An innovative smart agriculture system utilizing a deep neural network and embedded system to enhance crop yield

A. Gokula  Chandar; K. Sivasankari; S. Leela Lakshmi; S. Sugumaran; S. Kannadhasan; S. Balakumar

doi:10.31893/multiscience.2024065

Keywords:

genetic algorithm

artificial neural network

prediction

crop yield

Abstract

Wheat crop classification and prediction are important tasks for the optimization of crop yield and resource utilization. In this study, we propose an Artificial Neural Network (ANN) model integrated with Genetic Algorithm (GA) to predict and classify the wheat crop images of different ages. The dataset of 19,300 images was used, and the model was trained and tested using various performance evaluation metrics. The results show that the proposed ANN+GA model achieved the highest accuracy of 99.29% during the training phase and 98.65% during the testing phase. The model was also compared with other state-of-the-art machine learning models, and the proposed model was found to be superior in terms of accuracy, specificity, sensitivity, precision, and F-measure. The graph of training and testing accurateness and loss values in contradiction ofindividual epoch demonstrating the speediness of model convergence. Our proposed model is feasible and robust, giving better classification and crop forecast outcomes for numerous wheat crop age groups with least resource necessities. These findings could be useful for farmers and agricultural researchers in improving crop yield and resource management.
References
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20. Shuai, G., & Basso, B. (2022). Subfield maize yield prediction improves when in-season crop water deficit is included in remote sensing imagery-based models. Remote Sensing of Environment, 272, 112938. http://doi.org/10.1016/j.rse.2022.112938
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24. Wang, Z., et al. (2018). The positive effects of secreting cytokines IL-17 and IFN-γ on the early-stage differentiation and negative effects on the calcification of primary osteoblasts in vitro. International Immunopharmacology, 57, 1–10. http:doi.org/10.1016/j.intimp.2018.02.002
25. Zhang, J., Kong, F., Zhai, Z., Wu, J., & Han, S. (2018). Robust Image Segmentation Method for Cotton Leaf Under Natural Conditions Based on Immune Algorithm and PCNN Algorithm. International Journal of Pattern Recognition and Artificial Intelligence, 32, 5, 1854011. http:doi.org/10.1142/S0218001418540113
26. Zhuo, W., et al. (2022). Crop yield prediction using MODIS LAI, TIGGE weather forecasts and WOFOST model: A case study for winter wheat in Hebei, China during 2009–2013. International Journal of Applied Earth Observation and Geoinformation, 106, 102668. http:doi.org/10.1016/j.jag.2021.102668
27. Zhang, Q., et al. (2022). A crop variety yield prediction system based on variety yield data compensation. Computers and Electronics in Agriculture, 203, 107460. http:doi.org/10.1016/j.compag.2022.107460

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Chandar, A. G., Sivasankari, K., Lakshmi, S. L., Sugumaran, S., Kannadhasan, S., & Balakumar, S. (2023). An innovative smart agriculture system utilizing a deep neural network and embedded system to enhance crop yield. Multidisciplinary Science Journal, 6(5), 2024063. https://doi.org/10.31893/multiscience.2024065

[1] Ali, A.M., et al. (2022). Crop Yield Prediction Using Multi Sensors Remote Sensing (Review Article). Egyptian Journal of Remote Sensing and Space Science, 25, 711–716. http:doi.org/10.1016/j.ejrs.2022.04.006

[2] Avneri, A., et al. (2023). UAS-based imaging for prediction of chickpea crop biophysical parameters and yield. Computers and Electronics in Agriculture, 205, 107581. http:doi.org/10.1016/j.compag.2022.107581

[3] Banerjee, S., Saini, A.K., Nigam, H., & Vijay, V.(2020). IoT Instrumented Food and Grain Warehouse Traceability System for Farmers. 2020 International Conference on Artificial Intelligence and Signal Processing, AISP 2020, 3–6. http:doi.org/10.1109/AISP48273.2020.9073248

[4] Brewster, C., Roussaki, I., Kalatzis, N., Doolin, K., & Ellis, K. (2017). IoT in Agriculture: Designing a Europe-Wide Large-Scale Pilot. IEEE Communications Magazine, 55, 9, 26–33., http:doi.org/10.1109/MCOM.2017.1600528

[5] Bregaglio, S., Ginaldi, F., Raparelli, E., Fila, G., & Bajocco, S. (2033). Improving crop yield prediction accuracy by embedding phenological heterogeneity into model parameter sets. Agricultural Systems, 209, 103666. http://doi.org/10.1016/j.agsy.2023.103666

[6] Harakannanavar, S.S., Rudagi, J.M., Puranikmath, V.I., Siddiqua, A., & Pramodhini, R. (2022). Plant leaf disease detection using computer vision and machine learning algorithms. Global Transitions Proceedings, 3,1, 305–310. http:doi.org/10.1016/j.gltp.2022.03.016

[7] Iniyan, S., Akhil Varma, V., & Teja Naidu, C. (2022). Crop yield prediction using machine learning techniques. Advances in Engineering Software, 175, 103326. http:doi.org/10.1016/j.advengsoft.2022.103326

[8] Jhajharia, K., Mathur, P., Jain, S., & Nijhawan, S. (2023). Crop Yield Prediction using Machine Learning and Deep Learning Techniques. Procedia Computer Science, 218, 406–417. http:doi.org/10.1016/j.procs.2023.01.023

