文献类型： 外文期刊

作者： Hu, Jingyu ¹ ; Feng, Hao ¹ ; Wang, Qilei ² ; Shen, Jianing ¹ ; Wang, Jian ¹ ; Liu, Yang ³ ; Feng, Haikuan ⁴ ; Yang, Hao ⁴ ; Guo, Wei ¹ ; Qiao, Hongbo ¹ ; Niu, Qinglin ⁵ ; Yue, Jibo ¹ ;

作者机构： 1.Henan Agr Univ, Coll Informat & Management Sci, Zhengzhou 450002, Peoples R China

2.Henan Jinyuan Seed Ind Co Ltd, Zhengzhou 450003, Peoples R China

3.China Agr Univ, Minist Educ, Key Lab Smart Agr Syst, Beijing 100083, Peoples R China

4.Beijing Res Ctr Informat Technol Agr, Minist Agr, Key Lab Quantitat Remote Sensing Agr, Beijing 100097, Peoples R China

5.Chinese Acad Agr Sci, Farmland Irrigat Res Inst FIRI, Xinxiang 453002, Peoples R China

6.Henan Polytech Univ, Inst Quantitat Remote Sensing & Smart Agr, Jiaozuo 454000, Peoples R China

关键词： unmanned aerial vehicle; crop leaf chlorophyll content; fractional vegetation cover; maturity; deep learning; ensemble learning; maize

期刊名称：REMOTE SENSING （ 影响因子：5.0； 五年影响因子：5.6 ）

ISSN：

年卷期： 2024 年 16 卷 5 期

页码：

收录情况： SCI

摘要： Crop leaf chlorophyll content (LCC) and fractional vegetation cover (FVC) are crucial indicators for assessing crop health, growth development, and maturity. In contrast to the traditional manual collection of crop trait parameters, unmanned aerial vehicle (UAV) technology rapidly generates LCC and FVC maps for breeding materials, facilitating prompt assessments of maturity information. This study addresses the following research questions: (1) Can image features based on pretrained deep learning networks and ensemble learning enhance the estimation of remote sensing LCC and FVC? (2) Can the proposed adaptive normal maturity detection (ANMD) algorithm effectively monitor maize maturity based on LCC and FVC maps? We conducted the following tasks: (1) Seven phases (tassel initiation to maturity) of maize canopy orthoimages and corresponding ground-truth data for LCC and six phases of FVC using UAVs were collected. (2) Three features, namely vegetation indices (VI), texture features (TF) based on Gray Level Co-occurrence Matrix, and deep features (DF), were evaluated for LCC and FVC estimation. Moreover, the potential of four single-machine learning models and three ensemble models for LCC and FVC estimation was evaluated. (3) The estimated LCC and FVC were combined with the proposed ANMD to monitor maize maturity. The research findings indicate that (1) image features extracted from pretrained deep learning networks more accurately describe crop canopy structure information, effectively eliminating saturation effects and enhancing LCC and FVC estimation accuracy. (2) Ensemble models outperform single-machine learning models in estimating LCC and FVC, providing greater precision. Remarkably, the stacking + DF strategy achieved optimal performance in estimating LCC (coefficient of determination (R2): 0.930; root mean square error (RMSE): 3.974; average absolute error (MAE): 3.096); and FVC (R2: 0.716; RMSE: 0.057; and MAE: 0.044). (3) The proposed ANMD algorithm combined with LCC and FVC maps can be used to effectively monitor maize maturity. Establishing the maturity threshold for LCC based on the wax ripening period (P5) and successfully applying it to the wax ripening-mature period (P5-P7) achieved high monitoring accuracy (overall accuracy (OA): 0.9625-0.9875; user's accuracy: 0.9583-0.9933; and producer's accuracy: 0.9634-1). Similarly, utilizing the ANMD algorithm with FVC also attained elevated monitoring accuracy during P5-P7 (OA: 0.9125-0.9750; UA: 0.878-0.9778; and PA: 0.9362-0.9934). This study offers robust insights for future agricultural production and breeding, offering valuable insights for the further exploration of crop monitoring technologies and methodologies.