Enhancing Fruit Tree Yield Prediction with an Optimized Grey Neural Network Model Using the Fruit Fly Algorithm
Abstract - 138
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Keywords

Fruit tree yield
BP neural network
Combination model
Drosophila algorithm
Grey prediction model

How to Cite

Huang, L., & Guo, L. (2024). Enhancing Fruit Tree Yield Prediction with an Optimized Grey Neural Network Model Using the Fruit Fly Algorithm. Journal of Advances in Applied & Computational Mathematics, 11, 72–83. https://doi.org/10.15377/2409-5761.2024.11.4

Abstract

This article first integrates data on fruit tree yield and related influencing factors in Fujian Province, covering two major categories of factors: social and natural. On this basis, this article calculated the correlation coefficients between fruit tree yield and various factors, verifying the rationality of indicator selection. Subsequently, this article used a combination of grey model GM (1,1), BP neural network model, and fruit fly algorithm to optimize the grey model and neural network for fruit tree yield prediction. In the end, based on the research results, it was found that the combination model of fruit fly algorithm optimized grey model and neural network has a better prediction effect on fruit tree yield, which is more suitable for us to deeply understand the changes in fruit tree yield. It can also be well trained for relatively random natural factors.

https://doi.org/10.15377/2409-5761.2024.11.4
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References

Jiang Z, Jianbo Z. The development of modern chinese fruit industry from the perspective of "alternative agriculture". Agric History China. 2023; 42(04): 56-71.

Lili J, Lisa P, Jinhua Y. Mathematical modeling and data processing. Beijing: Science Press; 2020.

Haijun H. Research on Key Technologies for Precise Application of Water and Fertilizer in Apple Orchards. Zigong: Sichuan University of Light Industry and Technology; 2021.

Shulin H, Huimin L, Liqiang J, Yong L. Research on water demand prediction of fruit trees based on neural network algorithms. J Irrig Drain Eng. 2022; 41(1): 19-24. https://doi.org/10.13522/j.cnki.ggps.2021332

Yanan X. Research on development strategies of Australian nut industry in Chongzuo City, Guangxi. Nanning: Guangxi University; 2022.

Batista APB, Scolforo HF, de Mello JM, Guedes MC, Terra MCN, Scalon JD, et al. Spatial association of fruit yield of Bertholletia excelsa Bonpl. trees in eastern Amazon. Forest Ecol Manag. 2019; 441: 99-105. https://doi.org/10.1016/j.foreco.2019.03.043

Chongjiao Z, Xiaolin S, Shuanghong H. Research on refrigerator order demand prediction based on fruit fly algorithm optimized grey neural network. Math Pract Theor. 2017; 47(20): 15-9.

Feng L, Jiangbo X. The way of emergency news propaganda and management for major natural disasters: taking the news and public opinion guidance of Fujian province's defense against super typhoon "Du Suri" as an example. Chinese Journalist. 2023; (09): 83-5.

Feng X, Haihua S, Jiayi W, Ran Z, Junhui W, Huan Y, et al. Research on status evaluation of relay protection devices based on grey relational degree. Electr Meas Instrum. 2024; (in press). Available from: https://kns.cnki.net/kcms/detail/23.1202.TH.20240511.0905.004.html

Wang S, Xing Q, Wang X, Wu Q. Demand forecasting model of coal logistics based on Drosophila grey neural network. Int J Syst Assur Eng Manag. 2023; 14(2): 807-15. DOI:https://doi.org/10.1007/s13198-021-01586-x

Wu H, Wang L, Xie M, Wang K, Fu Y. Peak prediction of carbon emissions from electricity based on grey BP neural network. Electron Des Eng. 2024; 32(09): 105-9. https://doi.org/10.14022/j.issn1674-6236.2024.09.022

van Klompenburg T, Kassahun A, Catal C. Crop yield prediction using machine learning: A systematic literature review. Comput Electron Agric. 2020; 177: 105709. https://doi.org/10.1016/j.compag.2020.105709

Dias KOG, Piepho HP, Guimaraes LJM, Guimaraes PEO, Parentoni SN, Pinto MO, et al. Novel strategies for genomic prediction of untested single-cross maize hybrids using unbalanced historical data. Theor Appl Genet. 2020; 133: 443-55. https://doi.org/10.1007/s00122-019-03475-1

Dahane A, Benameur R, Kechar B, Benyamina A. An IoT based smart farming system using machine learning. 2020 International Symposium on Networks, Computers and Communications (ISNCC), Montreal, QC, Canada, 2020, pp. 1-6. https://doi.org/10.1109/ISNCC49221.2020.9297341

Sawant D, Jaiswal A, Singh J, Shah P. AgriBot - An intelligent interactive interface to assist f armers in agricultural activities. 2019 IEEE Bombay Section Signature Conference (IBSSC), Mumbai, India, 2019 pp. 1-6. https://doi.org/10.1109/IBSSC47189.2019.8973066

