An intelligent cocoa quality testing framework based on deep learning techniques

Essah, Richard; Anand, Darpan; Singh, Surender

doi:10.1016/j.measen.2022.100466

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…Deep-learning image classifiers can now be used in the early diagnosis of plant diseases because of advancements in computing power. There is a considerable amount of literature that has been published that claims very accurate levels of performance on newly developed image classifiers in the quest to enhance existing models for plant disease identification [20]. However, a significant proportion of this literature lacks a set of predefined methodologies, making comparisons between works challenging.…”

Section: Discussionmentioning

confidence: 99%

Plant Disease Prognosis Using Spatial-Exploitation-Based Deep-Learning Models

Vankara,

Nandini,

Muddada

et al. 2023

RAiSE-2023

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Section: Discussionmentioning

confidence: 99%

Plant Disease Prognosis Using Spatial-Exploitation-Based Deep-Learning Models

Vankara,

Nandini,

Muddada

et al. 2023

RAiSE-2023

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“…Sistem inspeksi cacat pada permukaan kayu dengan model deteksi objek YOLOv5 [2]. Penelitian serupa juga dilakukan oleh dengan menggunakan berbagai macam metode antara lain integrasi antara YOLOv5 dan ResNet [7], deep learning [8,9], integrasi antara machine learning dan deep learning [10], FE-YOLOv5 [11], serta YOLOv3 [12]. Selain itu, Aplikasi untuk mendeteksi kualitas daging dikembangkan dengan mengaplikasikan region of interest (ROI) [13].…”

Section: Pendahuluanunclassified

Deteksi Cacat pada Sekrup Berbasis Citra Menggunakan YOLOv5

2024

jikstik

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Perusahaan pasti menginginkan produk yang dihasilkannya berkualitas baik. Kenyataannya kecacatan produk merupakan hal yang sulit dihindari. Kegiatan pengendalian kualitas diperlukan untuk mencegah produk cacat sampai kepada pelanggan. Pengendalian kualitas yang dilakukan manual membutuhkan waktu yang lama dengan tingkat subjektias serta resiko human error yang tinggi. Pemanfaatan teknologi dibutuhkan untuk membantu kegiatan pengendalian kualitas. Teknologi yang dapat dimanfaatkan untuk pengendalian kualitas adalah Articial Intelligence (AI) dengan metode deep learning menggunakan arsitektur YOLOv5. Tujuan penelitian ini adalah mengembangkan model yang dapat mendeteksi kualitas sekrup berbasis citra. Pengolahan data dilakukan dengan Roboow untuk proses preprocessing. Proses pembuatan model menggunakan Google Colab dengan bahasa pemrograman python. Hasil penelitian menunjukkan pelatihan mendapatkan waktu pelatihan sebesar 0.404 jam atau 24.24 menit, precision 0.842, recall 0.857, dan mean average precision 0.887. Uji coba yang dilakukan menghasilkan bahwa citra dapat terdeteksi dengan baik. Namun, terdapat beberapa citra yang kurang baik dan maksimal untuk dideteksi.

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“…As a result, using cutting-edge technologies, such as Spectral Analysis combined with Machine Learning and Optimization through the genetic algorithm, leads to a radical transformation of how we evaluate and classify cocoa beans [5].…”

Section: Introductionmentioning

confidence: 99%

Classification of Cocoa Beans by Analyzing Spectral Measurements Using Machine Learning and Genetic Algorithm

Ayikpa,

Gouton,

Mamadou

et al. 2024

J. Imaging

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The quality of cocoa beans is crucial in influencing the taste, aroma, and texture of chocolate and consumer satisfaction. High-quality cocoa beans are valued on the international market, benefiting Ivorian producers. Our study uses advanced techniques to evaluate and classify cocoa beans by analyzing spectral measurements, integrating machine learning algorithms, and optimizing parameters through genetic algorithms. The results highlight the critical importance of parameter optimization for optimal performance. Logistic regression, support vector machines (SVM), and random forest algorithms demonstrate a consistent performance. XGBoost shows improvements in the second generation, followed by a slight decrease in the fifth. On the other hand, the performance of AdaBoost is not satisfactory in generations two and five. The results are presented on three levels: first, using all parameters reveals that logistic regression obtains the best performance with a precision of 83.78%. Then, the results of the parameters selected in the second generation still show the logistic regression with the best precision of 84.71%. Finally, the results of the parameters chosen in the second generation place random forest in the lead with a score of 74.12%.

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An intelligent cocoa quality testing framework based on deep learning techniques

Cited by 9 publications

References 11 publications

Plant Disease Prognosis Using Spatial-Exploitation-Based Deep-Learning Models

Plant Disease Prognosis Using Spatial-Exploitation-Based Deep-Learning Models

Deteksi Cacat pada Sekrup Berbasis Citra Menggunakan YOLOv5

Classification of Cocoa Beans by Analyzing Spectral Measurements Using Machine Learning and Genetic Algorithm

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