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Evaluation of machine learning approaches for prediction of pigeon pea yield based on weather parameters in India

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Abstract

Pigeon pea is the second most important grain legume in India, primarily grown under rainfed conditions. Any changes in agro-climatic conditions will have a profound influence on the productivity of pigeon pea (Cajanus cajan) yield and, as a result, the total pulse production of the country. In this context, weather-based crop yield prediction will enable farmers, decision-makers, and administrators in dealing with hardships. The current study examines the application of the stepwise linear regression method, supervised machine learning algorithms (support vector machines (SVM) and random forest (RF)), shrinkage regression approaches (least absolute shrinkage and selection operator (LASSO) or elastic net (ENET)), and artificial neural network (ANN) model for pigeon pea yield prediction using long-term weather data. Among the approaches, ANN resulted in a higher coefficient of determination (R2 = 0.88–0.99), model efficiency (0.88–1.00) with subsequent lower normalised root mean square error (nRMSE) during calibration (1.13–12.55%), and validation (0.33–21.20%) over others. The temperature alone or its interaction with other weather parameters was identified as the most influencing variables in the study area. The Pearson correlation coefficients were also determined for the observed and predicted yield. Those values also showed ANN as the best model with correlation values ranging from 0.939 to 0.999 followed by RF (0.955–0.982) and LASSO (0.880–0.982). However, all the approaches adopted in the study were outperformed the statistical method, i.e. stepwise linear regression with lower error values and higher model efficiency. Thus, these approaches can be effectively used for precise yield prediction of pigeon pea over different districts of Karnataka in India.

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References

  • Amaratunga V, Wickramasinghe L, Perera A, Jayasinghe J, Rathnayake U (2020) Artificial neural network to estimate the paddy yield prediction using climatic data. Mathem Probl Eng Article ID 8627824.https://doi.org/10.1155/2020/8627824

  • Anonymous (2020) https://www.fao.org/faostat/en/#data/QCL. Accessed 3 Apr 2022

  • Anonymous (2021) Pocket Book of Agricultural Statistics. https://eands.dacnet.nic.in/PDF/Pocket%202020-%20Final%20web%20file.pdf. Accessed 6 Apr 2022

  • Bajya DR, Monga D, Tyagi MP, Meena BL (2010) Population dynamics of Helicoverpa armigera on chick pea, pigeon pea and cotton in correlation with weather parameters. Ann Pl Protec Sci 18(1):227–229

    Google Scholar 

  • Bali N, Singla A (2021) Deep learning based wheat crop yield prediction model in Punjab region of North India. App Artificial Intel 35(15):1304–1328. https://doi.org/10.1080/08839514.2021.1976091

    Article  Google Scholar 

  • Basir MS, Chowdhury M, Islam MN, Ashik-E-Rabbani M (2021) Artificial neural network model in predicting yield of mechanically transplanted rice from transplanting parameters in Bangladesh. J Agric Food Res 5:100186. https://doi.org/10.1016/j.jafr.2021.100186

    Article  Google Scholar 

  • Behroozi-Khazaei N, Nasirahmadi A (2017) A neural network based model to analyze rice parboiling process with small dataset. J Food Sci Technol 54(8):2562–2569. https://doi.org/10.1007/s13197-017-2701-x

    Article  Google Scholar 

  • Bhagat AA, Tirmali AM, Chaudhary PM (2020) Precipitation distribution and impact of weather parameters on area, production and productivity of pigeonpea in Jalgaon district. Int J Chemical Stud 8(4):2167–2172. https://doi.org/10.22271/chemi.2020.v8.i4x.9950

    Article  Google Scholar 

  • Birthal PS, Khan TM, Negi DS, Agarwal S (2014) Impact of climate change on yields of major food crops in India: implications for food security. Agric Econ Res Rev 27(2):145–155. https://doi.org/10.5958/0974-0279.2014.00019.6

    Article  Google Scholar 

  • Bocca FF, Rodrigues LHA (2016) The effect of tuning, feature engineering, and feature selection in data mining applied to rainfed sugarcane yield modelling. Comput Electron Agric 128:67–76. https://doi.org/10.1016/j.compag.2016.08.015

    Article  Google Scholar 

  • Borah SR, Dutta SK (2004) Seasonal incidence of Helicoverpa armigera Hubner larvae on pigeonpea. Bioved 15(1/2):127–130

    Google Scholar 

  • Breiman L (2001) Random Forests. Mach Learn 45(1):05–32

    Article  Google Scholar 

  • Chakravarty S, Agnihotri M, Kumar R (2016) Impact of weather parameters on population build up and natural parasitization of pod fly, Melanagromyza obtusa (Malloch) on Pigeon pea. Ann Pl Protec Sci 24(2):290–294

    Google Scholar 

  • Chhetry GKN, Ranjana DT (2014) Wilt epidemiology of pigeonpea (Cajanus cajan (L.) Millsp.) in organic farming system. IOSR J Agric Veter Sci 7(1):1–6

