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Forecasting Volume of Corn Production through Neural Network Model: A Post-harvest Monitoring Tool

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Research Paper 04/05/2023
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Forecasting Volume of Corn Production through Neural Network Model: A Post-harvest Monitoring Tool

Ronald Lachica Aquino
Int. J. Biosci.22( 5), 25-34, May 2023.
Certificate: IJB 2023 [Generate Certificate]
Key: ForecastingMaizeMonitoringNeural networkPost harvestTime series

Abstract

The paper deals with the forecasting of volume (in tonnes) of corn production relative to the harvested farmed area during the second semester of agricultural cropping. Time series data used were obtained from the open stat database published by the Philippine Statistics Authority from the second semester of 1987 to second semester of 2022. Artificial Neural Network (ANN) models were developed, trained and validated to forecast the volume of corn production. Statistical errors such as Root Mean Square Error (RMSE) were computed and compared to identify the most suitable model to forecast the corresponding volume. ANN () model was identified and used to forecast the volume of corn production. The two sets of data, namely actual and forecast volumes of production, were found to have statistically no significant difference, which implies that the model gives forecast values that are relatively close to the actual ones.

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Agatonovic-Kustrin S, Beresford R. 2000. Basic concepts of artificial neural network (ANN) modeling. Journal of Pharmaceutical and Biomedical Analysis 22(5), 717-727. https://doi.org/10.1016/S0731-7085(99)00272-1

Agri Farming. 2021. Agriculture in Philippines- Farming, Major Crops. Agri Farming: https://www.agrifarming.in/agriculture-in-philippines-farming-major-crops

Ahmed M, Sultan M, Elbayoumi T, Tissot P. 2019. Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks. Remote Sensing 11(15), 1769. https://doi.org/10.3390/rs11151769

Ali Z, Hussain I, Faisal M, Nazir HM, Hussain T, Shad M, Gani S. 2017. Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model. Advance in Meteorology. https://doi.org/10.1155/2017/5681308

Cococcioni M, Rossi F, Ruffaldi E, Saponara S. 2020. Home Applications in Electronics Pervading Industry, Environment and Society Conference paper. Applications in Electronics Pervading Industry, Environment and Society 7, 213-221.

Dehghani M, Saghafian B, Rivaz F, Khodadadi  A. 2017. Evaluation of dynamic regression and artificial neural networks models for real-time hydrological drought forecasting. Arabian Journal of Geosciences.

Di Persio L, Honchar O. 2016. Artificial Neural Networks architectures for stock price prediction:. International Journal of Circuits, Systems and Signal Processing 10, 403-413.

Dikshit A, Pradhan B, Santosh M. 2022. Artificial neural networks in drought prediction in the 21st century–A scientometric analysis. Applied Soft Computing 114, 108080. https://doi.org/10.1016/j.asoc.2021.108080

FAO. 2022. Food and Agricultural Organization of the United Nation. GIEWS – Global Information and Early Warning System: https://www.fao.org/giews/countrybrief/country.jsp?code=PHL

Fernandes JL, Ebecken NF, Esquerdo JD. 2017. Sugarcane yield prediction in Brazil using NDVI time series and neural networks ensemble. International Journal of Remote Sensing 38(16), 4631-4644. https://doi.org/10.1080/01431161.2017.1325531

Gagne TO, Reygondeau G, Jenkins CN, Sexton JO, Bograd SJ, Hazen EL, Van Houtan KS. 2020. Towards a global understanding of the drivers of marine and terrestrial biodiversity. PloS One. https://doi.org/10.1371/journal.pone.0228065

Grain Pro. 2023. GrainPro. Retrieved from The Benefits of Post-Harvest Management: https://news.grainpro.com/the-benefits-of-post-harvest-management

Gro-Intelligence. 2018. No Time to Waste: A New Model for Agricultural Forecasting. https://gro-intelligence.com/:   https://gro-intelligence.com/insights/no-time-to-waste-a-new-model-for-agricultural-forecasting

Gutierrez-Estrada JC, Silva C, Yañez E, Rodriguez N, Pulido-Calvo I. 2007. Monthly catch forecasting of anchovy Engraulis ringens in. Fisheries Research 86(2), 188-200.

