Prediction method of sugarcane important phenotype data based on multi-model and multi-task.

Abstract

The efficacy of generalized sugarcane yield prediction models holds significant implications for global food security. Given that machine learning algorithms often surpass the precision of remote sensing technology, further exploration of machine learning algorithms in the development of sugarcane yield prediction models is imperative. In this study, we employed six key phenotypic traits of sugarcane, specifically plant height, stem diameter, third-node length (internode length), leaf length, leaf width, and field brix, along with eight machine learning methods: logistic regression, linear regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Backpropagation Neural Network (BPNN), Decision Tree, Random Forest, and the XGBoost algorithm. The aim was to establish an intelligent model ensemble for predicting two crucial phenotypic characteristics-stem diameter and plant height-that determine sugarcane yield, ultimately enhancing the overall yield.The experimental findings indicate that the XGBoost algorithm outperforms the other seven algorithms in predicting these significant phenotypic traits of sugarcane. Furthermore, an analysis of the sugarcane intelligent prediction model's performance under a specialized data environment, incorporating self-prepared data, reveals that the XGBoost algorithm exhibits greater stability. Notably, the data pertaining to these crucial phenotypic traits have a profound impact on the efficacy of the intelligent models. The research demonstrates that a sugarcane yield prediction model ensemble, incorporating multiple intelligent algorithms, can accurately forecast stem diameter and plant height, thereby predicting sugarcane yield. Additionally, this approach, combined with the principles of sugarcane cross-breeding, provides a valuable reference for the artificial breeding of new sugarcane varieties that excel in stem diameter and plant height, bridging a research gap in indirect yield prediction through sugarcane phenotypic traits.

Plant phenotyping relevance

サトウキビの茎径・草丈という植物形質を、複数の機械学習手法で予測・比較する方法論が研究の中心であるため。

Code and data availability

The paper's sugarcane phenotype datasets (555 samples, six traits) and ML models are described but not publicly deposited; the Data Availability Statement states all relevant data are within the manuscript, and no code, model, or dataset URL is provided.

No evidence-backed public reproduction asset is currently recorded.