Abstract
Crop yield prediction is every nation’s most important and difficult task. Due to the frequent climate changes, farmers struggle to achieve a high yield. This work presents an effective methodology for crop yield prediction of major crop production. In the presented methodology, crop yield is predicted by considering soil health data, crop production data and rainfall data. The input soil nutrients, weather and crop production data are processed. The input data is collected from the Uttar Pradesh (UP) region. In the first phase, the soil fertility index estimates the soil quality. In the second phase, the soil quality score and other crop yield related parameters like rainfall and crop production data are taken for further processing. Initially, the input soil health, crop production, and rainfall data are pre-processed. In the pre-processing stage, data cleaning with if-null processing and min-max normalization is used for data standardization. The features are then selected using the adaptive bald eagle search optimization (ABES) approach. Finally, the crop yield prediction of sugarcane, wheat and rice crops is obtained accurately by utilizing a hybrid deep capsule auto encoder with a softmax regression (Hybrid DCAS) model. Here, the hyper-parameter tuning of the presented deep learning model is achieved by a modified Flamingo Search (MFS) optimization approach. The overall implementation is carried out on PYTHON. The performance of the developed model is compared with other models in terms of classification and prediction performance.
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References
Alsattar HA, Zaidan AA, Zaidan BB (2020) Novel meta-heuristic bald eagle search optimization algorithm. Artif Intell Rev 53(3):2237–2264
Bhojani SH, Bhatt N (2020) Wheat crop yield prediction using new activation functions in neural network. Neural Comput & Applic 32(17):13941–13951
Bouwma I, Zinngrebe Y, Runhaar H (2019) Nature conservation and agriculture: two EU policy domains that finally meet? In EU Bioeconomy Economics and Policies: Palgrave Macmillan, Cham, II: 153–175
Dang C, Liu Y, Yue H, Qian J, Zhu R (2021) Autumn crop yield prediction using data-driven approaches:-support vector machines, random forest, and deep neural network methods. Can J Remote Sens 47(2):162–181
Elavarasan D, Vincent PD (2020) Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications. IEEE Access 8:86886–86901
Elavarasan D, Vincent PM (2021) A reinforced random forest model for enhanced crop yield prediction by integrating agrarian parameters. J Ambient Intell Humaniz Comput 12(11):10009–10022
FAO RICE PRICE UPDATE (2018a) Food and Agriculture Organization of the United Nations (FAO), Rome
Filippi P, Jones EJ, Wimalathunge NS, Somarathna PD, Pozza LE, Ugbaje SU, Jephcott TG, Paterson SE, Whelan BM, Bishop TF (2019) An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning. Precis Agric 20(5):1015–1029
Gomez-Zavaglia A, Mejuto JC, Simal-Gandara J (2020) Mitigation of emerging implications of climate change on food production systems. Food Res Int 134:109256
Gong L, Yu M, Jiang S, Cutsuridis V, Pearson S (2021) Deep learning based prediction on greenhouse crop yield combined TCN and RNN. Sensors 21(13):4537
Gopal PM, Bhargavi R (2019) A novel approach for efficient crop yield prediction. Comput Electron Agric 165:104968
Gulati A, Terway P, Hussain S (2021) Performance of agriculture in Uttar Pradesh. Revital Indian Agric Boosting Farmer Incomes, 175
Hammer RG, Sentelhas PC, Mariano JC (2020) Sugarcane yield prediction through data mining and crop simulation models. Sugar Tech 22(2):216–225
Hu MF, Liu JW, Li WM (2021) Learning optimal primary capsules by information bottleneck. In international conference on artificial neural networks, springer, Cham 519-528
Iaksch J, Fernandes E, Borsato M (2021) Digitalization and big data in smart farming–a review. J Management Anal 8(2):333–349
