Zhikai Cheng, Xiaobo Gu, Chunyu Wei, Zhihui Zhou, Tongtong Zhao, Yuming Wang, Wenlong Li, Yadan Du, Huanjie Cai
{"title":"监测小麦和玉米的地上部分生物量:结合集合学习和异速理论的新型模型","authors":"Zhikai Cheng, Xiaobo Gu, Chunyu Wei, Zhihui Zhou, Tongtong Zhao, Yuming Wang, Wenlong Li, Yadan Du, Huanjie Cai","doi":"10.1016/j.eja.2024.127338","DOIUrl":null,"url":null,"abstract":"<div><p>Accurate monitoring of crop organ biomass facilitates optimizing agronomic strategies to maximize yield or economic benefit. Unmanned aerial vehicle (UAV) is extensively employed for aboveground biomass (AGB) monitoring at the farm scale, but previous studies have mostly concentrated on total AGB rather than individual organ biomass. Furthermore, film-mulched crops, a widely used cropping pattern in northwest China, have received less attention for AGB monitoring. We aim to develop a novel model to precisely estimate the AGB of leaf (AGB<sub>Leaf</sub>), stem (AGB<sub>Stem</sub>), and reproductive organs (AGB<sub>R</sub>) by UAV for film-mulched wheat and maize. The maize-wheat rotation field experiments with treatments of five nitrogen application amounts and three planting densities were conducted from 2021 to 2023, respectively. Firstly, we constructed allometric models at jointing, heading (tasseling), and grain filling stages by ground sampling data in 2021–2022. Next, the input feature set was obtained by feature selection methods (Lasso and Boruta) using UAV image data, and three traditional methods (partial least squares, ridge regression, and support vector machine) and three ensemble learning models (random forest, extreme gradient boosting, and local cascade ensemble (LCE)) were trained for AGB<sub>Leaf</sub> inversion based on the physically-based PROSAIL model simulation dataset. Finally, the optimal AGB<sub>Leaf</sub> inversion hybrid model was coupled with the allometric model to estimate the AGB<sub>Stem</sub> and AGB<sub>R</sub> in 2022–2023. The results indicated that both wheat and maize organ biomass conformed to the allometric pattern. While feature selection helped reduce computation and complexity, but didn’t improve monitoring accuracy. The normalized root mean square error (NRMSE) of the optimal hybrid model (PROSAIL + Boruta + LCE) on the measured wheat and maize AGB<sub>Leaf</sub> datasets were 12.72 %–24.93 % and 19.65 %–25.16 %, respectively. After coupling the allometric model, the coefficient of determination (R<sup>2</sup>) of wheat and maize AGB<sub>Stem</sub> were 0.64–0.85 and 0.63–0.68, and the NRMSE were 15.05 %–25.28 % and 24.10 %–27.06 %, respectively; and the corresponding R<sup>2</sup> of AGB<sub>R</sub> was 0.67–0.76 and 0.72, and the NRMSE were 16.81 %–22.12 % and 21.66 %, for wheat and maize, respectively. Overall, the novel model performed well in film-mulched wheat and maize, providing a cost-effective approach for organ biomass monitoring. In the future, further validation of the model’s transferability is necessary to increase the potential for generalization in production practice.</p></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"161 ","pages":"Article 127338"},"PeriodicalIF":4.5000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Monitoring aboveground organs biomass of wheat and maize: A novel model combining ensemble learning and allometric theory\",\"authors\":\"Zhikai Cheng, Xiaobo Gu, Chunyu Wei, Zhihui Zhou, Tongtong Zhao, Yuming Wang, Wenlong Li, Yadan Du, Huanjie Cai\",\"doi\":\"10.1016/j.eja.2024.127338\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Accurate monitoring of crop organ biomass facilitates optimizing agronomic strategies to maximize yield or economic benefit. Unmanned aerial vehicle (UAV) is extensively employed for aboveground biomass (AGB) monitoring at the farm scale, but previous studies have mostly concentrated on total AGB rather than individual organ biomass. Furthermore, film-mulched crops, a widely used cropping pattern in northwest China, have received less attention for AGB monitoring. We aim to develop a novel model to precisely estimate the AGB of leaf (AGB<sub>Leaf</sub>), stem (AGB<sub>Stem</sub>), and reproductive organs (AGB<sub>R</sub>) by UAV for film-mulched wheat and maize. The maize-wheat rotation field experiments with treatments of five nitrogen application amounts and three planting densities were conducted from 2021 to 2023, respectively. Firstly, we constructed allometric models at jointing, heading (tasseling), and grain filling stages by ground sampling data in 2021–2022. Next, the input feature set was obtained by feature selection methods (Lasso and Boruta) using UAV image data, and three traditional methods (partial least squares, ridge regression, and support vector machine) and three ensemble learning models (random forest, extreme gradient boosting, and local cascade ensemble (LCE)) were trained for AGB<sub>Leaf</sub> inversion based on the physically-based PROSAIL model simulation dataset. Finally, the optimal AGB<sub>Leaf</sub> inversion hybrid model was coupled with the allometric model to estimate the AGB<sub>Stem</sub> and AGB<sub>R</sub> in 2022–2023. The results indicated that both wheat and maize organ biomass conformed to the allometric