Kumar YJN, Spandana V, Vaishnavi VS, Neha K, Devi VGRR. Supervised machine learning approach for crop yield prediction in agriculture sector. 2020 5th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2020, pp. 736-41. https://doi.org/10.1109/ICCES48766.2020.9137868

Usha Rani N, Gowthami G. Smart crop suggester In Jyothi S, Mamatha D, Satapathy S, Raju K, Favorskaya M, Eds. Advances in Computational and Bio-Engineering. CBE 2019. Learning and Analytics in Intelligent Systems, vol 15. Springer, Cham; https://doi.org/10.1007/978-3-030-46939-9_34

Pudumalar S, Ramanujam E, et al. Crop recommendation system for precision agriculture. Eighth International Conference on Advanced Computing (ICoAC). 2017; pp. 32-36. https://doi.org/10.1109/ICoAC.2017.7951740

Ahamed ATMS, et al. Applying data mining techniques topredict annual yield of major crops and recommend planting different crops in different districts in Bangladesh. IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). 2015; pp. 1-6, https://doi.org/10.1109/SNPD.2015.7176185

Kumar R, Singh MP, Kumar P, Singh JP. Crop Selection Method to maximize crop yield rate using machine learning technique. 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Avadi, India, 2015, pp. 138-45. https://doi.org/10.1109/ICSTM.2015.7225403

Reddy PC, Babu AS. Survey on weather prediction using big data analystics. 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, 2017, pp. 1-6. https://doi.org/10.1109/ICECCT.2017.8117883

Kishor RV, Shatrughan KP, Balasaheb KK, Sadashiv MB, Sachin V, Gaike VV, et al. Agromet expert system for cotton and soyabean crops in regional area. 2018 International Conference on Advances in Communication and Computing Technology (ICACCT), Sangamner, India, 2018, pp. 149-54. https://doi.org/10.1109/ICACCT.2018.8529664

Ghazaryan G, Skakun S, König S, Rezaei EE, Siebert S, Dubovyk O. Crop yield estimation using multi-source satellite image series and deep learning. IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 2020, pp. 5163-6. https://doi.org/10.1109/IGARSS39084.2020.9324027

Meeradevi, Salpekar H. Design and implementation of mobile application for crop yield prediction using machine learning. 2019 Global Conference for Advancement in Technology (GCAT), Bangalore, India, 2019, pp. 1-6. https://doi.org/10.1109/GCAT47503.2019.8978315

Le TL, Wang JW, Wang CC, Nguyen TN. Automatic defect inspection for coated eyeglass based on symmetrized energy analysis of color channels. Symmetry, 2019; 11(12): 1518. https://doi.org/10.3390/sym11121518

Nguyen TN, Liu BH, Wang SY. On new approaches of maximum weighted target coverage and sensor connectivity: Hardness and approximation. IEEE Trans Network Sci Eng. 2019; 7(3): 1736-51. https://doi.org/10.1109/TNSE.2019.2952369

Rajendrakumar S, Parvati VK, Rajashekarappa, Parameshachari BD. Automation of irrigation system through embedded computing technology. Proceedings of the 3rd International Conference on Cryptography, Security and Privacy. 2019; 289-93. https://doi.org/10.1145/3309074.3309108

Parameshachari BD, Kiran, Rashmi P, Supriya MC, Rajashekarappa, Panduranga HT, et al. Controlled partial image encryption based on LSIC and chaotic map. ICCSP 2019: 2019 the 3rd International Conference on Cryptography, Security and Privacy Kuala Lumpur Malaysia: January 19 - 21, 2019, pp. 60-3. https://doi.org/10.1145/3309074.3309107

Ahamed ATMS, Mahmood NT, Hossain N, MT Kabir, Das K, Rahman F, et al. Applying data mining techniques to predict annual yield of major crops and recommend planting different crops in different districts in Bangladesh. 2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Takamatsu, Japan: 2015, pp. 1-6. https://doi.org/10.1109/SNPD.2015.7176185

Ahmad I, Saeed U, Fahad M, Ullah A, Habib ur Rahman M, Ahmad A, et al. Yield forecasting of spring maize using remote sensing and crop modeling in Faisalabad-Punjab Pakistan. J Indian Soc Remote Sens. 2018; 46(10): 1701-11. https://doi.org/10.1007/s12524-018-0825-8

Ali I, Cawkwell F, Dwyer E, Green S. Modeling managed grassland biomass estimation by using multitemporal remote sensing data—a machine learning approach. IEEE J Sel Top Appl Earth Obs Remote Sens. 2017; 10(7): 3254-64. https://doi.org/10.1109/JSTARS.2016.2561618

Ananthara MG; Arunkumar T, Hemavathy R. CRY-An improved crop yield prediction model using bee hive clustering approach for agricultural data sets. 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering, Salem, India: 2013, pp. 473-8. https://doi.org/10.1109/ICPRIME.2013.6496717