    Article  Google Scholar 

  • Das B, Murgaonkar D, Navyashree S, Kumar P (2022) Novel combination artificial neural network models could not outperform individual models for weather-based cashew yield prediction. Int J Biometeorol. https://doi.org/10.1007/s00484-022-02306-1

  • Das B, Nair B, Arunachalam V, Reddy KV, Venkatesh P, Chakraborty D, Desai S (2020) Comparative evaluation of linear and nonlinear weather-based models for coconut yield prediction in the west coast of India. Int J Biometeorol. https://doi.org/10.1007/s00484-020-01884-2

  • Das B, Nair B, Reddy VK, Venkatesh P (2018) Evaluation of multiple linear, neural network and penalised regression models for prediction of rice yield based on weather parameters for west coast of India. Int J Biometeorol 62(10):1809–1822. https://doi.org/10.1007/s00484-018-1583-6

    Article  Google Scholar 

  • Dharmaraja S, Jain V, Anjoy P, Chandra H (2020) Empirical analysis for crop yield forecasting in India. Agric Res 9(1):132–138

    Article  Google Scholar 

  • Ezeaku IE, Ajeigbe HA, Okechukwu EC (2016) Evaluation of introduced pigeonpea (Cajanus cajan (l.) Mill sp.) Genotypes for growth and yield performance in Sudano-Sahelian ecology of Nigeria. J Animal Plant Sci 26(01):163–169

    Google Scholar 

  • Friedman J, Hastie T, Tibshirani R (2009) Lasso and elastic-net regularized generalized linear models. In: R Package ‘glmnet’ Version 2009

  • Gandhi N, Petkar O, Armstrong LJ (2016) Rice crop yield prediction using artificial neural networks. In: IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR). Chennai, India, pp 105–110

  • Ghosh K, Balasubramanian R, Bandopadhyay S, Chattopadhyay N, Singh KK, Rathore LS (2014) Development of crop yield forecast models under FASAL-a case study of kharif rice in West Bengal. J Agrometeorol 16(1):1–8

    Article  CAS  Google Scholar 

  • Hastie T, Tibshirani R, Friedman JH, Friedman JH (2009) The elements of statistical learning: data mining, inference, and prediction. Springer, New York

  • Jamieson PD, Porter JR, Wilson DR (1991) A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand. Field Crop Res 27:337–350. https://doi.org/10.1016/0378-4290(91)90040-3

    Article  Google Scholar 

  • Jat BL, Dahiya KK, Lal R, Niwas R (2017) Effect of weather parameters on seasonal incidence of pod borer complex in pigeonpea. J Agrometeorol 19(3):255–258

    Article  Google Scholar 

  • Ji B, Sun Y, Yang S, Wan J (2007) Artificial neural networks for rice yield prediction in mountainous regions. J Agric Sci 145(3):249–261. https://doi.org/10.1017/S0021859606006691

    Article  Google Scholar 

  • Kuhn M (2008) Building predictive models in R using caret package. J Stat Softw 28:01–26

    Article  Google Scholar 

  • Kumari P, Mishra GC, Srivastava CP (2016) Statistical models for forecasting pigeonpea yield in Varanasi region. J Agrometeorol 18(2):306–310

    Article  Google Scholar 

  • Kumar IE, Venkatasubramanian S, Scheidegger C, Friedler S (2020) Problems with Shapley-value-based explanations as feature importance measures. In: International Conference on Machine Learning. https://proceedings.mlr.press/v119/kumar20e.html

  • Manoj KN, Umesh MR, Ramesh YM, Anand SR, Angadi S (2019) Dry matter production and radiation use efficiency of pulses grown under different light conditions. Bangladesh J Botany 48(1):9–15

    Article  Google Scholar 

  • Mishra MK, Singh RP, Ali S (2012) Chemical control and avoidable yield losses of Pigeon pea due to insect pests. Ann Plant Protec Sci 20(2):306–309

    Google Scholar 

  • Mishra S, Singh R, Kumar R, Kalia A, Panigrahy SR (2017) Impact of climate change on pigeon pea. Econ Affairs 62(3):455–457. https://doi.org/10.5958/0976-4666.2017.00057.2

    Article  Google Scholar 

  • Nanda MK, Chowdhury S, Madan S, Saha G (2010) Studies on yield limiting meteorological factors for production of rabi pigeon pea in West Bengal. J Agrometeorol 12(1):64–68

    Article  Google Scholar 

  • Pandey KK, Gaind Lal B, Chelak Y (2020) Model development for yield forecasting through principal component analysis and stepwise regression analysis for pigeon pea crop on chhattisgarh plain zone. In: Advances in Agriculture for Doubling of Farmer’s Income, Empyreal Publishing House, India, pp 30–34

  • Paswan RP, Begum SA (2013) Regression and neural networks models for prediction of crop production. Int J Sci Eng Res 4(9):98–108

    Google Scholar 

  • Patil DD, Pandey V, Gurjar R, Patel HP (2018) Effect of intra-seasonal variation in temperature and rainfall on seed yield of pigeon pea cultivars using CROPGRO model. J Agrometeorol 20(4):286–292