pattern. While feature selection helped reduce computation and complexity, but didn’t improve monitoring accuracy. The normalized root mean square error (NRMSE) of the optimal hybrid model (PROSAIL + Boruta + LCE) on the measured wheat and maize AGB<sub>Leaf</sub> datasets were 12.72 %–24.93 % and 19.65 %–25.16 %, respectively. After coupling the allometric model, the coefficient of determination (R<sup>2</sup>) of wheat and maize AGB<sub>Stem</sub> were 0.64–0.85 and 0.63–0.68, and the NRMSE were 15.05 %–25.28 % and 24.10 %–27.06 %, respectively; and the corresponding R<sup>2</sup> of AGB<sub>R</sub> was 0.67–0.76 and 0.72, and the NRMSE were 16.81 %–22.12 % and 21.66 %, for wheat and maize, respectively. Overall, the novel model performed well in film-mulched wheat and maize, providing a cost-effective approach for organ biomass monitoring. In the future, further validation of the model’s transferability is necessary to increase the potential for generalization in production practice.</p></div>\",\"PeriodicalId\":51045,\"journal\":{\"name\":\"European Journal of Agronomy\",\"volume\":\"161 \",\"pages\":\"Article 127338\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Agronomy\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1161030124002594\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRONOMY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Agronomy","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1161030124002594","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
Monitoring aboveground organs biomass of wheat and maize: A novel model combining ensemble learning and allometric theory
Accurate monitoring of crop organ biomass facilitates optimizing agronomic strategies to maximize yield or economic benefit. Unmanned aerial vehicle (UAV) is extensively employed for aboveground biomass (AGB) monitoring at the farm scale, but previous studies have mostly concentrated on total AGB rather than individual organ biomass. Furthermore, film-mulched crops, a widely used cropping pattern in northwest China, have received less attention for AGB monitoring. We aim to develop a novel model to precisely estimate the AGB of leaf (AGBLeaf), stem (AGBStem), and reproductive organs (AGBR) by UAV for film-mulched wheat and maize. The maize-wheat rotation field experiments with treatments of five nitrogen application amounts and three planting densities were conducted from 2021 to 2023, respectively. Firstly, we constructed allometric models at jointing, heading (tasseling), and grain filling stages by ground sampling data in 2021–2022. Next, the input feature set was obtained by feature selection methods (Lasso and Boruta) using UAV image data, and three traditional methods (partial least squares, ridge regression, and support vector machine) and three ensemble learning models (random forest, extreme gradient boosting, and local cascade ensemble (LCE)) were trained for AGBLeaf inversion based on the physically-based PROSAIL model simulation dataset. Finally, the optimal AGBLeaf inversion hybrid model was coupled with the allometric model to estimate the AGBStem and AGBR in 2022–2023. The results indicated that both wheat and maize organ biomass conformed to the allometric pattern. While feature selection helped reduce computation and complexity, but didn’t improve monitoring accuracy. The normalized root mean square error (NRMSE) of the optimal hybrid model (PROSAIL + Boruta + LCE) on the measured wheat and maize AGBLeaf datasets were 12.72 %–24.93 % and 19.65 %–25.16 %, respectively. After coupling the allometric model, the coefficient of determination (R2) of wheat and maize AGBStem were 0.64–0.85 and 0.63–0.68, and the NRMSE were 15.05 %–25.28 % and 24.10 %–27.06 %, respectively; and the corresponding R2 of AGBR was 0.67–0.76 and 0.72, and the NRMSE were 16.81 %–22.12 % and 21.66 %, for wheat and maize, respectively. Overall, the novel model performed well in film-mulched wheat and maize, providing a cost-effective approach for organ biomass monitoring. In the future, further validation of the model’s transferability is necessary to increase the potential for generalization in production practice.
期刊介绍:
The European Journal of Agronomy, the official journal of the European Society for Agronomy, publishes original research papers reporting experimental and theoretical contributions to field-based agronomy and crop science. The journal will consider research at the field level for agricultural, horticultural and tree crops, that uses comprehensive and explanatory approaches. The EJA covers the following topics:
crop physiology
crop production and management including irrigation, fertilization and soil management
agroclimatology and modelling
plant-soil relationships
crop quality and post-harvest physiology
farming and cropping systems
agroecosystems and the environment
crop-weed interactions and management
organic farming
horticultural crops
papers from the European Society for Agronomy bi-annual meetings
In determining the suitability of submitted articles for publication, particular scrutiny is placed on the degree of novelty and significance of the research and the extent to which it adds to existing knowledge in agronomy.