An integrated learning algorithm for early prediction of melon harvest.

Abstract

Different modeling techniques must be applied to manage production and statistical estimation to predict the expected harvest. By calculating advanced production methods and the rational valuation of different factors, we can accurately capture the variety of growth characteristics and the expected yield. This paper obtained 32 feature variables related to melons, including phenological features, shape features, and color features. The Gradient Boosted Decision Tree (GBDT) network and the Grid Search (GS) hyperparameter seeking method was applied to calculate the degree of importance of all melon fruits' characteristics and construct prediction models for three expected harvest indexes of melon yield, sugar content, and endocarp hardness. To facilitate growers to carry out prediction and estimation in the field without destroying the melon fruits. The reduced feature variables were selected as inputs. The GBDT model was used to provide a significant advantage in prediction compared to both Random Forest (RF) and Support Vector Regression (SVR) methods. In addition, to verify the feasibility of using only reduced feature variables as input for the evaluation work, this study also compares the predictive effects of the model when all feature variables and only reduced feature variables are used. The GBDT prediction model proposed in this paper predicted melon yield, sugar content, and hardness using reduced features as input, and the model R2 could reach more than 90%. Therefore, this method can effectively help growers carry out early non-destructive inspection and growth prediction of melons in the field.

Plant phenotyping relevance

メロンの形態・色・生育特徴から収量、糖度、硬度を非破壊推定する学習モデルが研究の中心であり、植物形質の計算的推定手法に該当する。

Code and data availability

The paper's melon phenotype measurements (255 samples, 32 phenological/shape/color features with yield, sugar content, hardness) are explicitly restricted and available only from the corresponding author on request. No public dataset, code, or model deposit is mentioned.

No evidence-backed public reproduction asset is currently recorded.