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Prediction of Apple Fruit Quality by Soil Nutrient Content and Artificial Neural Network

Mengyao Yan1, Xianqi Zeng1, Banghui Zhang1, Hui Zhang2, Di Tan1, Binghua Cai1, Shenchun Qu1, Sanhong Wang1,*
1 College of Horticulture, Nanjing Agricultural University, Nanjing, China
2 College of Agriculture, Nanjing Agricultural University, Nanjing, China
* Corresponding Author: Sanhong Wang. Email:
(This article belongs to this Special Issue: Integrating Agronomy and Plant Physiology for Improving Crop Production)

Phyton-International Journal of Experimental Botany 2023, 92(1), 193-208. https://doi.org/10.32604/phyton.2022.023078

Received 08 April 2022; Accepted 25 May 2022; Issue published 06 September 2022

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.

Keywords

Apple; soil nutrients; fruit quality; artificial neural network; sensitivity analysis; response surface methodology analysis

Cite This Article

Yan, M., Zeng, X., Zhang, B., Zhang, H., Tan, D. et al. (2023). Prediction of Apple Fruit Quality by Soil Nutrient Content and Artificial Neural Network. Phyton-International Journal of Experimental Botany, 92(1), 193–208.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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