Traditional Machine Learning-Based Classification of Cashew Kernels Using Colour Features

Yıl 2023, Cilt: 20 Sayı: 1, 115 - 124, 19.01.2023

Geofrey Prudence Baitu , Omsalma Alsadig Adam Gadalla , Y. Benal Öztekin

https://doi.org/10.33462/jotaf.1100782

Cited By: 4

Öz

Cashew is one of the major commercial commodities contributing to the national economy of Tanzania as foreign revenue. And yet still the processing of cashew is run locally using manual labour for a big part. If processed well under ideal conditions, cashews kernels are expected to be white in colour. But due to various factors like prolonged roasting in the steam chambers or over-drying, some cashew kernels tend to have a slight brown colour, and these are referred to as scorched cashews. Despite sharing the same characteristics with white cashew kernels, including nutritional quality, these cashew kernels are supposed to be graded differently. In many places around the world, particularly in Tanzania, the sorting and grading process of cashew kernels is performed by hand. In international trade, cashew grading is very important and this means more effective and consistent methods need to be applied in this stage of production in order to increase the quality of the products. The objective of this study was to evaluate the use of traditional Machine Learning techniques in the classification of cashew kernels as white or scorched by using colour features. In this experiment, various colour features were extracted from the images. The extracted features include the means (μ), standard deviations (σ), and skewness (γ) of the channels in RGB and HSV colour spaces. The relevant features for this classification problem were selected by applying the wrapper approach using the Boruta Library in Python, and the irrelevant ones were removed. 5 models are studied and their efficiencies analysed. The studied models are Logistic Regression, Decision Tree, Random Forest, Support Vector Machine and K-Nearest Neighbour. The Decision Tree model recorded the least accuracy of 98.4%. The maximum accuracy of 99.8% was obtained in the Random Forest model with 100 trees. Due to simplicity in application and high accuracy, the Random Forest is recommended as the best model from this study.

Anahtar Kelimeler

Logistic Regression, Decision Tree, Random Forest, Cashews, Support Vector Machine K-Nearest Neighbour

Destekleyen Kurum

Ondokuz Mayis University

Proje Numarası

PYO.ZRT.1904.22.010

Traditional Machine Learning-Based Classification of Cashew Kernels Using Colour Features

Öz

Kaju, Tanzanya'nın ülke ekonomisine dış gelir olarak katkı sağlayan başlıca ticari ürünlerden biridir. Kaju çekirdeklerinin işlenmesi, halen büyük ölçüde el emeği kullanılarak yerel olanaklarla yapılmaktadır. İdeal koşullarda iyi işlenirse kajuların beyaz renkte olması beklenir. Ancak, buhar odalarında uzun süre kavurma veya aşırı kurutma gibi çeşitli faktörler nedeniyle, bazı kaju çekirdekleri hafif kahverengi bir renge dönüşebilmektedir. Renk değiştirmiş bu kajulara kavrulmuş kaju denir. Besin kalitesi de dahil olmak üzere beyaz kaju çekirdekleri ile aynı özelliklere sahip olmasına rağmen, renk ve görünüm tüketicilerin kalite algısını etkilediği için bu kaju çekirdeklerinin ayrılması gerekmektedir. Tanzanya başta olmak üzere dünyanın pek çok yerinde kaju çekirdeklerinin ayırma ve sınıflandırma işlemi elle yapılmaktadır. Uluslararası ticarette, kaju sınıflandırması çok önemli olup ürün kalitesini artırmak için üretimin bu aşamasında daha etkili ve tutarlı yöntemlerin uygulanması gerektiği anlamına gelir. Bu çalışmanın amacı, kaju çekirdeklerinin beyaz veya kavrulmuş olarak sınıflandırılmasında renk özellikleri kullanılarak geleneksel Makine Öğrenmesi tekniklerinin kullanımının değerlendirilmesidir. Bu çalışmada, görüntülerden farklı renk özellikleri çıkarılmıştır. Çıkarılan özellikler, RGB ve HSV renk uzaylarında kanalların ortalamaları (μ), standart sapmaları (σ) ve çarpıklığını (γ) içerir. Python'da Boruta Kütüphanesi kullanılarak sarmal (wrapper) yöntemi uygulanarak bu sınıflandırma problemi için ilgili özellikler seçilmiş ve ilgili olmayanlar çıkarılmıştır. Bu çalışmada 5 model çalışılmış ve verimlilikleri analiz edilmiştir. Değerlendirme teknikleri Lojistik Regresyon, Karar Ağacı, Rastgele Orman, Destek Vektör Makinesi ve K-En Yakın Komşu (KNN) yöntemleridir. Karar Ağacı modeli, %98,4 ile en düşük doğruluğu vermiştir. 100 ağaçlı Rastgele Orman modelinde maksimum %99,8 doğruluk elde edilmiştir. Uygulamadaki basitliği ve yüksek doğruluğu nedeniyle Rastgele Orman bu çalışma için en iyi model olarak önerilmektedir.