Baral, Kumar Tripathy, Bijayasingh. Yield prediction using artificial neural networks. In: Das VV, Stephen J, Chaba Y, Eds. Computer Networks and Information Technologies. CNC 2011. Communications in Computer and Information Science, vol 142, Berlin, Heidelberg: Springer; 2011, https://doi.org/10.1007/978-3-642-19542-6_57

Bargoti S, Underwood JP. Image segmentation for fruit detection and yield estimation in apple orchards. J Field Rob. 2017; 34(6): 1039-60. https://doi.org/10.1002/rob.21699

Beulah R. A survey on different data mining techniques for crop yield prediction. Int J Comput Sci Eng. 2019; 7(1): 738-44. https://doi.org/10.26438/ijcse/v7i1.738744

Bose P, Kasabov NK, Bruzzone L, Hartono RN. Spiking neural networks for crop yield estimation based on spatiotemporal analysis of image time series. IEEE Trans Geosci Remote Sens. 2016; 54(11): 6563-73. https://doi.org/10.1109/TGRS.2016.2586602

Çakır Y, Kırcı M, Güneş EO. Yield prediction of wheat in south-east region of Turkey by using artificial neural networks. 2014 The Third International Conference on Agro-Geoinformatics, Beijing, China, 2014, pp. 1-4. https://doi.org/10.1109/Agro-Geoinformatics.2014.6910609

Charoen-Ung P, Mittrapiyanuruk P. Sugarcane yield grade prediction using random forest with forward feature selection and hyper-parameter tuning, In: Unger H, Sodsee S, Meesad P, Eds. Recent Advances in Information and Communication Technology 2018. IC2IT 2018. Advances in Intelligent Systems and Computing, vol 769. Springer, 2019. https://doi.org/10.1007/978-3-319-93692-5_4

Everingham Y, Sexton J, Skocaj D. Inman-Bamber G. Accurate prediction of sugarcane yield using a random forest algorithm. Agron Sustain Dev. 2016; 36: Article number: 27. https://doi.org/10.1007/s13593-016-0364-z

Everingham YL, Smyth CW, Inman-Bamber NG. Ensemble data mining approaches to forecast regional sugarcane crop production. Agric For Meteorol. 2009; 149:(3-4), 689-96. https://doi.org/10.1016/J.AGRFORMET.2008.10.018

Fernandes JL, Ebecken NFF, Mora Esquerdo JCD. Sugarcane yield prediction in Brazil using NDVI time series and neural networks ensemble. Int J Remote Sens. 2017; 38(16): 4631-44. https://doi.org/10.1080/01431161.2017.1325531

Filippi P, Jones EJ, Wimalathunge NS, Somarathna PDSN, Pozza LE, Ugbaje SU, et al. An approach to forecast grain crop yield using multilayered, multi-farm data sets and machine learning. Precis Agric. 2019; 20: 1015-29. https://doi.org/10.1007/s11119-018-09628-4

Gandhi N, Armstrong L. Applying data mining techniques to predict yield of rice in humid subtropical climatic zone of India. 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India: 2016, pp. 1901-6.

Goldstein A, Fink L, Meitin A, Bohadana S, Lutenberg O, Ravid G. Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist’s tacit knowledge. Precis Agric. 2018; 19(3): 421-44. https://doi.org/10.1007/s11119-017-9527-4

Gonzalez-Sanchez A, Frausto-Solis J, Ojeda-Bustamante W. Predictive ability of machine learning methods for massive crop yield prediction. Spanish J Agric Res. 2014; 12(2): 313-28. https://doi.org/10.5424/sjar/2014122-4439

Jeong JH, Resop JP, Mueller ND, Fleisher DH, Yun K, Butler EE, et al. Random forests for global and regional crop yield predictions. PLoS One. 2016; 11(6): 1-15. https://doi.org/10.1371/journal.pone.0156571

Khanal S, Fulton J, Klopfenstein A, Douridas N, Shearer S. Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield. Comput. Electron. Agric. 2018; 153: 213-25. https://doi.org/10.1016/J.COMPAG.2018.07.016

Matsumura K, Gaitan CF, Sugimoto K, Cannon AJ, Hsieh WW. Maize yield forecasting by linear regression and artificial neural networks in Jilin, China. J Agric Sci. 2015; 153(3): 399-410. https://doi.org/10.1017/S0021859614000392

Tao J, Ning L, Shuting S. Research on water demand prediction in Jixi City based on grey prediction model GM (1,1). Heilongjiang Water Resour Sci Technol. 2024; 52 (04): 26-9. https://doi.org/10.14122/j.cnki.hskj.2024.04.012

Hongmei Z, Chengtao Z. Comparative study on medium and long-term prediction of reservoir inflow runoff based on LSTM and BP neural network. Hydrology. (in press). https://doi.org/10.19797/j.cnki.1000-0852.20230413

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Copyright (c) 2024 Lingfeng Huang, Litao Guo

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