Machine learning techniques for non-destructive estimation of plum fruit weight
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...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-85051-2 |
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| author | Atefeh Sabouri Adel Bakhshipour Mehrnaz Poorsalehi Abouzar Abouzari |
| author_facet | Atefeh Sabouri Adel Bakhshipour Mehrnaz Poorsalehi Abouzar Abouzari |
| author_sort | Atefeh Sabouri |
| collection | DOAJ |
| description | 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 R2 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. |
| format | Article |
| id | doaj-art-e20e78a78a69479e8daa9e2a67808375 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e20e78a78a69479e8daa9e2a678083752025-01-05T12:19:46ZengNature PortfolioScientific Reports2045-23222025-01-0115112010.1038/s41598-024-85051-2Machine learning techniques for non-destructive estimation of plum fruit weightAtefeh Sabouri0Adel Bakhshipour1Mehrnaz Poorsalehi2Abouzar Abouzari3Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences, University of GuilanDepartment of Biosystems Engineering, Faculty of Agricultural Sciences, University of GuilanDepartment of Agronomy and Plant Breeding, Faculty of Agricultural Sciences, University of GuilanCrop and Horticultural Science Research Department, Mazandaran Agricultural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO)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 R2 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.https://doi.org/10.1038/s41598-024-85051-2Artificial intelligenceImage processingFruit dimensionsRegression models |
| spellingShingle | Atefeh Sabouri Adel Bakhshipour Mehrnaz Poorsalehi Abouzar Abouzari Machine learning techniques for non-destructive estimation of plum fruit weight Scientific Reports Artificial intelligence Image processing Fruit dimensions Regression models |
| title | Machine learning techniques for non-destructive estimation of plum fruit weight |
| title_full | Machine learning techniques for non-destructive estimation of plum fruit weight |
| title_fullStr | Machine learning techniques for non-destructive estimation of plum fruit weight |
| title_full_unstemmed | Machine learning techniques for non-destructive estimation of plum fruit weight |
| title_short | Machine learning techniques for non-destructive estimation of plum fruit weight |
| title_sort | machine learning techniques for non destructive estimation of plum fruit weight |
| topic | Artificial intelligence Image processing Fruit dimensions Regression models |
| url | https://doi.org/10.1038/s41598-024-85051-2 |
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