Prediction of arabica coffee production using artificial neural network and multiple linear regression techniques
Lewin, B., Giovannucci, D. & Varangis, P. Coffee markets: New paradigms in global supply and demand. World Bank Agric. Rural Dev. Discuss. Paper https://doi.org/10.2139/ssrn.996111 (2004).
Google Scholar
Panhuysen, S. & Pierrot, J. Coffee barometer 2014. Hivos, IUCN Nederland, Oxfam, Novib, Solidaridad, WWF. (2014).
Torok, A., Mizik, T., Jambor, A. J. I. J. O. E. & Issues, F. The competitiveness of global coffee trade. Int. J. Econ. Financ. 8, 1–6. https://doi.org/10.32479/ijefi.6692 (2018).
Google Scholar
Degaga, J. & Alamerie, K. J. E. Supply and performance of coffee markets in Gololcha district of Oromia region, Ethiopia. Ekonomika Poljoprivrede. 67, 797–816. https://doi.org/10.5937/ekopolj2003797d (2020).
Google Scholar
Genanaw, T. & Lamenew, W. Indigenous knowledge management framework for coffee production in Ethiopia. Ethiop. e-J. Res. Innov. Foresight. 6, 53–61 (2019).
Duarte, A., Uribe, J. C., Sarache, W. & Calderón, A. J. E. Economic, environmental, and social assessment of bioethanol production using multiple coffee crop residues. Energy 216, 119170. https://doi.org/10.1016/j.energy.2020.119170 (2021).
Google Scholar
Effendi, D. & Rismaya, M. Design and development of coffee production information system to support coffee production productivity in farmers group. IOP Conf. Ser. Mater. Sci. Eng. 879, 012058. https://doi.org/10.1088/1757-899x/879/1/012058 (2020).
Google Scholar
Baloi, D. & Price, A. D. J. I. J. O. P. M. Modelling global risk factors affecting construction cost performance. Int. J. Proj. Manag. 21, 261–269. https://doi.org/10.1016/s0263-7863(02)00017-0 (2003).
Google Scholar
Kittichotsatsawat, Y., Jangkrajarng, V. & Tippayawong, K. Y. J. S. Enhancing coffee supply chain towards sustainable growth with big data and modern agricultural technologies. Sustainability. 13, 4593. https://doi.org/10.3390/su13084593 (2021).
Google Scholar
International Coffee Organization. Developing a sustainable coffee economy (2020). http://www.ico.org/sustaindev_e.asp. Accessed 30 March 2022.
Chuqian, W. A study on the situation and development of the coffee industry in Thailand (2018).
Trébuil, G., Ekasingh, B. & Ekasingh, M. Agricultural commercialisation, diversification, and conservation of renewable resources in northern Thailand highlands. Moussons. 9–10, 131–155. https://doi.org/10.4000/moussons.2005 (2006).
Google Scholar
Sutthi, C. Highland agriculture: From better to worse. Hill Tribes Today. 107–142. (1989).
Romani, S., Cevoli, C., Fabbri, A., Alessandrini, L. & Dalla Rosa, M. Evaluation of coffee roasting degree by using electronic nose and artificial neural network for off-line quality control. J. Food Sci. 77, C960–C965. https://doi.org/10.1016/j.foodres.2020.109667 (2012).
Google Scholar
Moon, M. A. Demand and supply integration: The key to world-class demand forecasting. Walter de Gruyter https://doi.org/10.1515/9781501506024 (2018).
Google Scholar
Doucoure, B., Agbossou, K. & Cardenas, A. Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data. Renew. Energy. 92, 202–211. https://doi.org/10.1016/j.renene.2016.02.003 (2016).
Google Scholar
Ashokkumar, K., Chowdary, D. D. & Sree, C. D. Data analysis and prediction on cloud computing for enhancing productivity in agriculture. IOP Conf. Ser. Mater. Sci. Eng. 590, 012014. https://doi.org/10.1088/1757-899x/590/1/012014 (2019).
