Model for estimating red mulberry leaf area using a genetic algorithm
DOI:
https://doi.org/10.36560/17620241996Keywords:
artificial intelligence, optimization, Morus rubraAbstract
The success of sericulture depends directly on the quality and quantity of mulberry leaves, as it is essential for the feeding and development of silkworm caterpillars and, consequently, influences the quality of the silk thread manufactured. The estimation of mulberry leaf area is important to have plant development and growth indicators, such as transpiration intensity, net assimilation rate, leaf area ratio, specific leaf area and leaf area index, which allow predicting crop productivity. Thus, the objective of this study was to develop and test a model capable of estimating the red mulberry leaf area using a genetic algorithm. The model was adjusted with the proposed stochastic optimization method. The mean error found for the tested dataset was approximately 228.17 mm2 in sample space with mean leaf area of 6515.55 mm2. The information generated allows applying the model to estimate red mulberry leaf area in future studies.
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