基于图像特征与机器学习的烤烟烟叶产量预测方法

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中图分类号：S572；S126 文献标识码：A文章编号：1007-5119（2025）04-0087-11

Abstract： To explore the feasibilityof predicting flue-cured tobacco yield based on RGB images in combination with machine learninganddeepleamingalgorithms，afieldexperimentwascariedoutusingflue-curedtobaco Zhongchuan208.RGBimagesof flue-cured tobacco wereobtaied 25days aftertopping using drones.Color，textureandshapeoftheimages were extracted，totaling 35 features.Featuresselection were performed using therandom forest algorithm，and a yield prediction model was constructed using seven machineleaing algorits （BPNN，GA-BPNN，ELM，PSO-ELM，SVR，GA-SVR，RF）andonedeeleaingalgorithm（1DCNN）. The results showed as the follows：The accuracy （R2=0.970） and generalization ability （R2=0.817） of the random forest predictionmodelestablishedbythecombinationoffeatures（color，shape，andtexture）obtainedthroughtherandomforestalgorithm are higher hanthoseof te othersix machinelearning modelsand theconvolutional neuralnetwork model.The predictedyieldare in goodaccordance withthe measuredvalues.Insummaryweconstructedatobaco yieldpredictionmodelthroughthecombinationof feature selection and the random forest algorithm and provided novel tools for tobacco yield prediction.

Keywords： flue-cured tobacco; image features; machine learning;1Dconvolutional neural network; yield prediction

作物产量是农业生产过程中最终的数量特征[1]传统产量测定往往需要采收后才能统计，无法及时反馈生产，指导规划，而产量预测可为作物管理和最终产量估计提供参考。（剩余17165字）