Using artificial neural network in predicting the key fruit quality of loquat

Abstract

The formation and regulation of loquat fruit quality have always been an important research field to improve fruit quality, commodities, and market value. Fruit size, soluble solids content, and titratable acid content represent the most important quality factors in loquat. Mineral nutrients in abundance or deficiency are among the most important key factor that affect fruit quality. In the present study, we use artificial neural network (ANN) to explore the effects of mineral nutrients in soil and leaves on the key fruit quality of loquat. The results show that the ANN model with the structure of 12–12–1 can predict the single fruit weight with the highest accuracy (R2 = .91), the ANN model with the structure of 10–11–1 can predict the soluble solid content with the highest accuracy (R2 = .91), and the ANN model with the structure of 9–10–1 can predict the titratable acid content with the highest accuracy (R2 = .95). Meanwhile, we also conduct sensitivity analysis to analyze the relative contribution of mineral nutrients in soils and leaves to determine of the key fruit quality. In terms of relative contribution, Ca, Fe, and Mg content in soils and Zn, K, and Ca content in leaves contribute relatively largely to a single fruit weight, Mn, Fe, and Mg content in soils and the N content in leaves contribute relatively largely to the soluble solid content, and the P, Ca, N, Mg, and Fe in leaves contribute relatively largely to the titratable acid content of loquat. The established artificial neural network prediction models can improve the quality of loquat fruit by optimizing the content of mineral elements in soils and leaves.

Integrative analyses of metabolome and transcriptome reveals metabolomic variations and candidate genes involved in sweet cherry (Prunus avium L.) fruit quality during development and ripening

“…Studying fruit quality not only helps us to select a breeding program by exploring the most promising fruit genotypes, but also helps to improve the fruit quality, health-promoting components, commodity status, and market value [ 6 , 33 ]. As a seasonal fruit, sweet cherry is highly edible and has ornamental value.…”

Prediction of tannin, protein, and total phenolic content of grain sorghum using image analysis and machine learning

“…Several previous studies (reviewed in Huang et al, 2021) reported high prediction accuracy of the ANN model employed in plant science and indicated that it can be an effective and reliable forecasting statistical tool. The multilayer perceptron (MLP) is one of the most common types of ANN broadly used in different breeding programs to find a model for the prediction of complex traits, such as yield.…”

Physico-Chemical Properties Prediction of Flame Seedless Grape Berries Using an Artificial Neural Network Model

“…Moreover, different studies examined ANN for determining the quality of different fruit and vegetables with the aid of some features that include fruit color, size, etc. [ 28 , 30 ]. Although determining the quality of any food material is a challenging task, ANN technologies and their intelligent capabilities make them both highly accurate and economically advantageous [ 29 ].…”

“…It can treat existing data internally through different activation functions, adjust weights for different settings, examine hidden associations between data, and, therefore, represent unknown data, which is of different significance for prediction. An ANN’s powerful computing power lets it treat more data and solve many identical multifaceted nonlinear problems [ 30 ]. As an example, Amoriello et al [ 28 ] collected data for seven strawberry varieties and created two statistical procedures, i.e., multiple linear regression (MLR) and ANN models, to predict titratable acidity, dry matter, soluble solids, and firmness as well as nutritional attributes, such as antioxidant potential, total anthocyanins, and total phenols.…”

“…The study showed that color coordinates and the established ANN models could be successfully utilized to assess strawberry quality. Huang et al [ 30 ] applied ANN to discover the effects of mineral nutrients in leaves and soil on the fruit quality attributes of loquat. The results indicated that the developed ANN models with different topologies could predict the titratable acid content, the single fruit weight, and soluble solids content with higher coefficients of determination.…”