Google Scholar
Rajeswari, S. & Suthendran, K. Developing an agricultural product price prediction model using HADT algorithm. Int. J. Eng. Adv. Technol. 9, 569–575. https://doi.org/10.35940/ijeat.a1126.1291s419 (2019).
Google Scholar
Janiesch, C., Zschech, P. & Heinrich, K. Machine learning and deep learning. Electron. Mark. 3, 685–695. https://doi.org/10.1007/s12525-021-00475-2 (2021).
Google Scholar
Tripathy, A., Agrawal, A. & Rath, S. K. Classification of sentiment reviews using n-gram machine learning approach. Expert Syst. Appl. 57, 117–126. https://doi.org/10.1016/j.eswa.2016.03.028 (2016).
Google Scholar
Sun, S., Cao, Z., Zhu, H. & Zhao, J. A survey of optimization methods from a machine learning perspective. IEEE Trans. Cybern. 8, 3668–3681. https://doi.org/10.1109/tcyb.2019.2950779 (2019).
Google Scholar
Valletta, J. J., Torney, C., Kings, M., Thornton, A. & Madden, J. Applications of machine learning in animal behaviour studies. Anim. Behav. 124, 203–220. https://doi.org/10.1016/j.anbehav.2016.12.005 (2017).
Google Scholar
Dogan, A. & Birant, D. Machine learning and data mining in manufacturing. Expert Syst. Appl. 166, 114060. https://doi.org/10.1016/j.eswa.2020.114060 (2021).
Google Scholar
Wiens, J. & Shenoy, E. S. Machine learning for healthcare: On the verge of a major shift in healthcare epidemiology. Clin. Infect. Dis. 66, 149–153. https://doi.org/10.1093/cid/cix731 (2018).
Google Scholar
Howard, J. Artificial intelligence: Implications for the future of work. Am. J. Ind. Med. 11, 917–926. https://doi.org/10.1002/ajim.23037 (2019).
Google Scholar
L’heureux, A., Grolinger, K., Elyamany, H. F. & Capretz, M. A. Machine learning with big data: Challenges and approaches. IEEE Access. 5, 7776–7797. https://doi.org/10.1109/access.2017.2696365 (2017).
Google Scholar
Kim, D. et al. Review of machine learning methods in soft robotics. PLoS ONE 16, e0246102. https://doi.org/10.1371/journal.pone.0246102 (2021).
Google Scholar
Abiodun, O. I. et al. State-of-the-art in artificial neural network applications: A survey. Heliyon. 11, e00938. https://doi.org/10.1016/j.heliyon.2018.e00938 (2018).
Google Scholar
Lindsay, G. W. Attention in psychology, neuroscience, and machine learning. Front. Comput. Neurosci. 14, 29. https://doi.org/10.3389/fncom.2020.00029 (2020).
Google Scholar
Dalal, S. R. et al. In model-based testing in practice. In Proceedings of the 21st International Conference on Software Engineering—ICSE ’99. 285–294. https://doi.org/10.1145/302405.302640 (1999).
Brownlee, J. Supervised and unsupervised machine learning algorithms. Mach. Learn. Mastery. 3 (2016). https://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-algorithms/. Accessed 30 March 2022.
Çınar, Z. M. et al. Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability. 19, 8211. https://doi.org/10.3390/su12198211 (2020).
Google Scholar
Onsree, T. & Tippayawong, N. Machine learning application to predict yields of solid products from biomass torrefaction. Renew. Energy. 167, 425–432. https://doi.org/10.1016/j.renene.2020.11.099 (2021).
Google Scholar
Onsree, T., Tippayawong, N., Phithakkitnukoon, S. & Lauterbach, J. Interpretable machine-learning model with a collaborative game approach to predict yields and higher heating value of torrefied biomass. Energy 249, 123676. https://doi.org/10.1016/j.energy.2022.123676 (2022).
Google Scholar
Agatonovic-Kustrin, S. & Beresford, R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J. Pharm. Biomed. Anal. 5, 717–727. https://doi.org/10.1016/s0731-7085(99)00272-1 (2000).