[9] Kiruthika, J.K., Sudha, M., Divya, G., Indhu, R., & Yawanikha, T. (2022). Identification of Maize Plant Leaf Disease Detection using ConvNet Model. 8th International Conference on Smart Structures and Systems, ICSSS 2022, 29–31. http:doi.org/10.1109/ICSSS54381.2022.9782225

[10] Kaur, A., Goyal, P., Rajhans, R., Agarwal, L., & Goyal, N. (2023). Fusion of multivariate time series meteorological and static soil data for multistage crop yield prediction using multi-head self attention network. Expert Systems with Applications, 226, 120098.http://doi.org/10.1016/j.eswa.2023.120098

[11] Kalaiarasi, E., & Anbarasi, A. (2022). Multi-parametric multiple kernel deep neural network for crop yield prediction. Materials Today: Proceedings, 62, 115, 4635–4642. http://doi.org/ 10.1016/j.matpr.2022.03.115

[12] Li, Z., Ding, L., & Xu, D. (2022). Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China. Science of the Total Environment ,815, 152880. http://doi.org/10.1016/j.scitotenv.2021.152880

[13] Li, B., Long, Y., & Song, H. (2018). Detection of green apples in natural scenes based on saliency theory and gaussian curve fitting. International Journal of Agricultural and Biological Engineering, 11, 1, 192–198. http:doi.org/10.25165/j.ijabe.20181101.2899

[14] Måløy, H., Windju, S., Bergersen, S., Alsheikh, M., & Downing, K.L. (2021). Multimodal performers for genomic selection and crop yield prediction. Smart Agricultural Technology, 1, 100017. http://doi.org/ 10.1016/j.atech.2021.100017

[15] Pant, J., Pant, R.P., Kumar Singh, M., Pratap Singh, D., & Pant, H. (2021). Analysis of agricultural crop yield prediction using statistical techniques of machine learning. Materials Today: Proceedings, 46, 10922–10926. http:doi.org/10.1016/j.matpr.2021.01.948

[16] Qiao, M., et al. (2021). Crop yield prediction from multi-spectral, multi-temporal remotely sensed imagery using recurrent 3D convolutional neural networks. International Journal of Applied Earth Observation and Geoinformation, 102, 102436. http:doi.org/10.1016/j.jag.2021.102436

[17] Qiao, M., et al.(2023). KSTAGE: A knowledge-guided spatial-temporal attention graph learning network for crop yield prediction. Information Sciences, 619, 19–37. http://doi.org/10.1016/j.ins.2022.10.112

[18] Romero-Vergel, A.P. (2022). TURION: A physiological crop model for yield prediction of asparagus using sentinel-1 data. European Journal of Agronomy, 143, 126690. http://doi.org/10.1016/j.eja.2022.126690

[19] Sood, S., & Singh, H. (2020). An implementation and analysis of deep learning models for the detection of wheat rust disease. Proceedings of the 3rd International Conference on Intelligent Sustainable Systems, ICISS 2020, 341–347. http:doi.org/10.1109/ICISS49785.2020.9316123

[20] Shuai, G., & Basso, B. (2022). Subfield maize yield prediction improves when in-season crop water deficit is included in remote sensing imagery-based models. Remote Sensing of Environment, 272, 112938. http://doi.org/10.1016/j.rse.2022.112938

[21] Sivanantham, V., et al.(2022). Quantile correlative deep feedforward multilayer perceptron for crop yield prediction. Computers and Electrical Engineering, 98, 107696. http://doi.org/ 10.1016/j.compeleceng.2022.107696

[22] Tian, L., Wang, C., Li, H., & Sun, H. (2020). Yield prediction model of rice and wheat crops based on ecological distance algorithm. Environmental Technology and Innovation, 20, 101132. http://doi.org/10.1016/j.eti.2020.101132

[23] Van Klompenburg, T., Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture, 177, 105709. http:doi.org/10.1016/j.compag.2020.105709

[24] Wang, Z., et al. (2018). The positive effects of secreting cytokines IL-17 and IFN-γ on the early-stage differentiation and negative effects on the calcification of primary osteoblasts in vitro. International Immunopharmacology, 57, 1–10. http:doi.org/10.1016/j.intimp.2018.02.002

[25] Zhang, J., Kong, F., Zhai, Z., Wu, J., & Han, S. (2018). Robust Image Segmentation Method for Cotton Leaf Under Natural Conditions Based on Immune Algorithm and PCNN Algorithm. International Journal of Pattern Recognition and Artificial Intelligence, 32, 5, 1854011. http:doi.org/10.1142/S0218001418540113

[26] Zhuo, W., et al. (2022). Crop yield prediction using MODIS LAI, TIGGE weather forecasts and WOFOST model: A case study for winter wheat in Hebei, China during 2009–2013. International Journal of Applied Earth Observation and Geoinformation, 106, 102668. http:doi.org/10.1016/j.jag.2021.102668

[27] Zhang, Q., et al. (2022). A crop variety yield prediction system based on variety yield data compensation. Computers and Electronics in Agriculture, 203, 107460. http:doi.org/10.1016/j.compag.2022.107460

Abstract

References

How to cite