The use of machine learning methods in classification of pumpkin seeds (Cucurbita pepo L.)

Köklü, Murat; Sarigil, Seyma; Özbek, Osman

doi:10.1007/s10722-021-01226-0

Cited by 35 publications

(19 citation statements)

References 28 publications

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“…Four different classification models were built using Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) [ 38 , 39 , 40 ], and CatBoost; the models were parameterized using empirical settings, as shown in Table 7 . To ensure the reliability of the experimental results and to avoid the chance of single experimental results, several experiments were conducted on different models.…”

Section: Resultsmentioning

confidence: 99%

An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries

Chang

Wang

Yang

et al. 2023

Sensors

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Selecting the best planting area for blueberries is an essential issue in agriculture. To better improve the effectiveness of blueberry cultivation, a machine learning-based classification model for blueberry ecological suitability was proposed for the first time and its validation was conducted by using multi-source environmental features data in this paper. The sparrow search algorithm (SSA) was adopted to optimize the CatBoost model and classify the ecological suitability of blueberries based on the selection of data features. Firstly, the Borderline-SMOTE algorithm was used to balance the number of positive and negative samples. The Variance Inflation Factor and information gain methods were applied to filter out the factors affecting the growth of blueberries. Subsequently, the processed data were fed into the CatBoost for training, and the parameters of the CatBoost were optimized to obtain the optimal model using SSA. Finally, the SSA-CatBoost model was adopted to classify the ecological suitability of blueberries and output the suitability types. Taking a study on a blueberry plantation in Majiang County, Guizhou Province, China as an example, the findings demonstrate that the AUC value of the SSA-CatBoost-based blueberry ecological suitability model is 0.921, which is 2.68% higher than that of the CatBoost (AUC = 0.897) and is significantly higher than Logistic Regression (AUC = 0.855), Support Vector Machine (AUC = 0.864), and Random Forest (AUC = 0.875). Furthermore, the ecological suitability of blueberries in Majiang County is mapped according to the classification results of different models. When comparing the actual blueberry cultivation situation in Majiang County, the classification results of the SSA-CatBoost model proposed in this paper matches best with the real blueberry cultivation situation in Majiang County, which is of a high reference value for the selection of blueberry cultivation sites.

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Section: Resultsmentioning

confidence: 99%

An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries

Chang

Wang

Yang

et al. 2023

Sensors

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“…Matrix of confusion generated using data of validation of the LDA model. 1 1 = Orelha-de-Vó; 2 = Fava-Rajada-Preta; 3 = Espírito-Santo-Vermelho; 4 = Espírito-Santo-Marrom; 5 = Fava-Rajada; 6 = Fava-Amarela; 7 = Mulatinha; 8 = Fígado-de-Frango; 9 = Fava-Manteiga; 10 = Fava-S. Koklu, Sarigil and Ozbek (2021) used a confusion matrix to show the success of a model used for classification of pumpkin (Cucurbita pepo L.) seeds. In addition, Altuntas, Comert and Kocamaz (2019) used the same classification to predict the performance of CNN models on the identification of haploid and diploid maize seeds.…”

Section: Resultsmentioning

confidence: 99%

CLASSIFICATION OF Phaseolus lunatus L. USING IMAGE ANALYSIS AND MACHINE LEARNING MODELS

et al. 2022

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Image analysis combined with machine learning models can be an excellent tool for classification of fava (Phaseolus lunatus L.) genotypes and is a low-cost system. Fava is grown by family farmers, mainly, in the Northeast and South regions of Brazil, presenting economic and social importance. Evaluations to gather information on qualitative and quantitative characters of seeds enable the description and distinction of genotypes, allowing the evaluation of variability of plant species, which is essential in breeding programs. The use of image analysis is a fast and economic tool for obtaining large quantity of information. Machine learning techniques have been developed and implemented in the agricultural sector due to technological advances and increasing use of artificial intelligence, which enables the automatization of several processes. In this context, the objective of this work was to evaluate different machine learning models to classify fava genotypes, using data obtained through image analysis. Images of fava seeds were captured using a table scanner (HP Scanjet 2004), set to true color mode, arranged upside down inside of an aluminum box fully closed during the capture of the images for an adequate illumination and prevention of environmental noises. The K-Nearest Neighbor, Naive Bayes, Linear Discriminant Analysis, Support Vector Machine, Gradient Boosting, Bootstrap Aggregating, Classification and Regression Trees, Random Forest, and C50 models were used for the study. Linear Discriminant Analysis was the model that presented the highest efficiency for classifying the genotypes, with an accuracy of 90%.

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“…Köklü ve arkadaşlarının (2021) yaptıkları çalışmada, kabak çekirdeklerini sınıflandırılması için makine öğrenmesi yöntemlerinden LR, DVM, ROF ve KNN algoritmalarını kullanmışlardır. Deneysel değerlendirmelerin sonucunda en iyi başarının DVM yöntemi ile olduğunu bildirmişlerdir [32]. Bu çalışmada, Türkiye'de elde edilmiş Besni ve Keçimen cinsi kuru üzümleri sınıflandırılması için hibrit yığınlanmış oto kodlayıcı (YOK) ve rotasyon orman (RO) algoritmasının (YOK-RO) önerilmektedir.…”

Section: Introductionunclassified

Kurum Üzüm Tanelerinin Sınıflandırılması İçin Hibrit Bir Yaklaşım

Kılıçarslan

2022

Mühendislik Bilimleri Ve Araştırmaları Dergisi

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Raisin grains are a very good source of energy and nutrients due to the minerals and vitamins they contain. Since raisins constitute 23% of the world in Turkey, it is important to determine the variety and quality. Traditionally, deciding on the type and quality of raisin grains is a long and costly process. In addition, experience and knowledge are important in determining the type of raisin grains. In addition, since each of the experts has different experience, knowledge and expertise, there is a difference in terms of classification of raisins. Therefore, it is important to develop a decision support system with data mining methods for the correct classification of raisin grains. In this study, we propose one hybrid model using rotation forest (RO) and deep learning algorithms of Stacked Autoencoder (SAE) for the prediction of the type of raisin grains. As a result of the experimental evaluation, the hybrid SAE-ROF method has achieved a high success rate of 91.50% in terms of performance from the classical data mining methods and deep learning methods used in the study.

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The use of machine learning methods in classification of pumpkin seeds (Cucurbita pepo L.)

Cited by 35 publications

References 28 publications

An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries

An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries

CLASSIFICATION OF Phaseolus lunatus L. USING IMAGE ANALYSIS AND MACHINE LEARNING MODELS

Kurum Üzüm Tanelerinin Sınıflandırılması İçin Hibrit Bir Yaklaşım

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