热带农业工程2025，Vol.49Issue(1)：23-28,6.
基于手机相片和机器学习算法的滨海土壤盐分含量预测
Prediction of Coastal Soil Salinity Content Based on Mobile Phone Photos and Machine Learning Algorithms
摘要
Abstract
This study focused on exposed soils in the coastal areas of Yancheng City,Jiangsu Province,collecting soil samples under various weather conditions.Concurrently,smartphone images of the corresponding soil areas were captured.Soil electrical conductivity(EC)was measured indoors,and the collected soil images were processed using RStudio software.The results indicated a significant correlation between soil EC and the hue parameter in the image colors,as well as correlations between hue and brightness,and brightness and saturation.A random forest model was trained using 70%of the soil data,validated with leave-one-out cross-validation(LOOCV),and tested on the remaining 30%of the data.This process was repeated 100 times to achieve a high-precision model(R2val=0.94,RMSEval=1.92,RPDval=4.30).Analysis of the model's parameter importance revealed that hue is the key predictor for soil salinization,followed by saturation and brightness.关键词
土壤盐渍化/手机相片/颜色空间/随机森林Key words
soil salinization/phone photos/color space/random forest分类
天文与地球科学引用本文复制引用
钮一可,周宇菲,侍晓颖,王婉婷,许新怡,徐璐..基于手机相片和机器学习算法的滨海土壤盐分含量预测[J].热带农业工程,2025,49(1):23-28,6.基金项目
国家自然科学基金项目(No.42371053) （No.42371053）
大学生创新创业训练计划项目(No.202210320103Y) （No.202210320103Y）
大学生创新创业训练计划项目(No.XSJCX13011). （No.XSJCX13011）
