• S. O. Oladele Federal University of Technology, Akure, Nigeria



Prediction, physical properties, locust beans, neural network


For efficient design of post-harvest processing machines/equipment, knowledge of the physical properties of agricultural materials is required. Individual measurements of respective material’s properties are typically used to calculate the equipment/machines design parameters. These properties are important for grading and sorting processes. This study experimentally measured the individual dimensional properties of the seeds and by simulation used artificial neural network to predict some physical properties of African locust bean (Parkia biglobosa) seeds. Two artificial neural network structures, the Multilayer Propagation Neural Network (MLP) and the Radial Basis Neural Network (RBF) were employed. The dimensional properties of the seeds were used as the input variables and the derived seeds' properties - volume, surface and projected regions, geometric mean diameter and sphericity - were used as the output variables. The results showed that MLP structures of the artificial neural network could provide a better prediction of the seeds physical properties parameters, making it a viable replacement method for the manual approach.


Adewumi, I.O.; Ijadunola, J.A.; Oladimeji, S.T. and Kosemani, B.S. 2015. Determination of some physical properties and terminal velocity of locust beans of Parkia Specie (Parkia biglobosa). Science and Engineering Perspectives, 10:105-111.

Afonso Junior, P. C., Correa, P. C., Pinto, F. A. C. and Queiroz, D. M. (2007). Aerodynamic properties of coffee cherries and beans. Biosystems Engineering, 98:39-46.

Aguwa, J. I. and Okafor, J. O. (2012). Preliminary investigation in the use of locust bean pod extract as a binder for the production of laterite blocks for buildings. International Journal of Environmental Science, Management and Engineering. 1(2):57-67

Ajewole, P. O., Ayelegun, T. A. and Oni, I. O. (2018). Effects of drying on some engineering properties of African locust bean seeds. American Journal of Engineering Research, 7, 329–335.

Akintan, A. O., Gbadebo, J. O., Akeredolu, O. A., Arabambi, V. I., Azeez, A. A. and Akintan, C. I. (2013). Marketing analysis of Parkia biglobosa (Jacq.) Benth. seeds in selected markets in Ibadan, Oyo state. Journal of Forestry Research and Management, 10, 20–28.

Akintayo, E.T. (2004). Characteristics and composition of Parkia biglobosa and Jatropha curcas oils and cakes. Bioresource Technology, 92: 307 -310.

Alabi, D. A., Akinsulire, O. R and Sanyanolu, M. A. (2004). Qualitative determination of chemical and biochemical changes in African locust bean (Parkia biglobosa) and melon (Citrullus vulgaris) seeds during fermentation to condiments. Pakistan Journal of Nutrition, 3(3):140 – 145.

Bamgboye, I. and Sadiku, O. A. (2015). Moisture dependent physical properties of locust bean (Parkia biglobosa) seeds. Future of Food: Journal of Food, Agriculture and Society, 3(2):27-40.

Bwade, K. E. and Aliyu, B. (2012). Investigations on the effect of moisture content and variety factors on some physical properties of pumpkin seed (Cucurbitaceae spp). International Journal of Engineering, Business and Enterprise Applications (IJEBEA), 3(1):20-24.

Dahikar, S. S. and Rode, S. V. (2014). Agricultural crop yield prediction using an artificial neural network approach. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, 2(1):683-686.

Demir, B., Ikbal, E. and Ercisli, S. (2017). Prediction of physical parameters of pumpkin seeds using neural network. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 45(1).22-27.

Dousti, A., Ghazavi, M. A. and Maleki, A. (2013). Grading of empty walnut using signal processing and artificial neural network techniques. International Journal of Agriculture and Crop Sciences, 6(15):1072-1078.

El-Sanhoty, R., Shahwan, T. and Ramadan, M. F. (2006). Application of artificial neural networks to develop a classification model between genetically modified maize (Bt-176) and conventional maize by applying lipid analysis data. Journal of Food Composition and Analysis, 19(6):628-636.

Emmanuel, I., Aladesanmi, O. J., Adekunle, O. and Oluwafemi, O. (2017). Effect of fermentation on the physiochemical properties and nutritionally valuable minerals of locust bean (Parkia biglobosa). American Journal of Food Technology,12, 379–384

Eski, İ., Demir, B., Gürbüz, F., Kuş, Z. A., Yilmaz, K. U., Uzun, M. and Ercişli, S. (2018). Design of neural network predictor for the physical properties of almond nuts. Erwerbs-Obstbau, 60(2), 153-160.

Ezeaku, C.A., Akubuo, C. O. and Onwualu, A. P. (1998). Measurement of the resistance of bambara groundnut seed to compressive loading. Journal of Agricultural Engineering and Technology, 6:12-18.

Granitto, P. M., Navone, H. D., Verdes, P. F. and Ceccatto, H. A. (2002). Weed seeds identification by machine vision. Computers and Electronics in Agriculture, 33(2):91-103.

Ibrahim, A. and Onwualu, A. P. (20050. Technologies for extraction of oil from oil-bearing agricultural products: a review. Journal of Agricultural Engineering and Technology, 13:58-70

Irmak, A., Jones, J. W., Batchelor, W. D., Irmak, S., Boote, K. J. and Paz, J. O. (2006). Artificial neural network model as a data analysis tool in precision farming. Transactions of the ASABE, 49(6):2027-2037.

Kaliniewicz, Z., Jadwisieńczak, K., Choszcz, D., Kolankowska, E., Przywitowski, M. and Śliwiński, D. (2014). Correlations between germination capacity and selected properties of parsnip seeds (Pastinaca sativa L.). Agricultural Engineering, 1(149):39-49.

