1. Prediction of Apple Fruit Quality by Soil Nutrient Content and Artificial Neural Network.
- Author
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Mengyao Yan, Xianqi Zeng, Banghui Zhang, Hui Zhang, Di Tan, Binghua Cai, Shenchun Qu, and Sanhong Wang
- Subjects
ARTIFICIAL neural networks ,APPLES ,FRUIT quality ,RESPONSE surfaces (Statistics) ,ORCHARDS - Abstract
The effect of soil nutrient content on fruit yield and fruit quality is very important. To explore the effect of soil nutrients on apple quality we investigated 200 fruit samples from 40 orchards in Feng County, Jiangsu Province. Soil mineral elements and fruit quality were measured. The effect of soil nutrient content on fruit quality was analyzed by artificial neural network (ANN) model. The results showed that the prediction accuracy was highest (R² = 0.851, 0.847, 0.885, 0.678 and 0.746) in mass per fruit (MPF), hardness (HB), soluble solids concentrations (SSC), titratable acid concentration (TA) and solid-acid ratio (SSC/TA), respectively. The sensitivity analysis of the prediction model showed that soil available P, K, Ca and Mg contents had the greatest impact on the quality of apple fruit. Response surface method (RSM) was performed to determine the optimum range of the available P, K, Ca, and Mg contents in orchards In Feng County, which were 10~20 mg.kg
-1 , 170~200 mg.kg-1 , 1000~1500 mg.kg-1 , and 80~200 mg·kg-1 , respectively. The research also concluded that improving the content of available P and available Ca in orchard soil was crucial to improve apple fruit quality in Feng County, Jiangsu Province. [ABSTRACT FROM AUTHOR]- Published
- 2023
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