ArticleScientific reports2025
Machine learning techniques for non-destructive estimation of plum fruit weight.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Precise 2D vision solutions for estimating avocado physical characteristics.Scientific reports · 2025Pooled it
- Morphological and Pomological Characterization of Indigenous Pear Genotypes (Food science & nutrition · 2026Article
- Review
- Non-Destructive Detection of Internal Quality of Sanhua Plum Based on Multi-Source Information Fusion.Foods (Basel, Switzerland) · 2026Article
- High precision banana variety identification using vision transformer based feature extraction and support vector machine.Scientific reports · 2025Article
- Identifying graft incompatible rootstocks for sweet cherry through machine learning algorithms.PloS one · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.