Sabouri Atefeh, Bakhshipour Adel, Poorsalehi Mehrnaz, Abouzari Abouzar
Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.
Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.
Sci Rep. 2025 Jan 4;15(1):751. doi: 10.1038/s41598-024-85051-2.
Plum fruit fresh weight (FW) estimation is crucial for various agricultural practices, including yield prediction, quality control, and market pricing. Traditional methods for estimating fruit weight are often destructive, time-consuming, and labor-intensive. In this study, we addressed the problem of predicting plum FW using artificial intelligence (AI) methods based on fruit dimensions. We aimed to evaluate various machine learning (ML) techniques for this purpose. Images of fruit samples were captured using a smartphone camera, processed to extract binary images, and used to calculate dimensions. We tested several ML methods, including Support Vector Regression (SVR), Multivariate Linear Regression (MLR), Multi-Layer Perceptron (MLP), and Decision Tree (DT). The SVR model with a Pearson-VII kernel (PUK) function and penalty value (c) of 0.1 was the most accurate, achieving an R of 0.9369 and root mean squared error (RMSE) of 0.4850 (gr) during training, and 0.9267 and 0.4863 (gr) during testing. This method is important for researchers and practitioners seeking efficient, quick, and non-destructive ways to estimate fruit weight. Future research can build on these findings by applying the model to other fruit types and conditions.
李果实鲜重（FW）估计对于各种农业实践至关重要，包括产量预测、质量控制和市场定价。传统的果实重量估计方法通常具有破坏性、耗时且劳动强度大。在本研究中，我们解决了基于果实尺寸使用人工智能（AI）方法预测李果实鲜重的问题。为此，我们旨在评估各种机器学习（ML）技术。使用智能手机摄像头拍摄果实样本图像，对其进行处理以提取二值图像，并用于计算尺寸。我们测试了几种ML方法，包括支持向量回归（SVR）、多元线性回归（MLR）、多层感知器（MLP）和决策树（DT）。具有Pearson - VII核（PUK）函数且惩罚值（c）为0.1的SVR模型最为准确，在训练期间R值为0.9369，均方根误差（RMSE）为0.4850（克），在测试期间R值为0.9267，RMSE为0.4863（克）。该方法对于寻求高效、快速且无损的果实重量估计方法的研究人员和从业者而言非常重要。未来的研究可以通过将该模型应用于其他果实类型和条件来基于这些发现展开。