Google Scholar
Singh, Y. & Chauhan, A. S. Neural networks in data mining. J. Theor. Appl. Inf. Technol. 1, 1–6 (2009).
Google Scholar
Abrougui, K. et al. Prediction of organic potato yield using tillage systems and soil properties by artificial neural network (ANN) and multiple linear regressions (MLR). Soil Tillage Res. 190, 202–208. https://doi.org/10.1016/j.still.2019.01.011 (2019).
Google Scholar
May, R. J., Dandy, G. C., Maier, H. R. & Nixon, J. B. Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems. Environ. Model. Softw. 10–11, 1289–1299. https://doi.org/10.1016/j.envsoft.2008.03.008 (2008).
Google Scholar
Anderson, D. & McNeill, G. Artificial neural networks technology. Kaman Sci. Corp. 6, 1–83 (1992).
Zain, A. M., Haron, H. & Sharif, S. Prediction of surface roughness in the end milling machining using artificial neural network. Expert Syst. Appl. 37, 1755–1768. https://doi.org/10.1016/j.eswa.2009.07.033 (2010).
Google Scholar
Togun, N. K. & Baysec, S. Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks. Appl. Energy. 87, 349–355. https://doi.org/10.1016/j.apenergy.2009.08.016 (2010).
Google Scholar
Yosinski, J., Clune, J., Nguyen, A., Fuchs, T. & Lipson, H. Understanding neural networks through deep visualization. Explain AI. Interpret. Explain. Vis. Deep Learn. https://doi.org/10.1007/978-3-030-28954-6_4 (2015).
Google Scholar
Krenker, A., Bešter, J. & Kos, A. Introduction to the artificial neural networks. Artif. Neural Netw. Methodol. Adv. Biomed. Appl https://doi.org/10.5772/15751 (2011).
Google Scholar
Kouadri, S., Pande, C. B., Panneerselvam, B., Moharir, K. N. & Elbeltagi, A. Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models. Environ. Sci. Pollut. Res. 29, 21067–21091. https://doi.org/10.1007/s11356-021-17084-3 (2022).
Google Scholar
Ceylan, Z. & Bulkan, S. Forecasting PM10 levels using ANN and MLR: A case study for Sakarya City. Glob. Nest J. 20, 281–290. https://doi.org/10.30955/gnj.002522 (2018).
Google Scholar
Shams, S. R., Jahani, A., Kalantary, S., Moeinaddini, M. & Khorasani, N. The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models for predicting SO2 concentration. Urban Clim. 37, 100837. https://doi.org/10.1016/j.uclim.2021.100837 (2021).
Google Scholar
Chakraborty, A. & Goswami, D. Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN). Arab. J. Geosci. 10, 1–11. https://doi.org/10.1007/s12517-017-3167-x (2017).
Google Scholar
Louis, B., Agrawal, V. K. & Khadikar, P. V. Prediction of intrinsic solubility of generic drugs using MLR, ANN and SVM analyses. Eur. J. Med. Chem. 45, 4018–4025. https://doi.org/10.1016/j.ejmech.2010.05.059 (2010).
Google Scholar
Ekemen Keskin, T., Özler, E., Şander, E., Düğenci, M. & Ahmed, M. Y. Prediction of electrical conductivity using ANN and MLR: A case study from Turkey. Acta Geophys. 68, 811–820. https://doi.org/10.1007/s11600-020-00424-1 (2020).
Google Scholar
Lee, K. Y., Chung, N. & Hwang, S. Application of an artificial neural network (ANN) model for predicting mosquito abundances in urban areas. Ecol. Inform. 36, 172–180. https://doi.org/10.1016/j.ecoinf.2015.08.011 (2016).
Google Scholar
Chutsagulprom, N., Chaisee, K., Wongsaijai, B., Inkeaw, P. & Oonariya, C. Spatial interpolation methods for estimating monthly rainfall distribution in Thailand. Theor. Appl. Climatol. 148, 1–12. https://doi.org/10.21203/rs.3.rs-568778/v1 (2022).
