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| Filter results6 paper(s) found. |
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1. Inversion of Potato Chlorophyll Content Based on Radiation Transfer Model and Machine Learning AlgorithmLeaf chlorophyll content (LCC) significantly correlates with crop growth conditions, nitrogen content, yield, etc. It is a crucial indicator for elucidating the senescence process of plants and can reflect their growth and nutrition status. However, the performance of traditional LCC inversion models is limited by the quality and scale of training data. It is difficult to satisfy the needs of precision agriculture. 【Objective】Therefore, this study proposes a hybrid modeling framework based... Y. Ma, J. Zhang, D. Pan, Q. Wu, S. Xiaoyu, X. Xu |
2. Hybrid Fuzzy–pid Control for Variable-rate Center Pivot Irrigation: an Automation-driven Approach to Precision Water ManagementPrecision agriculture increasingly relies on advanced automation and intelligent control strategies to address the spatial and temporal variability of crop water requirements while minimizing resource consumption. Center pivot irrigation systems are widely deployed in large-scale farming operations; however, their conventional control architectures are typically based on fixed schedules or linear feedback laws, which are insufficient to handle the nonlinear dynamics, uncertainties, and disturbances... F.R. Jimenez Lopez, A. Jimenez, I.A. Ruge Ruge, D.Y. Garcia Ramirez |
3. Deep Learning-based Anomaly Detection System for Rice Crop Health MonitoringGlobal food security relies heavily on the stable production of rice (Oryza sativa L.), yet cultivation remains vulnerable to various phytosanitary anomalies, including foliar diseases like Pyricularia and Rhynchosporium, scald, and abiotic stressors such as herbicide damage. Traditional agronomic management relies on visual scouting, which is inherently subjective, labor-intensive, and often leads to delayed interventions. This study proposes an automated, high-throughput solution for real-time... F.R. Jimenez Lopez, A. Jimenez, D.Y. Garcia Ramirez |
4. Study on the Phenological Zoning Method for Winter Wheat in the Huang-Huai-Hai Region of ChinaThe impact of global climate change on agricultural phenology is becoming increasingly significant. As a major producer of winter wheat, China's cultivation areas span multiple climate zones. Against the backdrop of climate change, the spatiotemporal differentiation of crop phenology has raised new scientific demands for agricultural zoning. Phenological zoning has guiding significance for variety selection, irrigation management, and pest prediction. However, existing research often relies... S. Xiaoyu, Q. Wu, Y. Ma, J. Zhang, P. Dong, X. Xu |
5. Estimation of Sugarcane Yield Based on Phenological Feature Extraction from Time-Series Sentinel-1 Images and Machine LearningDue to frequently rainy and cloudy weather in the main sugarcane production areas, optical remote sensing data are often missing, and the conventional yield estimation models based on radar remote sensing data lack the support of crop growth mechanisms. This study aims to explore a new yield estimation method for capturing the key dynamic growth features of sugarcane under all-weather conditions. This study takes the sugarcane yield in the dominant area of sugarcane production, Guangxi Zhuang... H. Xue, X. Xu, G. Yang, Z. Xu, S. Xiaoyu, L. Chen |
6. UAV-Based Multispectral Modelling of Biomass and Crude Protein Yield for Green Biorefinery ApplicationsIn animal production systems, protein demand is steadily increasing due to global population growth. This rising demand has highlighted the need to identify alternative and sustainable protein sources. Green biorefinery systems can efficiently extract protein from plant biomass. Previous studies confirmed that perennial grass crops such as Perennial Ryegrass, Festulolium, and Tall Fescue can produce high-quality biomass suitable for protein extraction. An estimation model of biomass yield and... M. Canciani, E. Han, U. Jørgensen, Y. Ikeda, N.P. Hansen, S.K. Jensen, M.R. Weisbjerg, T. Didion |