Optimization of wheat grain yield by artificial neural network
Journal Title: International Journal of Farming and Allied Sciences - Year 2014, Vol 3, Issue 7
Abstract
Wheat is more important than other grain crops. Maximum grain yield can be determined by several components that reflect positive or negative effects. The objective of this article is optimization of wheat grain yield by artificial neural network. Field experiment was carried out at the Department of Medicine Plant, Arak University, Iran. The results indicated that the remobilization of stored pre-anthesis assimilates to grain (R1), crop height (R2), 1,000-grains weight (R3), grain number per ear (R4), vegetative growth duration (R5), grain-filling duration (R6), grain-filling rate (R7), tiller number (R8), harvest index (R9), and spike length (R10), were effective. The R2 for the training and test phases was 0.99 and 0.94, respectively, which reveals the capability of the ANN to predicting yield. The optimum values obtained were 15.1%, 107.2 cm, 38.5 g, 42.2, 108 d, 52 d, 1.16 mg seed-1 day-1, 3.48 plant-1 and 12.5 cm for R1 through R10, respectively. The optimization increased the potential yield to 6200 kg ha-1, which was higher than that observed for the cultivars (3200 to 5300 kg ha-1). As a result of training the neural network, the accuracy of predicting yields is on average about 72%.
Authors and Affiliations
Hussein Salehi Arjmand, Mansour Ghorbanpour, Saeed Sharafi, Gholam Hussein Babaei Abarghooei
Keywords
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