Islam K, Rahman M, Jashimuddin M. 2018. Modeling land use change using Cellular Automata and Artificial Neural Network: The case of Chunati Wildlife Sanctuary, Bangladesh. Ecological Indicators 88, 439-453. https://doi.org/10.1016/j.ecolind.2018.01.047

Jahani A, Saffariha M. 2020. Human Activities Impact Prediction in Vegetation Diversity of Lar National Park in Iran Using Artificial Neural Network Model. Integrated Environmental Assessment and Management. https://doi.org/10.1002/ieam.4349

Jebb AT, Pariggon S, Woo S. 2017. Exploratory data analysis as a foundation of inductive research. Human Resource Management Review 27, 265-276. https://doi.org/10.1016/j.hrmr.2016.08.003

Kaul M, Hill RL, Walthall C. 2005. Artificial neural networks for corn andnsoybean yield prediction. Agricultural Systems 85, 1-18. https://doi.org/10.1016/j.agsy.2004.07.009

Kriegeskorte N, Golan T. 2019. Neural network models and deep learning. Current Biology, 29(7). https://doi.org/10.1016/j.cub.2019.02.034

Kujawa S, Niedbala G. 2021. Artificial Neural Networks in Agriculture. Agriculture 11(6), 497. https://doi.org/10.3390/agriculture11060497

Mishra S, Mishra D, Santra G. 2016. Applications of Machine Learning Techniques in. Indian Journal of Science and Technology 9(38). https://doi.org/10.17485/ijst/2016/v9i38/95032

Morshed SR, Fattah M, Haque M, Morshed S. 2022. Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh. Physics and Chemistry of the Earth, Parts A/B/C 126, 103021. https://doi.org/10.1016/j.pce.2021.103021

Mustaffa Z, Yusof Y. 2011. A comparison of normalization techniques in predicting dengue outbreak. International Conference on Information and Finance, 345-349.

OECD.org. 2021. Crop Production. Retrieved from OECD: https://data.oecd.org/agroutput/cropproduction.htm

Palanivel K, Surianarayanan C. 2019. An Approach for Prediction of Crop Yield Using Machine Learning and Big Data Techniques. International Journal of Computer Engineering and Technology, 10(3), 110-118.

Prosekov AY, Ivanova SA. 2018. Food security: The challenge of the present. Geoforum 91, 73-77. https://doi.org/10.1016/j.geoforum.2018.02.030

Ramasubramanian V. 2006. Forecasting Techniques in Agriculture. Journal of the Indian Society of Agricultural Statistics.

Samantaray S, Sahoo A., Ghose DK. 2019. Assessment of Runoff via Precipitation using Neural Networks:Watershed Modelling for Developing Environment in Arid Region. Pertanika Journal of Science & technology 27(4), 2245-2263.

Saputra MH, Lee HS. 2019. Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton. Sustainability 11(11), 3024. https://doi.org/10.3390/su11113024

Sarkar A, Pandey P. 2015. River Water Quality Modelling Using Artificial Neural Network Technique. Aquatic Procedia 4, 1070-1077. https://doi.org/10.1016/j.aqpro.2015.02.135

Source Trace. 2019. SourceTrace. Cutting Post-Harvest Losses: The Bottom-up Approach to Increasing Food Security: https://www.sourcetrace.com/blog/cutting-post-harvest-losses-bottom-approach-increasing-food-security/

Tourenq C, Aulagnier S, Mesleard F, Durieux L, Johnson A, Gonzales G, Lek S. 1999. Use of artificial neural networks for predicting rice crop damage by greater flamingos in the Camargue, France. Ecological Modelling, 120(2-3), 349-358. https://doi.org/10.1016/S0304-3800(99)00114-3

Vedantu. 2022. Vedantu. Retrieved from https://www.vedantu.com: https://www.vedantu.com/commerce/food-security

Yatoo SA, Sahu P, Kalubarme MH, Kansara B. B. 2020. Monitoring land use changes and its future prospects using cellular automata simulation and artificial neural network for Ahmedabad city, India. GeoJournal 87, 765-786.

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