Seed Architectural Phenes Prediction and Variety Classification of Dry Beans (Phaseolus vulgaris) Using Machine Learning Algorithms

Mendigoria, Christan Hail; Concepcion, Ronnie; Dadios, Elmer P.; Aquino, Heinrick; Alaias, Oliver John; Sybingco, Edwin; Vicerra, Ryan Rhay P.; Cuello, Joel L.

doi:10.1109/r10-htc53172.2021.9641554

Cited by 11 publications

(2 citation statements)

References 24 publications

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“…For example, in 2022, Shahoveisi et al used traditional machine learning methods to model the risk of disease development caused by sclerotinia sclerotiorum on rapeseed and dry beans [3]. In 2021, Mendigoria predicted the morphological characteristics and variety classification of dry beans through traditional machine learning methods [4].…”

Section: Introductionmentioning

confidence: 99%

Clustering and Classification for Dry Bean Feature Imbalanced Data

Lee,

Wang,

Huang

2024

Preprint

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The dry bean dataset of this study uses the imbalanced data set of the University of California, Irvine (UCI) machine learning warehouse platform. The dataset consists of 13,611 data points, including 16 explanatory variables and 1 target variable (Class). This paper proposes clustering and classification of dry beans based on K-means combined with machine learning methods such as decision tree (DT), random forest (RF) and support vector machine (SVM). The key idea is that K-means performs clustering first, and then imports the clustering results into DT, RF, and SVM to obtain the classification accuracy and compares it with traditional machine learning methods to improve the classification accuracy of dry bean imbalanced data. The experimental result is shown that the proposed method found K-means + SVM to have good classification accuracy on the dry bean dataset. This study further explored the 11 decision rules obtained in K-means + DT. In the ranking of explanatory variable importance in K-means + RF, it was found that the top three were Compactness, ShapeFactor3, and ShapeFactor1. Therefore, the proposed method of first clustering and then classifying can be used as an effective means to analyze dry bean imbalanced data.

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Section: Introductionmentioning

confidence: 99%

Clustering and Classification for Dry Bean Feature Imbalanced Data

Lee,

Wang,

Huang

2024

Preprint

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“…Different studies have used machine learning and image processing to classify seeds for different applications including classification of healthy seeds from bad ones. Studies by [1][2][3][4] used colour thresholding to segment images to extract certain features from seed images including contrast, correlation and entropy, and studies by [3] [5-6] used morphological features such as area and perimeter. Using the data from the extracted features, they use algorithms such as K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Support Vector Machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

Classification of Lactuca Sativa Seed Varieties Using Spectral- Textural-Spatial Features and Decision Tree Classifier

2023

Proceedings of 2023 the 13th International Workshop on Computer Science and Engineering

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It is important to identify the variety of a crop before planting the seed. Identifying lettuce seed varieties manually can be a tough process. This study focuses on creating a system that can classify lettuce seed images using machine learning. Three Lettuce varieties were considered in this study, including the Romaine, Black Seeded Simpson, and Lollo Rosso. 100 seed images were captured for each lettuce seed variety in a controlled environment. The seed images were segmented using color thresholding in the HSV color space. Color, texture, and morphological features were used for variety classification including RGB color features, Solidity, Correlation, Compactness, Perimeter, Entropy. Decision Tree Classification algorithm was used to classify lettuce seeds. The system was able to successfully classify lettuce seed varieties with an average accuracy of 92%.

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