    Article  Google Scholar 

  • Piaskowski JL, Brown D, Campbell KG (2016) Near-infrared calibration of soluble stem carbohydrates for predicting drought tolerance in spring wheat. Agron J 108:285–293. https://doi.org/10.2134/agronj2015.0173

    Article  CAS  Google Scholar 

  • Rao AV, Wani SP, Srinivas K, Singh P, Bairagi SD, Ramadevi O (2013) Assessing impacts of projected climate on pigeonpea crop at Gulbarga. J Agrometeorol 15:32–37

    Google Scholar 

  • Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1(5):206–215

    Article  Google Scholar 

  • Saunders C, Stitson MO, Weston J, Bottou L, Smola A (1998) Support vector machine-reference manual. Department of Computer Science, Royal Holloway

  • Sharma SK, Bhagat DV, Ranjeet PD, Khapedia HL, Mirdha IS, Sikarwar RS (2018) Soybean and wheat crop yield forecasting based on statistical model in Malwa agroclimatic zone. Int J Chemic Stud 6(4):1070–1073

    Google Scholar 

  • Shi W, Tao F, Zhang Z (2013) A review on statistical models for identifying climate contributions to crop yields. J Geographic Sci 23(3):567–576

    Article  Google Scholar 

  • Siddique KHM, Loss SP, Regan KL, Jettner RL (1999) Adaptation and seed yield of cool season grain legumes in Mediterranean continents of South Western Australia. Australian J Agric Res 50:75–387. DOI: 10.1071/A98096 Singh RS, Patel C, Yadav MK, Singh KK (2014) Yield forecasting of rice and wheat crops for eastern Uttar Pradesh. J Agrometeorol 16:199–202

    Google Scholar 

  • Singh AK, Singh SS, Ved P, Kumar S, Dwivedi SK (2015) Pulses production in India: present status, sent status, bottleneck and way forward. J. AgriSearch 2(2):75–83

    Google Scholar 

  • Sridhara S, Ramesh N, Gopakkali P, Das B, Venkatappa SD, Sanjivaiah SH, Kumar Singh K, Singh P, El-Ansary DO, Mahmoud EA, Elansary HO (2020) Weather-based neural network, stepwise linear and sparse regression approach for rabi sorghum yield forecasting of Karnataka India. Agron 10:11–1645. https://doi.org/10.3390/agronomy10111645

    Article  Google Scholar 

  • Subharani S, Singh TK (2009) Yield loss assessment and economic injury level of pod borer complex in Pigeon pea. Ann Plant Protec Sci 17(2):299–302

    Google Scholar 

  • Terra FS, Demattê JAM, Rossel RAV (2015) Spectral libraries for quantitative analyses of tropical Brazilian soils: comparing vis– NIR and mid-IR reflectance data. Geoderma 255:81–93

    Article  Google Scholar 

  • Tiwari AK, Shivhare AK, Kumar SV, Bhoware A, Sahare D (2017) Pigeonpea production technology. Directorate of Pulses Development, Bhopal, Government of India 1–12

  • Tripathi AK (2017) Impact of weather parameters on pod borer, phytophthora blight and wilt incidence in Pigeonpea and their management. J Entomol Zool Stud 5:400–402

    Google Scholar 

  • Uno Y, Prasher SO, Lacroix R, Goel PK, Karimi Y, Viau A, Patel RM (2005) Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data. Comput Electron Agric 47:49–161. https://doi.org/10.1016/j.compag.2004.11.014

    Article  Google Scholar 

  • Yadu A, Patel A, Gautam S, Rawat S (2018) Development of multivariate statistical Pigeon pea yield prediction model for Raipur district Chhattisgarh. Int J Chemic Stud 6(6):2782–2785

    Google Scholar 

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Acknowledgements

The authors acknowledge and thank the University of Agricultural and Horticultural Sciences, Shimoga, for technical guidance and support.

Funding

India Meteorological Department (IMD), New Delhi, India, sponsored FASAL project under which the present investigation has been carried out.

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Authors and Affiliations

  1. Center For Climate Resilient Agriculture, University of Agricultural and Horticultural Sciences, Shivamogga, 577204, Karnataka, India

    Shankarappa Sridhara, Konapura Nagaraja Manoj, Pradeep Gopakkali & Girish R. Kashyap

  2. ICAR-Central Coastal Agricultural Research Institute, Old Goa, 403402, Goa, India

    Bappa Das

  3. India Meteorological Department, New Delhi, 110003, India

    Kamlesh Kumar Singh

  4. Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, 53115, Bonn, Germany

    Amit Kumar Srivastava

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  1. Shankarappa Sridhara
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  2. Konapura Nagaraja Manoj
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Correspondence to Shankarappa Sridhara.

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Sridhara, S., Manoj, K.N., Gopakkali, P. et al. Evaluation of machine learning approaches for prediction of pigeon pea yield based on weather parameters in India. Int J Biometeorol 67, 165–180 (2023). https://doi.org/10.1007/s00484-022-02396-x

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