RNAs. Dez arquiteturas de rede foram treinadas por 100 vezes, selecionando-se, ao final do treinamento, aquela com menor erro quadrático médio para os dados de vali