Anahtar Kelimeler

Lojistik Regresyon, Karar Ağacı, Rastgele Orman, Destek Vektör Makinesi ve K-En Yakın Komşu (KNN), Kaju fıstığı

Proje Numarası

PYO.ZRT.1904.22.010

Kaynakça

• Ahmadabadi, H.N,. Omid, M., Mohtasebi, S.S,, Firouz, M.S. (2017). Design, development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine. Information Processing in Agriculture, 4(4): 333-341.
• Aran, M., Nath, G,A., Shyna, A. (2016). Automated Cashew Kernel Grading Using Machine Vision. 2016 International Conference on Next Generation Intelligent Systems (ICNGIS). 1-3 September, P.1-5. Kottayam, India.
• Babu, C.S., Thota, L.S., Rao, A.A., Hanuman, T., Rambabu, P., Sankar. Y.V.P., Maroju. S.P., Seshagiri. A., Medisetty. R., Al-Ahmari. A.M., Mesrie. A., Al-Shehri, A., Al-Hanif, A.K.A. (2012). Intelligent model to classify cashew kernels. International Journal of Engineering and Innovative Technology, 2(6): 294-302.
• Catarino, L., Menezes, Y., Sardinha, R. (2015). Cashew cultivation in Guinea-Bissau – risks and challenges of the success of a cash crop. Scientia Agricola, 72(5): 459-467.
• Dong, Y., Li, M., Sun, Y. (2013). Research on threshold segmentation algorithms. Advanced Materials Research, 860-863: 2888-2891.
• Du, C.J., He, H.J., Sun, D.W. (2016). Chapter 4 - Object Classification Methods. Computer Vision Technology for Food Quality Evaluation. Academic Press, San Diego.
• Faria, J. (2021). Production Volume of Cashew Nuts in Tanzania from The Crop Season 2014/15 to The Crop Season 2019/20. https://www.statista.com/statistics/1184534/production-volume-of-cashew-nuts-in-tanzania/ (Accessed date: 07.01.2022) from Statista.
• Ganganagowdar, N.V., Siddaramappa, H.K. (2011a). Cashew kernels classification using colour features. International Journal of Machine Intelligence, 3(2): 52-57.
• Ganganagowdar, N.V., Siddaramappa, H. K. (2011b). Cashew kernels classification using texture features. International Journal of Machine Intelligence, 3: 45-51
• Ganganagowdar, N.V., Siddaramappa, H.K. (2016). Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks. Acta Scientiarum. Agronomy, 38: 145-155.
• Karcık, H., Taşan, M. (2018). Determination of heavy metal contents in some organic certified dried nuts. Journal of Tekirdag Agricultural Faculty, 15(2): 101-111.
• Kilanko, O., Ojolo, S.J., Leramo, R.O., Ilori, T.A., Oyedepo, S.O., Babalola, P.O., Fayomi, O.S., Onwordi, P.N., Ufot, E., Ekwere, A. (2020). Dataset on physical properties of raw and roasted cashew nuts. Data in Brief, 33: 106514
• Kumar, J.A., Rao, P.R., Desai, A.R. (2013). Cashew kernel classification using machine learning approaches. Journal of the Indian Society of Agricultural Statistics, 67(1): 121-129.
• Mehak, A., Veena, D. (2018). A machine vision-based approach to cashew kernel grading for efficient industry grade application. International Journal of Advance Research, Ideas and Innovations in Technology, 4(6): 865-871.
• Muniz, C.R., Freire, F.C., Lemos, É., Pinto, G.A., Figueiredo, E.A., Figueiredo, R.W. (2006). Effect of processing conditions on the microbiological quality of cashew nuts. Brazilian Journal of Food Technology, 9(1): 33-38.
• Nadar, W., Kundargi, J.M. (2018). Classification of cashew based on the shape parameter. International Journal of Engineering Research and Technology, 2(4): 159-163.
• Nagpure, V., Joshi, K. (2016). Grading of cashew nuts on the bases of texture, color and size. International Journal on Recent and Innovation Trends in Computing and Communication, 4(4): 171–173.
• Özpınar, S., Çay, A. (2018). The role of agricultural mechanization in farming system in a continental climate. Journal of Tekirdag Agricultural Faculty 15(2): 58-72.
• Sunoj, S., Igathinathane, C., Jenicka, S. (2018). Cashews whole and splits classification using a novel machine vision approach. Postharvest Biology and Technology, 138: 19-30.
• Thakkar, M., Bhatt, M., Bhensdadia, C.K. (2011). Performance evaluation of classification techniques for computer vision based cashew grading system. International Journal of Computer Applications, 18(6): 9-12.
• Vidyarthi, S., Singh, S., Tiwari, R., Rai, R. (2020). Classification of first quality fancy cashew kernels using four deep convolutional neural network models. Journal of Food Process Engineering, 43(12): 1-13.