Google Scholar
Chen, L. J., Lian, G. P. & Han, L. J. Prediction of human skin permeability using artificial neural network (ANN) modeling. Acta Pharmacol. Sin. 28, 591–600. https://doi.org/10.1111/j.1745-7254.2007.00528.x (2007).
Google Scholar
Soroushmehr, S. R. & Najarian, K. Transforming big data into computational models for personalized medicine and health care. Dialogues Clin. Neurosci. https://doi.org/10.31887/dcns.2016.18.3/ssoroushmehr (2022).
Google Scholar
Cervantes-Sanchez, F., Cruz-Aceves, I., Hernandez-Aguirre, A., Hernandez-Gonzalez, M. A. & Solorio-Meza, S. E. Automatic segmentation of coronary arteries in X-ray angiograms using multiscale analysis and artificial neural networks. Appl. Sci. 24, 5507. https://doi.org/10.3390/app9245507 (2019).
Google Scholar
Priya, D. K., Sam, B. B., Lavanya, S. & Sajin, A. P. A survey on medical image denoising using optimisation technique and classification. Int. Conf. Inf. Commun. Embed. Syst. https://doi.org/10.1109/icices.2017.8070729 (2017).
Google Scholar
Patil, J. V. & Bailke, P. Real time facial expression recognition using RealSense camera and ANN. Int. Conf. Invent. Comput. Technol. 2, 1–6. https://doi.org/10.1109/inventive.2016.7824820 (2016).
Google Scholar
Islam, K. T. & Raj, R. G. Real-time (vision-based) road sign recognition using an artificial neural network. Sensors. 17, 853. https://doi.org/10.3390/s17040853 (2017).
Google Scholar
Lahmyed, R., Ansari, M. E. & Kerkaou, Z. J. S. C. Automatic road sign detection and recognition based on neural network. Soft Comput. 26, 1–22. https://doi.org/10.1007/s00500-021-06726-w (2022).
Google Scholar
Kryvinska, N., Poniszewska-Maranda, A. & Gregus, M. An approach towards service system building for road traffic signs detection and recognition. Proc. Comput. Sci. 141, 64–71. https://doi.org/10.1016/j.procs.2018.10.150 (2018).
Google Scholar
Chauhan, R., Dumka, P. & Mishra, D. R. Modelling conventional and solar earth still by using the LM algorithm-based artificial neural network. Int. J. Ambient Energy. 43, 1389–1396. https://doi.org/10.1080/01430750.2019.1707113 (2022).
Google Scholar
Zaefizadeh, M., Jalili, A., Khayatnezhad, M., Gholamin, R. & Mokhtari, T. Comparison of multiple linear regressions (MLR) and artificial neural network (ANN) in predicting the yield using its components in the hulless barley. Adv. Environ. Biol. 5, 109–114 (2011).
Anita, S. In classification Cherry’s Coffee using k-Nearest Neighbor (KNN) and Artificial Neural Network (ANN). In 2020 International Conference on Information Technology Systems and Innovation (ICITSI).117–122 (IEEE). https://doi.org/10.1109/icitsi50517.2020.9264925 (2020).
Ahmad, A. S. et al. A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renew. Sustain. Energy Rev. 33, 102–109. https://doi.org/10.1016/j.rser.2014.01.069 (2014).
Google Scholar
Leme, D. S., da Silva, S. A., Barbosa, B. H. G., Borém, F. M. & Pereira, R. G. F. A. Recognition of coffee roasting degree using a computer vision system. Comput. Electron. Agric. 156, 312–317. https://doi.org/10.1016/j.compag.2018.11.029 (2019).
Google Scholar
Van Klompenburg, T., Kassahun, A. & Catal, C. Crop yield prediction using machine learning: A systematic literature review. Comput. Electron. Agric. 177, 105709. https://doi.org/10.1016/j.compag.2020.105709 (2020).