Jain S, Shukla S, Wadhvani R (2018) Dynamic selection of normalization techniques using data complexity measures. Expert Syst Appl 106:252–262
Joshua V, Priyadharson SM, Kannadasan R (2021) Exploration of machine learning approaches for paddy yield prediction in eastern part of Tamilnadu. Agronomy 11(10):2068
Khaki S, Wang L (2019) Crop yield prediction using deep neural networks. Front Plant Sci 10:621
Khaki S, Wang L, Archontoulis SV (2020) A cnn-rnn framework for crop yield prediction. Front Plant Sci 10:1750
Khalil ZH, Abdullaev SM (2021) Neural network for grain yield predicting based multispectral satellite imagery: comparative study. Procedia Comput Sci 186:269–278
Lata S (2019) Irrigation water Management for Agricultural Development in Uttar Pradesh. Springer International Publishing, India
Li W, Luo Y, Zhu Q, Liu J, Le J (2008) Applications of AR*-GRNN model for financial time series forecasting. Neural Comput & Applic 17(5):441–448
Luo Y, Jiang J, Zhu J, Huang Q, Li W, Wang Y, Gao Y (2022) A caps-Ubi model for protein ubiquitination site prediction. Front Plant Sci, 1582
Miriyala GP, Sinha AK (2020) Prediction of crop yield using deep learning techniques: a concise review. Recent Advances in Computer Based Systems, Processes and Applications, 145–159
Nevavuori P, Narra N, Lipping T (2019) Crop yield prediction with deep convolutional neural networks. Comput Electron Agric 163:104859
Nosratabadi S, Szell K, Beszedes B, Imre F, Ardabili S, Mosavi A (2020) Comparative analysis of ANN-ICA and ANN-GWO for crop yield prediction. In 2020 RIVF Int Conf Comput Commun Technol (RIVF) IEEE 1-5
Oikonomidis A, Catal C, Kassahun A (2022) Hybrid deep learning-based models for crop yield prediction. Appl Artif Intell, 1-18
Reddy DJ, Kumar MR (2021) Crop yield prediction using machine learning algorithm. In 2021 5th international conference on intelligent computing and control systems (ICICCS), IEEE, 1466-1470
Saxena S, Kumar A (2019) Economic analysis of climate change impact, adaptation and mitigation on potato farming in India with special reference to Agra district. Indian J Econ Dev, 7(3)
Sharma KK, Singh AK, Dubey SK (2018) Rainfall trend analysis for crop planning under rainfed conditions in district Agra of Uttar Pradesh. MAUSAM 69(4):599–606
Sharma S, Rai S, Krishnan NC (2020) Wheat crop yield prediction using deep LSTM model. arXiv preprint arXiv:2011.01498
Singh S (2019) Determinants of agriculture production in Uttar Pradesh, India: a regional analysis. Res Rev Int J Multidiscip 4:1–14
Sood S, Singh H (2021) Computer vision and machine learning based approaches for food security: a review. Multimed Tools Appl 80(18):27973–27999
Sun J, Di L, Sun Z, Shen Y, Lai Z (2019) County-level soybean yield prediction using deep CNN-LSTM model. Sensors 19(20):4363
Talaviya T, Shah D, Patel N, Yagnik H, Shah M (2020) Implementation of artificial intelligence in agriculture for optimization of irrigation and application of pesticides and herbicides. Artif Intell Agric 4:58–73
Xi E, Bing S, Jin Y (2017) Capsule network performance on complex data. arXiv preprint arXiv:1712.03480
Xu X, Gao P, Zhu X, Guo W, Ding J, Li C, Zhu M, Wu X (2019) Design of an integrated climatic assessment indicator (ICAI) for wheat production: a case study in Jiangsu Province, China. Ecol Indic 101:943–953
Zhiheng W, Jianhua L (2021) Flamingo search algorithm: a new swarm intelligence optimization algorithm. IEEE Access 9:88564–88582
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Kumar, R., Pandey, S. An accurate prediction of crop yield using hybrid deep capsule auto encoder with softmax regression. Multimed Tools Appl 82, 15371–15393 (2023). https://doi.org/10.1007/s11042-022-13919-4
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DOI: https://doi.org/10.1007/s11042-022-13919-4