Deep Neural Network and Multiparametric Deep Neural Networks for Harvest Predictions
Keywords:
Agriculture, Crop Yield, Deep Neural Network, Harvest, Hyperparameters, Multiparametric Neural NetworksAbstract
Deep Neural Network is a Deep Learning technique which was used for predicting the crop yields. To forecast various crop yields, the Deep Neural Network(DNN) used a series of non-linear layers, which, at each level, abstracted the original meteorological and soil data. The paper proposes use of the Multiparametric Deep Neural Network(MDNN) to simulate various soil properties, climatic variables such as temperature, vapour pressure, cloud coverage, frequency of the damp days and humidity and other climatic variations in order to forecast the agricultural yields outperforming the existing DNN. In order to improve the accuracy the MDNN uses the data from the past. This paper presents an improvement of accuracy of the harvest predictions which will help farmers and officials which aid for better yield. Results from the experiments show that in predicting the agricultural yields of the selected crops the MDNN performs better than the DNN.
References
Chen, Y., Lee, W. S., Gan, H., Peres, N., Fraisse, C., Zhang, Y., & He, Y, “Strawberry Yield Prediction Based on a Deep Neural Network using High-Resolution Aerial Orthoimages”, Remote Sensing, Vol.11, Issue.13, pp.1584, 2019.
Crane-Droesch, A.,“Machine Learning Methods for Crop Yield Prediction and Climate Change Impact Assessment in Agriculture”, Environmental Research Letters, Vol.13, Issue.11, pp.114003, 2018.
Khaki, S., & Wang, L., “Crop Yield Prediction using Deep Neural Networks”, Frontiers in Plant Science, 10, 621, 2019.
Nigam, A., Garg, S., Agrawal, A., & Agrawal, P., “Crop Yield Prediction using Machine Learning Algorithms”, IEEE Fifth International Conference on Image Information Processing (ICIIP) pp.125-130, 2019.
Ahmad, I., Saeed, U., Fahad, M., Ullah, A., Habib ur Rahman, M., Ahmad, A., & Judge, J., “Yield Forecasting of Spring Maize using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan”, Journal of the Indian Society of Remote Sensing, 46, pp.1701-1711, 2019.
Hanuman, V., Pinnamaneni, K.V, & Singh, T.,“Best Fit Radial Kernel Support Vector Machine for Intelligent Crop Yield Prediction Method. In Machine Learning and Information Processing”, Proceedings of ICMLIP, pp.457-467, 2020.
Zhang, Q., Liu, Y., Gong, C., Chen, Y., & Yu, H.,“Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review”, Sensors, Vol.20, Issue.5, pp.1520, 2020.
Bali, N., & Singla, A.,“Deep Learning Based Wheat Crop Yield Prediction Model in Punjab Region of North India, Applied Artificial Intelligence”, Vol.35, Issue.15, pp.1304-1328, 2021.
Wang, X., Huang, J., Feng, Q., & Yin, D.,“Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-Producing Regions of China with Deep Learning Approach”, Remote Sensing, Vol.12, Issue.11, pp.1744, 2020.
Rather, N. A., Lone, P. A., Reshi, A. A., & Mir, M. M.,“An Analytical Study on Production and Export of Fresh and Dry Fruits in Jammu and Kashmir”,International Journal of Scientific and Research Publications, Vol.3, Issue.2, pp.1-7, 2013.
Aslam, M. A., Ahmed, M., Stöckle, C. O., Higgins, S. S., Hassan, F. U., & Hayat, R.. “Can Growing Degree Days and Photoperiod Predict Spring Wheat Phenology. Frontiers in Environmental Science”, 5, 57, 2017.
Tedesco-Oliveira, D., da Silva, R. P., Maldonado Jr, W., & Zerbato, C, “Convolutional Neural Networks in Predicting Cotton Yield from Images of Commercial Fields. Computers and Electronics in Agriculture”, 171, 105307, 2020.
S. Ashok Kumar, K.P. Rajesh, "Hyper-Parameters Activation on Machine Learning Algorithms to Improve the Recognition of Human Activities with IoT Sensor Dataset", Indian Journal of Science and Technology, Vol.16, Issue.35, pp.2856-2867, 2023. DOI:10.17485/IJST/v16i35.882.
Wang, Z., Ye, T., Wang, J., Cheng, Z., & Shi, P.,“Contribution of Climatic and Technological Factors to Crop Yield: Empirical Evidence from Late Paddy Rice in Hunan Province, China. Stochastic Environmental Research and Risk Assessment”, 2016.
Yue, Y., Li, J. H., Fan, L. F., Zhang, L. L., Zhao, P. F., Zhou, Q., & Dong, X. H,, “Prediction of Maize Growth Stages Based on Deep Learning”. Computers and Electronics In Agriculture, 172, 105351, 2020.
Hara, P., Piekutowska, M., & Niedba?a, G.,“Selection of Independent Variables for Crop Yield Prediction using Artificial Neural Network Models with Remote Sensing Data, Land”, 10(6), 609, 2021.
Mortada M. Abdulwahab, Hadeel A.Mohamed, Mutseuim A. Alameen, Mohamed A. Mosalam, Faris M.Elsadig,“Wireless Sensor Network of Monitoring Water Distribution Network Service using IoT", International Journal of Scientific Research in Computer Science and Engineering, Vol.11, Issue.1, pp.51-55, 2023.
Mamunur Rashid , Bifta Sama Bari , Yusri Yusup , Mohamad Anuar Kamaruddin , And Nuzhat Khan,“A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction”, IEEE Access date of current version Vol.9, 2021.
Abid Badshah , Basem Yousef Alkazemi , Fakhrud Din , Kamal Z. Zamli , Muhammad Hari,“Crop Classification and Yield Prediction Using Robust Machine Learning Models for Agricultural Sustainability”, IEEE Access, Vol.12, 2024 .
Nisha Thakur, Sanjeev Karmakar,“Deep Learning Approach using Long Short Term Memory Technique for Monthly Rainfall Prediction in Chhattisgarh, India", International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.1, pp.8-13, 2021.
Venkata Rama Rao Kolipaka, Anupama Namburu,“Crop Yield Prediction using Machine Learning and Deep Learning Techniques”, International Journal on Recent and Innovation Trends in Computing and Communication, ISSN:2321-8169 Vol. 11, Issue.10, 2023.
Leelavathi Kandasamy Subramaniam, Rajasenathipathi Marimuthu,“Crop Yield Prediction using Effective Deep Learning and Dimensionality Reduction Approaches for Indian Regional Crop”,E-Prime Advances in Electrical Engineering ,Electronics and Energy, Vol.8, 100611, 2024.
Thogaru Mallika, Selvani Deepthi Kavila, Balamurali Pydi, Budumuru Rajesh,“A Novel Method for Prediction of Crop Yield Using Deep Neural Networks”, International Journal of Intelligent Systems And Applications In Engineering, IJISAE, Vol.11, Issue.4, pp.308–315, 2023.
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