Application based on Hybrid CNN-SVM and PCA-SVM Approaches for Classification of Cocoa Beans

AYIKPA, Kacoutchy Jean; Diarra, Mamadou; Bakary, Ballo Abou; Gouton, Pierre; Jérôme, Adou Kablan

doi:10.14569/ijacsa.2022.0130927

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…1) SVM : A support vector machine (SVM) is a supervised machine learning algorithm [15] that can be used to solve classification and regression problems. SVMs are a generalization of linear classifiers.…”

Section: B Methodsmentioning

confidence: 99%

Cocoa Pods Diseases Detection by MobileNet Confluence and Classification Algorithms

MAMADOU,

AYIKPA,

BALLO

et al. 2023

IJACSA

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Cocoa cultivation is of immense importance to the people of Côte d'Ivoire. However, this culture is experiencing significant challenges due to diseases spread by various agents such as bacteria, viruses, and fungi, which cause considerable economic losses. Currently, the methods available to detect these cocoa diseases force farmers to seek the expertise of agronomists for visual inspections and diagnostics, a laborious and complex process. In the search for solutions, many studies have opted for using convolutional neural networks (CNNs) to identify diseases in cocoa pods. However, an essential advance is to develop hybrid approaches that combine the advantages of a CNN with sophisticated classification algorithms. This research stands out for its innovative contribution, combining MobileNetV2, a convolutional neural network architecture, with algorithms, such as Logistic Regression (LR), K Nearest Neighbors (KNN), Support Vector Machines (SVM), XGBoost, and Random Forest. The study was conducted in two distinct phases. First, each algorithm was evaluated individually, and then performance was measured when MobileNetV2 was merged with the algorithms mentioned. These hybrid approaches complement and amplify MobileNetV2's capabilities. To do so, they draw on MobileNetV2's inherent capabilities to extract key features and enhance information quality. By combining this expertise with the classification methods of these other models, hybrid approaches outperform individual techniques. Accuracy rates range from 72.4% to 86.04%.This performance amplitude underlines the effectiveness of the synergy between the extraction characteristics of MobileNetV2 and the classification skills of other algorithms.

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Section: B Methodsmentioning

confidence: 99%

Cocoa Pods Diseases Detection by MobileNet Confluence and Classification Algorithms

MAMADOU,

AYIKPA,

BALLO

et al. 2023

IJACSA

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“…Additionally, it permits the transformation of a set of correlated variables denoted as X into a smaller number, y, specifically the uncorrelated variable known as the principal component, where y<X, while retaining the variability inherent in the original data. Among the features of PCA is its capability to compress images, reducing their size while preserving maximum quality [17], [18]. The steps involved in PCA are outlined as follows.…”

Section: Principal Component Analysismentioning

confidence: 99%

Deep convolutional neural network for Lampung character recognition

Bintoro,

Zulkifli,

Fitriana

et al. 2024

Bulletin EEI

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Recognition of document based, and handwritten characters has recently emerged as highly relevant field of study in the field of digital image processing. The ability to read and write Lampung script is a crucial competency as it helps preserve the language, which is a part of Indonesian culture. This research utilizes data obtained from classified documents and handwritten samples, categorized into eight types. To recognize Lampung characters, deep convolutional neural network (DCNN) architecture is proposed. The novelty of this architecture lies in optimizing document-based and handwritten character recognition to achieve the best performance in terms of accuracy and execution time. The proposed architecture will be compared to principal component analysis (PCA) combined with support vector machine (SVM) to evaluate its results. Experimental results using the DCNN architecture show an average accuracy of 99.3% and an execution time of 283 seconds for all data, while PCA and SVM exhibit an average accuracy of 92.9%. Furthermore, the recognition results for all data from documents and handwritten samples yield satisfactory accuracy of 98.6%. These results make the DCNN architecture suitable for use in recognizing Lampung characters and are expected to make it easier for Lampung people to recognize Lampung character.

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“…This approach streamlines quality assessment, improves efficiency, and reduces costs without sample destruction. Classification of cacao variety 1 [3] Quality Assessment Morphological features 15 [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] Fermentation Fermentation level 8 [26,27,[29][30][31][32][33]35] Fermentation index 1 [28] Cacao Bean's Roasting Degree 2 [34,36] [36] demonstrated continuous monitoring of volatile compounds during cocoa refining using a gas sensor system. Trained Kernel Distribution Models (KDMs) characterized volatile profiles, identifying deviations in processing variables.…”

Section: Fermentationmentioning

confidence: 99%

A review of machine learning applications in Cacao Post-harvest management

Zhaira Marianne Ferrancol Ferran,

Luisa Ann Tamayo Ongquit,

Edwin Romeroso Arboleda

2024

Int. J. Sci. Res. Arch.

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Cacao beans play a crucial role in chocolate production, making them a highly relevant crop. The post-harvesting stage of cacao beans, encompassing classification, quality assessment, and fermentation, holds significant importance. With the increasing demand for marketable cacao beans, the need for reliable, accurate, and fast technologies has emerged. This study provides a comprehensive review of machine learning techniques in the post-harvesting stage of cacao beans from 2016 to the present. Analyzing 36 studies, it focuses on classification, quality assessment, and fermentation. The proposed framework includes the application domain, learning algorithms, performance metrics, and reported impacts. Notably, it explores various machine learning applications like classification, quality assessment, and fermentation, highlighting commonly used algorithms like ANN, CNN, and SVM. In terms of performance metrics, GLCM achieved the highest accuracy (99.61%) in cacao classification, ANFIS excelled in quality assessment (99.715%), and k-NN emerged as the most accurate for fermentation. This review serves as a valuable resource for researchers in the cacao bean sector, offering insights into machine learning advancements.

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Application based on Hybrid CNN-SVM and PCA-SVM Approaches for Classification of Cocoa Beans

Cited by 6 publications

References 18 publications

Cocoa Pods Diseases Detection by MobileNet Confluence and Classification Algorithms

Cocoa Pods Diseases Detection by MobileNet Confluence and Classification Algorithms

Deep convolutional neural network for Lampung character recognition

A review of machine learning applications in Cacao Post-harvest management

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