Khalesi, S., Mahmoudi, A., Hosainpour, A. and Alipour, A. (2012). Detection of walnut varieties using impact acoustics and artificial neural networks (ANNs). Modern Applied Science, 6(1):43-49.

Khazaei, J., Sarmadi, M. and Behzad, J. (2006). Physical properties of sunflower seeds and kernels related to harvesting and dehulling. Lucrari Stiintifice, 49:262-271.

Khazaei, N. B., Tavakoli, T., Ghassemian, H., Khoshtaghaza, M. H.and Banakar, A. (2013). Applied machine vision and artificial neural network for modeling and controlling of the grape drying process. Computers and Electronics in Agriculture, 98:205-213.

Ladokun, O.A. and Adejuwon, A.O. (2013). Nutritive and microbial analysis of two types of fermented bean (Parkia biglobosa). Academia Arena, 5(5):15-17.

Mohsenin, N. N. (1986). Physical properties of plant and animal materials. structure, physical characteristics and mechanical properties. Gordon and Breach Science Publishers, New York.

Monteiro, S. T., Minekawa, Y., Kosugi, Y., Akazawa, T.and Oda, K. (2007). Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 62(1):2-12.

Nwakonobi, T. U. and Onwualu, A. P. (2009). Effect of moisture content and types of structural surfaces on coefficient of friction of two Nigerian food grains: sorghum and millet. CIGR E-Journal, Vol. XI, Manuscript 1152

Obetta, S.E. and Onwualu, A. P. (1999). Effect of different surfaces and moisture content on angle of friction of food grains. Journal of Food Science and Technology, 36(1):58-60.

Ogunjimi, L. A. O., Aviara, N. A. and Aregbesola, O. A. (2002). Some engineering properties of locust bean seed. Journal of Food Engineering, 55: 95–99: 95-99.

Oje, K. (1993). Locust Bean Pods and Seeds: Some physical properties of relevance to dehulling and seed processing. Journal of Food Science and Technology, 30(4):253 – 255.

Okonkwo, C. E., Olaniran, A., Ojediran, J. O., Olayanju, T. A., Ajao, F. and Alake, A. S. (2019). Design, development, and evaluation of locust bean seed dehuller. Journal of Food Process Engineering, 42(3), e12963.

Olajide, J.O. and Ade-Omowage, B. I. O. (1999).Some physical properties of locust bean seed. Journal of Agricultural Engineering Research; 74: 15-22.

Olaoye, J. O. (2011). Development of small-scale equipment for depulping locust bean seeds International Journal of Engineering & Technology. 11(6): 173 –186

Olaoye, J. O. (2010). Machinery needs for processing of locust bean seeds in Nigeria. Proceedings of International Agricultural Engineering Conference (pp. 53–62). Asian Association for Agricultural Engineering, Shanghai, China

Oluwole, S. I. and Oluremi, O. K. (2012). Comparison between the amino acid, fatty acid, mineral and nutritional quality of raw, germinated and fermented African locust bean (Parkia biglobosa) flour. Acta Scientiarum Polonorum, Technologia Alimentaria 11(2): 151-165

Omafuvbe, B. O., Falade, O. S., Osuntogun, B. A. and Adewusa, S. R. A. (2004). Chemical and biochemical changes in African Locust Bean (Parkia biglobosa) and Melon (Citrullus vulgaris) seeds during fermentation to condiments. Pakistan Journal of Nutrition, 3 (3): 140-145.

Reshadsedghi, A., Mahmoudi, A., Azimirad, V., Hajilou, J. and Ghaffari, H. (2014). Non-Destructive detection of unshelled almonds quality based on their kernel percentage using impact-acoustics and ANN’s techniques. Agriculture Science Developments, 3(11):360-365.

Sahay, K. M. and Singh, K. K. (2004). Unit operations of agricultural processing. Vikas Publishing House PVT LTD.

Sanya, E. A., Ahouansou, R. H., Bagan, G., Vianou, A. and Hounhouigan, D. J. (2013). Effects of some pretreatments of African locust bean seeds (Parkia biglobosa) on delivered efficiency of a devised dehuller. Research Journal of Recent Sciences, Vol. 2(6), 43-51, June).

Sayıncı, B., Ercişli, S., Akbulut, M., Şavşatli, Y. and Baykal, H. (2015). Determination of shape in fruits of cherry laurel (Prunus laurocerasus) accessions by using elliptic Fourier analysis. Acta Scientiarum Polonorum Hortorum Cultus, 14(1):63-82.

Shahin, M. A., Tollner, E. W., Gitaitis, R. D., Sumner, D. R. and Maw, B. W. (2002). Classification of sweet onions based on internal defects using image processing and neural network techniques. Transactions ofAmerican Society of Agricultural Engineers, 45(5):1613-1618.

Sobukola, O.P. and Onwuka, V.I. 2011. Effect of moisture content on some physical properties of locust bean seed (Parkia fillicoidea L.). Journal of Food Process Engineering, 34: 1946–1964

Šťastný, J., Konečný, V. and Trenz, O. (2011). Agricultural data prediction by means of neural network. Agricultural Economics-Czech, 57(7):356-361.

Suthar, S. H. and Das, S. K. (1996). Some physical properties of karingda (Citrullus lanatus (Thumb) Mansf) seeds. Journal of Agricultural Engineering Research, 65(1):15-22.

Tabatabaeefar, A. and Rajabipour, A. (2005). Modeling the mass of apples by geometrical attributes. Scientia Horticulturae, 105(3):373-382.

Taner, A., Tekgüler, A. and Sauk, H. (2015). Classification of durum wheat varieties by artificial neural networks. Anadolu Journal of Agricultural Sciences, 30:51-59.

TIBCO Software Inc. (2020). Data Science Workbench, version 14.