Market analysis on cocoa beans export: The case of Ghana and Cote d’Ivoire in West Africa

Gyan, John Kwame; Bajan, Bartłomiej

doi:10.17306/j.jard.2022.01646

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…By improving the detection of the maturity of cocoa pods, it may be possible to reduce the quantity of pods harvested and thus reduce the environmental impact of the cocoa industry. It is important to remember that Côte d'Ivoire is the largest cocoa producer in the world, with an annual production of more than 2 million tons of cocoa beans, and any system allowing the improvement of the quality of cocoa will significantly impact the cocoa industry worldwide [5]. In this context, detecting the maturity of cocoa pods is crucial for improving the quality of the final product [6].…”

Section: Introductionmentioning

confidence: 99%

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Classification of Cocoa Pod Maturity Using Similarity Tools on an Image Database: Comparison of Feature Extractors and Color Spaces

AYIKPA

Diarra

Gouton

et al. 2023

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Côte d’Ivoire, the world’s largest cocoa producer, faces the challenge of quality production. Immature or overripe pods cannot produce quality cocoa beans, resulting in losses and an unprofitable harvest. To help farmer cooperatives determine the maturity of cocoa pods in time, our study evaluates the use of automation tools based on similarity measures. Although standard techniques, such as visual inspection and weighing, are commonly used to identify the maturity of cocoa pods, the use of automation tools based on similarity measures can improve the efficiency and accuracy of this process. We set up a database of cocoa pod images and used two feature extractors: one based on convolutional neural networks (CNN), in particular, MobileNet, and the other based on texture analysis using a gray-level co-occurrence matrix (GLCM). We evaluated the impact of different color spaces and feature extraction methods on our database. We used mathematical similarity measurement tools, such as the Euclidean distance, correlation distance, and chi-square distance, to classify cocoa pod images. Our experiments showed that the chi-square distance measurement offered the best accuracy, with a score of 99.61%, when we used GLCM as a feature extractor and the Lab color space. Using automation tools based on similarity measures can improve the efficiency and accuracy of cocoa pod maturity determination. The results of our experiments prove that the chi-square distance is the most appropriate measure of similarity for this task.

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Section: Introductionmentioning

confidence: 99%

“…Step 4: Then, for each color channel, the function calculates these same properties for four co-occurrence matrices of different gray levels, which are obtained using different offsets (1,3,5,7). These offsets determine the distance between the pixels taken into account to calculate the co-occurrence matrix.…”

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confidence: 99%

Classification of Cocoa Pod Maturity Using Similarity Tools on an Image Database: Comparison of Feature Extractors and Color Spaces

AYIKPA

Diarra

Gouton

et al. 2023

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Optimization of Cocoa Pods Maturity Classification Using Stacking and Voting with Ensemble Learning Methods in RGB and LAB Spaces

Ayikpa,

Ballo,

Mamadou

et al. 2024

J. Imaging

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Determining the maturity of cocoa pods early is not just about guaranteeing harvest quality and optimizing yield. It is also about efficient resource management. Rapid identification of the stage of maturity helps avoid losses linked to a premature or late harvest, improving productivity. Early determination of cocoa pod maturity ensures both the quality and quantity of the harvest, as immature or overripe pods cannot produce premium cocoa beans. Our innovative research harnesses artificial intelligence and computer vision technologies to revolutionize the cocoa industry, offering precise and advanced tools for accurately assessing cocoa pod maturity. Providing an objective and rapid assessment enables farmers to make informed decisions about the optimal time to harvest, helping to maximize the yield of their plantations. Furthermore, by automating this process, these technologies reduce the margins for human error and improve the management of agricultural resources. With this in mind, our study proposes to exploit a computer vision method based on the GLCM (gray level co-occurrence matrix) algorithm to extract the characteristics of images in the RGB (red, green, blue) and LAB (luminance, axis between red and green, axis between yellow and blue) color spaces. This approach allows for in-depth image analysis, which is essential for capturing the nuances of cocoa pod maturity. Next, we apply classification algorithms to identify the best performers. These algorithms are then combined via stacking and voting techniques, allowing our model to be optimized by taking advantage of the strengths of each method, thus guaranteeing more robust and precise results. The results demonstrated that the combination of algorithms produced superior performance, especially in the LAB color space, where voting scored 98.49% and stacking 98.71%. In comparison, in the RGB color space, voting scored 96.59% and stacking 97.06%. These results surpass those generally reported in the literature, showing the increased effectiveness of combined approaches in improving the accuracy of classification models. This highlights the importance of exploring ensemble techniques to maximize performance in complex contexts such as cocoa pod maturity classification.

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A Review on Sustainability in Cocoa and Chocolate Processing: A Focus on West Africa

Akosua Saah Asiedu

2024

African Journal of Agriculture and Food Science

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Cocoa is traded internationally and is highly significant to most economies. The majority of cocoa beans are sourced from West Africa, exporting over 70 % of cocoa globally. Despite this, West Africa makes minimal profits as compared to the consumer countries controlling the chocolate industry. A lot of sustainability interventions have been rolled out to ensure the continuous production of the crop. Europe and other international organizations have invested largely in cocoa sustainability. Some interventions have been in the form of certification standards, sensitization and awareness programs, training and empowering farmers, particularly in the West African region. Ghana and Cote d’Ivoire have benefitted from a lot of sustainability programs as the world’s largest cocoa producers. This review delves into several sustainability initiatives implemented in Ghana and Cote d’Ivoire through Voluntary Sustainability Standards, local government regulations, and international organizations. It also highlights the importance of sustainability in ensuring a consistent supply of high-quality cocoa beans for processors and highlights its associated health benefits.

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Market analysis on cocoa beans export: The case of Ghana and Cote d’Ivoire in West Africa

Cited by 3 publications

References 12 publications

Classification of Cocoa Pod Maturity Using Similarity Tools on an Image Database: Comparison of Feature Extractors and Color Spaces

Classification of Cocoa Pod Maturity Using Similarity Tools on an Image Database: Comparison of Feature Extractors and Color Spaces

Optimization of Cocoa Pods Maturity Classification Using Stacking and Voting with Ensemble Learning Methods in RGB and LAB Spaces

A Review on Sustainability in Cocoa and Chocolate Processing: A Focus on West Africa

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