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Master's Degree, Northwest Agriculture and Forestry University, China.The main research area is multi-robot wireless communication technology for agriculture.
Project Goal: Research and implementation of multi-robot communication system for orchards
Current Stage: Being tested
Project Goal: Wireless communication technology and control for agricultural robots
Current Stage: Complete
The leaf area index (LAI) is a key parameter for describing the canopy structure of apple trees. This index is also employed in evaluating the amount of pesticide sprayed per unit volume of apple trees. Hence, numerous manual and automatic methods have been explored for LAI estimation. In this work, the leaf area indices for different types of apple trees are obtained in terms of multispectral remote-sensing data collected with an unmanned aerial vehicle (UAV), along with simultaneous measurements of apple orchards. The proposed approach was tested on apple trees of the “Fuji”, “Golden Delicious”, and “Ruixue” types, which were planted in the Apple Experimental Station of the Northwest Agriculture and Forestry University in Baishui County, Shaanxi Province, China. Five vegetation indices of strong correlation with the apple leaf area index were selected and used to train models of support vector regression (SVR) and gradient-boosting decision trees (GBDT) for predicting the leaf area index of apple trees. The best model was selected based on the metrics of the coefficient of determination (R2) and the root-mean-square error (RMSE). The experimental results showed that the gradient-boosting decision tree model achieved the best performance with an R2 of 0.846, an RMSE of 0.356, and a spatial efficiency (SPAEF) of 0.57. This demonstrates the feasibility of our approach for fast and accurate remote-sensing-based estimation of the leaf area index of apple trees.
Zhijie Liu; Pengju Guo; Heng Liu; Pan Fan; Pengzong Zeng; Xiangyang Liu; Ce Feng; Wang Wang; Fuzeng Yang. Gradient Boosting Estimation of the Leaf Area Index of Apple Orchards in UAV Remote Sensing. Remote Sensing 2021, 13, 3263 .
AMA StyleZhijie Liu, Pengju Guo, Heng Liu, Pan Fan, Pengzong Zeng, Xiangyang Liu, Ce Feng, Wang Wang, Fuzeng Yang. Gradient Boosting Estimation of the Leaf Area Index of Apple Orchards in UAV Remote Sensing. Remote Sensing. 2021; 13 (16):3263.
Chicago/Turabian StyleZhijie Liu; Pengju Guo; Heng Liu; Pan Fan; Pengzong Zeng; Xiangyang Liu; Ce Feng; Wang Wang; Fuzeng Yang. 2021. "Gradient Boosting Estimation of the Leaf Area Index of Apple Orchards in UAV Remote Sensing." Remote Sensing 13, no. 16: 3263.
Multi-robots have shown good application prospects in agricultural production. Studying the synergistic technologies of agricultural multi-robots can not only improve the efficiency of the overall robot system and meet the needs of precision farming but also solve the problems of decreasing effective labor supply and increasing labor costs in agriculture. Therefore, starting from the point of view of an agricultural multiple robot system architectures, this paper reviews the representative research results of five synergistic technologies of agricultural multi-robots in recent years, namely, environment perception, task allocation, path planning, formation control, and communication, and summarizes the technological progress and development characteristics of these five technologies. Finally, because of these development characteristics, it is shown that the trends and research focus for agricultural multi-robots are to optimize the existing technologies and apply them to a variety of agricultural multi-robots, such as building a hybrid architecture of multi-robot systems, SLAM (simultaneous localization and mapping), cooperation learning of robots, hybrid path planning and formation reconstruction. While synergistic technologies of agricultural multi-robots are extremely challenging in production, in combination with previous research results for real agricultural multi-robots and social development demand, we conclude that it is realistic to expect automated multi-robot systems in the future.
Wenju Mao; Zhijie Liu; Heng Liu; Fuzeng Yang; Meirong Wang. Research Progress on Synergistic Technologies of Agricultural Multi-Robots. Applied Sciences 2021, 11, 1448 .
AMA StyleWenju Mao, Zhijie Liu, Heng Liu, Fuzeng Yang, Meirong Wang. Research Progress on Synergistic Technologies of Agricultural Multi-Robots. Applied Sciences. 2021; 11 (4):1448.
Chicago/Turabian StyleWenju Mao; Zhijie Liu; Heng Liu; Fuzeng Yang; Meirong Wang. 2021. "Research Progress on Synergistic Technologies of Agricultural Multi-Robots." Applied Sciences 11, no. 4: 1448.