Ensemble Deep Learning Algorithm for Forecasting of Rice Crop Yield based on Soil Nutrition Levels
DOI:
https://doi.org/10.4108/eetsis.v10i3.2610Keywords:
Support Vector Machine, Deep Neural Network, Deep Belief Network, ensemble learning, Model Agnostic Meta LearningAbstract
Agriculture is critical to the development of a growing country like India. For the vast majority of the population, agriculture is their primary source of income. Crop yield estimates that are accurate and timely can give crucial information for determining agriculture policy and making investments. Crop yield forecasting and prediction will boost agricultural productivity, while crop rotation will improve soil fertility. When farmers are unaware of the soil nutrition and composition, crop yields are reduced to a minimum. To address these concerns, the proposed methodology creates an ensemble deep learning system for predicting rice crop production based on soil nutrition levels. Soil nutrients and crop production statistics are taken as the input for the proposed method. The soil nutrients dataset contains different nutrients level in the soil. Crop production statistics are the amount of crop yield in a particular area. Normalization and mean of the attribute techniques are used as pre-processing approaches to fill the missing values in the input dataset. The suggested process utilizes a stacking-based ensemble deep learning strategy termed Model Agnostic Meta-Learning (MAML) for classification. MAML receives output from three different classifiers, including Deep Neural Network (DNN), Deep Belief Network (DBN) and Support Vector Machine (SVM). Then the MAML produce the final output as how much amount of rice crop is predicted in the particular soil. The proposed method provides better accuracy of 89.5%. Thus the designed model predicted the crop yield prediction in an effective manner.
References
Sharma B, Yadav JK, Yadav S. Predict crop production in India using machine learning technique: A survey. In2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) 2020 Jun 4 (pp. 993-997). IEEE
Fischer S, Hilger T, Piepho HP, Jordan I, Karungi J, Towett E, Shepherd K, Cadisch G. Soil and farm management effects on yield and nutrient concentrations of food crops in East Africa. Science of the Total Environment. 2020 May 10; 716:137078.
Van Klompenburg T, Kassahun A, Catal C. Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture. 2020 Oct 1; 177:105709.
Pandith V, Kour H, Singh S, Manhas J, Sharma V. Performance evaluation of machine learning techniques for mustard crop yield prediction from soil analysis. Journal of Scientific Research. 2020; 64(2):394-8.
Hara P, Piekutowska M, Niedbała G. Selection of independent variables for crop yield prediction using artificial neural network models with remote sensing data. Land. 2021 Jun 7; 10(6):609.
Pant J, Pant RP, Singh MK, Singh DP, Pant H. Analysis of agricultural crop yield prediction using statistical techniques of machine learning. Materials Today: Proceedings. 2021 Jan 1; 46:10922-6.
Ma Y, Zhang Z, Kang Y, Özdoğan M. Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach. Remote Sensing of Environment. 2021 Jun 15; 259:112408.
Kalimuthu M, Vaishnavi P, Kishore M. Crop prediction using machine learning. In2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT) 2020 Aug 20 (pp. 926-932). IEEE.
Sharifi A. Yield prediction with machine learning algorithms and satellite images. Journal of the Science of Food and Agriculture. 2021 Feb; 101(3):891-6.
Panigrahi KP, Das H, Sahoo AK, Moharana SC. Maize leaf disease detection and classification using machine learning algorithms. In Progress in Computing, Analytics and Networking 2020 (pp. 659-669). Springer, Singapore.
Yin, J., Tang, M., Cao, J., Wang, H., & You, M. (2022). A real-time dynamic concept adaptive learning algorithm for exploitability prediction. Neurocomputing, 472, 252-265.
Alvi, A. M., Siuly, S., Wang, H., Wang, K., & Whittaker, F. (2022). A deep learning based framework for diagnosis of mild cognitive impairment. Knowledge-Based Systems, 248, 108815.
Kamath P, Patil P, Shrilatha S, Sowmya S. Crop yield forecasting using data mining. Global Transitions Proceedings. 2021 Nov 1; 2(2):402-7.
Malik P, Sengupta S, Jadon JS. Comparative analysis of soil properties to predict fertility and crop yield using machine learning algorithms. In2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence) 2021 Jan 28 (pp. 1004-1007). IEEE.
Li L, Wang B, Feng P, Wang H, He Q, Wang Y, Li Liu D, Li Y, He J, Feng H, Yang G. Crop yield forecasting and associated optimum lead time analysis based on multi-source environmental data across China. Agricultural and Forest Meteorology. 2021 Oct 15; 308:108558.
Nazir A, Ullah S, Saqib ZA, Abbas A, Ali A, Iqbal MS, Hussain K, Shakir M, Shah M, Butt MU. Estimation and forecasting of rice yield using phenology-based algorithm and linear regression model on sentinel-ii satellite data. Agriculture. 2021 Oct 19; 11(10):1026.
Iniyan S, Jebakumar R. Mutual information feature selection (MIFS) based crop yield prediction on corn and soybean crops using multilayer stacked ensemble regression (MSER). Wireless Personal Communications. 2021 Jun 30:1-30.
Jeong S, Ko J, Yeom JM. Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea. Science of the Total Environment. 2022 Jan 1; 802:149726.
Oikonomidis A, Catal C, Kassahun A. Hybrid Deep Learning-based Models for Crop Yield Prediction. Applied artificial intelligence. 2022 Jan 24:1-8.
Nain G, Bhardwaj N, Jaslam PM, Dagar CS. Rice yield forecasting using agro-meteorological variables: A multivariate approach. Journal of Agrometeorology. 2021 Mar 1; 23(1):100-5.
Ajithkumar B. Rice yield forecasting using principal component regression and composite weather variables. Journal of Pharmacognosy and Phytochemistry. 2021; 10(2):595-600.
Kandan M, Niharika GS, Lakshmi MJ, Manikanta K, Bhavith K. Implementation of Crop Yield Forecasting System based on Climatic and Agricultural Parameters. In2021 IEEE International Conference on Intelligent Systems, Smart and Green Technologies (ICISSGT) 2021
Ahlawat S, Choudhary A. Hybrid CNN-SVM classifier for handwritten digit recognition. Procedia Computer Science. 2020 Jan 1; 167:2554-60.
Alfian G, Syafrudin M, Fitriyani NL, Anshari M, Stasa P, Svub J, Rhee J. Deep neural network for predicting diabetic retinopathy from risk factors. Mathematics. 2020 Sep 19;8(9):1620.
Li F, Zhang J, Shang C, Huang D, Oko E, Wang M. Modelling of a post-combustion CO2 capture process using deep belief network. Applied Thermal Engineering. 2018 Feb 5; 130:997-1003.
Usman SM, Khalid S, Bashir S. A deep learning based ensemble learning method for epileptic seizure prediction. Computers in Biology and Medicine. 2021 Sep 1; 136:104710.
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