Google Scholar
Torkashvand, A. M., Ahmadi, A. & Nikravesh, N. L. Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network (ANN) and multiple linear regressions (MLR). J. Integr. Agric. 7, 1634–1644. https://doi.org/10.1016/s2095-3119(16)61546-0 (2017).
Google Scholar
Dhyani, S. Predicting Rainfall for Agriculture in India Using Regression (Dublin Business School, 2020). https://esource.dbs.ie/handle/10788/4230.
Etminan, A. et al. Determining the best drought tolerance indices using artificial neural network (ANN): Insight into application of intelligent agriculture in agronomy and plant breeding. Cereal Res. Commun. 47, 170–181. https://doi.org/10.1556/0806.46.2018.057 (2019). Accessed 30 March 2022.
Google Scholar
Afan, H. A. et al. ANN based sediment prediction model utilizing different input scenarios. Water Resour. Manag. 4, 1231–1245. https://doi.org/10.1007/s11269-014-0870-1 (2015).
Google Scholar
Bouktif, S., Fiaz, A., Ouni, A. & Serhani, M. A. Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches. Energies 7, 1636. https://doi.org/10.3390/en11071636 (2018).
Google Scholar
Landwehr, N., Hall, M. & Frank, E. Logistic model trees. Mach. Learn. Mastery. 1, 161–205. https://doi.org/10.1007/s10994-005-0466-3 (2005).
Google Scholar
Wilkinson, L. Statistical methods in psychology journals: Guidelines and explanations. Am. Psychol. 8, 594. https://doi.org/10.1037/0003-066x.54.8.594 (1999).
Google Scholar
Kath, J., Byrareddy, V. M., Mushtaq, S., Craparo, A. & Porcel, M. Temperature and rainfall impacts on robusta coffee bean characteristics. Clim. Risk Manag. 32, 100281. https://doi.org/10.1016/j.crm.2021.100281 (2021).
Google Scholar
Jayakumar, M., Rajavel, M. & Surendran, U. Climate-based statistical regression models for crop yield forecasting of coffee in humid tropical Kerala, India. Int. J. Biometeorol. 12, 1943–1952. https://doi.org/10.1007/s00484-016-1181-4 (2016).
Google Scholar
Ilaboya, I. R. & Igbinedion, O. E. Performance of multiple linear regression (MLR) and artificial neural network (ANN) for the prediction of monthly maximum rainfall in Benin City, Nigeria. Int. J. Eng. Sci. Appl. 1, 21–37 (2019).
Katongtung, T., Onsree, T. & Tippayawong, N. Machine learning prediction of biocrude yields and higher heating values from hydrothermal liquefaction of wet biomass and wastes. Bioresour. Technol. 344, 126278. https://doi.org/10.1016/j.biortech.2021.126278 (2022).
Google Scholar
Wongchai, W., Onsree, T., Sukkam, N., Promwungkwa, A. & Tippayawong, N. Machine learning models for estimating above ground biomass of fast growing trees. Expert Syst. Appl. 199, 117186. https://doi.org/10.1016/j.eswa.2022.117186 (2022).
Google Scholar
Bayat, M., Ghorbanpour, M., Zare, R., Jaafari, A. & Pham, B. T. Application of artificial neural networks for predicting tree survival and mortality in the Hyrcanian forest of Iran. Comput. Electron. Agric. 164, 104929. https://doi.org/10.1016/j.compag.2019.104929 (2019).
Google Scholar
Ustaoglu, B., Cigizoglu, H. & Karaca, M. Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods. Meteorol. Appl. 15, 431–445. https://doi.org/10.1002/met.83 (2008).
Google Scholar
El-Shafie, A., Mukhlisin, M., Najah, A. A. & Taha, M. R. Performance of artificial neural network and regression techniques for rainfall-runoff prediction. Int. J. Phys. Sci. 6, 1997–2003. https://doi.org/10.54302/mausam.v62i1.4711 (2011).
Google Scholar
Matsumura, K., Gaitan, C. F., Sugimoto, K., Cannon, A. J. & Hsieh, W. W. Maize yield forecasting by linear regression and artificial neural networks in Jilin, China. J. Agric. Sci. 153, 399–410. https://doi.org/10.1017/s0021859614000392 (2015).
