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Che-Hao Chang, Jason Lin, Jia-Wei Chang, Yu-Shun Huang, Ming-Hsin Lai and Yen-Jen Chang
Recently, data-driven approaches have become the dominant solution for prediction problems in agricultural industries. Several deep learning models have been applied to crop yield prediction in smart farming. In this paper, we proposed an efficient hybri...
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Adriano Mancini, Francesco Solfanelli, Luca Coviello, Francesco Maria Martini, Serena Mandolesi and Raffaele Zanoli
Yield prediction is a crucial activity in scheduling agronomic operations and in informing the management and financial decisions of a wide range of stakeholders of the organic durum wheat supply chain. This research aims to develop a yield forecasting s...
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Sadia Alam Shammi, Yanbo Huang, Gary Feng, Haile Tewolde, Xin Zhang, Johnie Jenkins and Mark Shankle
The application of remote sensing, which is non-destructive and cost-efficient, has been widely used in crop monitoring and management. This study used a built-in multispectral imager on a small unmanned aerial vehicle (UAV) to capture multispectral imag...
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Zhiyang Li, Zhigang Nie and Guang Li
One of the crucial research areas in agricultural decision-making processes is crop yield prediction. This study leverages the advantages of hybrid models to address the complex interplay of genetic, environmental, and management factors to achieve more ...
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Luana Centorame, Thomas Gasperini, Alessio Ilari, Andrea Del Gatto and Ester Foppa Pedretti
Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, ...
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Zhongyu Yang, Zirui Yu, Xiaoyun Wang, Wugeng Yan, Shijie Sun, Meichen Feng, Jingjing Sun, Pengyan Su, Xinkai Sun, Zhigang Wang, Chenbo Yang, Chao Wang, Yu Zhao, Lujie Xiao, Xiaoyan Song, Meijun Zhang and Wude Yang
Aboveground biomass (AGB) is a key parameter reflecting crop growth which plays a vital role in agricultural management and ecosystem assessment. Real-time and non-destructive biomass monitoring is essential for accurate field management and crop yield p...
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Hui Deng, Wenjiang Zhang, Xiaoqian Zheng and Houxi Zhang
The accurate and timely identification of crops holds paramount significance for effective crop management and yield estimation. Unmanned aerial vehicle (UAV), with their superior spatial and temporal resolution compared to satellite-based remote sensing...
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Ning Wang, Zhong Ma, Pengcheng Huo, Xi Liu, Zhao He and Kedi Lu
Crop yield prediction is essential for tasks like determining the optimal profile of crops to be planted, allocating government resources, effectively planning and preparing for aid distribution, making decisions about imports, and so on. Crop yield pred...
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Yanxi Zhao, Dengpan Xiao, Huizi Bai, Jianzhao Tang, De Li Liu, Yongqing Qi and Yanjun Shen
The accuracy prediction for the crop yield is conducive to the food security in regions and/or nations. To some extent, the prediction model for crop yields combining the crop mechanism model with statistical regression model (SRM) can improve the timeli...
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Mohammed Baljon and Sunil Kumar Sharma
Every farmer requires access to rainfall prediction (RP) to continue their exploration of harvest yield. The proper use of water assets, the successful collection of water, and the successful pre-growth of water construction all depend on an accurate ass...
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