Google Scholar
Patle, G., Chettri, M. & Jhajharia, D. Monthly pan evaporation modelling using multiple linear regression and artificial neural network techniques. Water Supply 20, 800–808. https://doi.org/10.2166/ws.2019.189 (2020).
Google Scholar
Kisi, O., Tombul, M. & Kermani, M. Z. Modeling soil temperatures at different depths by using three different neural computing techniques. Theor. Appl. Climatol. 121, 377–387. https://doi.org/10.1007/s00704-014-1232-x (2015).
Google Scholar
Yasar, A., Simsek, E., Bilgili, M., Yucel, A. & Ilhan, I. Estimation of relative humidity based on artificial neural network approach in the Aegean Region of Turkey. Meteorol. Atmos. Phys. 115, 81–87. https://doi.org/10.1007/s00703-011-0168-2 (2012).
Google Scholar
Li, Y. et al. Toward building a transparent statistical model for improving crop yield prediction: Modeling rainfed corn in the US. Field Crops Res. 234, 55–65. https://doi.org/10.1016/j.fcr.2019.02.005 (2019).
Google Scholar
Han, X. et al. Crop evapotranspiration prediction by considering dynamic change of crop coefficient and the precipitation effect in back-propagation neural network model. J. Hydrol. 596, 126104. https://doi.org/10.1016/j.jhydrol.2021.126104 (2021).
Google Scholar
Adisa, O. M. et al. Application of artificial neural network for predicting maize production in South Africa. Sustainability. 4, 1145. https://doi.org/10.3390/su11041145 (2019).
Google Scholar
Gonzalez-Sanchez, A., Frausto-Solis, J. & Ojeda-Bustamante, W. Attribute selection impact on linear and nonlinear regression models for crop yield prediction. Sci. World J. 2014, 1–10. https://doi.org/10.1155/2014/509429 (2014).
Google Scholar
Balakrishnan, N. & Muthukumarasamy, G. Crop production-ensemble machine learning model for prediction. Int. J. Comput. Sci. Softw. Eng. 7, 148 (2016).
Leng, J. et al. A loosely-coupled deep reinforcement learning approach for order acceptance decision of mass-individualized printed circuit board manufacturing in industry 4.0. J. Clean. Prod. 280, 124405. https://doi.org/10.1016/j.jclepro.2020.124405 (2021).
Google Scholar
Leng, J., Chen, Q., Mao, N. & Jiang, P. Combining granular computing technique with deep learning for service planning under social manufacturing contexts. Knowl. Based Syst. 143, 295–306. https://doi.org/10.1016/j.knosys.2017.07.023 (2018).
Google Scholar
Phromphithak, S., Onsree, T. & Tippayawong, N. Machine learning prediction of cellulose-rich materials from biomass pretreatment with ionic liquid solvents. Bioresour. Technol. 323, 124642. https://doi.org/10.1016/j.biortech.2020.124642 (2021).
Google Scholar
Leng, J. et al. Bi-level artificial intelligence model for risk classification of acute respiratory diseases based on Chinese clinical data. Appl. Intell. https://doi.org/10.1007/s10489-022-03222-y (2022).
Google Scholar
Leng, J. & Jiang, P. Granular computing–based development of service process reference models in social manufacturing contexts. Concurr. Eng. 25, 95–107. https://doi.org/10.1177/1063293×16666312 (2017).
Google Scholar
Bu, F. & Wang, X. A smart agriculture IoT system based on deep reinforcement learning. Future Gener. Comput. Syst. 99, 500–507. https://doi.org/10.1016/j.future.2019.04.041 (2019).
Google Scholar
Hsu, K. L., Gupta, H. V. & Sorooshian, S. Artificial neural network modeling of the rainfall-runoff process. Water Resour. Res. 10, 2517–2530. https://doi.org/10.1029/95wr01955 (1995).
Google Scholar