scholarly journals A Quantitative Monitoring Method for Determining Maize Lodging in Different Growth Stages

2020 ◽  
Vol 12 (19) ◽  
pp. 3149
Author(s):  
HaiXiang Guan ◽  
HuanJun Liu ◽  
XiangTian Meng ◽  
Chong Luo ◽  
YiLin Bao ◽  
...  
Keyword(s):  
Screening Method ◽  
Canopy Structure ◽  
Window Size ◽  
Growth Stages ◽  
Homogeneous Field ◽  
Gray Level ◽  
Monitoring Method ◽  
Feature Sets ◽  
Optimal Window ◽  
Different Growth Stages

Many studies have achieved efficient and accurate methods for identifying crop lodging under homogeneous field surroundings. However, under complex field conditions, such as diverse fertilization methods, different crop growth stages, and various sowing periods, the accuracy of lodging identification must be improved. Therefore, a maize plot featuring different growth stages was selected in this study to explore an applicable and accurate lodging extraction method. Based on the Akaike information criterion (AIC), we propose an effective and rapid feature screening method (AIC method) and compare its performance using indexed methods (i.e., variation coefficient and relative difference). Seven feature sets extracted from unmanned aerial vehicle (UAV) images of lodging and nonlodging maize were established using a canopy height model (CHM) and the multispectral imagery acquired from the UAV. In addition to accuracy parameters (i.e., Kappa coefficient and overall accuracy), the difference index (DI) was applied to search for the optimal window size of texture features. After screening all feature sets by applying the AIC method, binary logistic regression classification (BLRC), maximum likelihood classification (MLC), and random forest classification (RFC) were utilized to discriminate among lodging and nonlodging maize based on the selected features. The results revealed that the optimal window sizes of the gray-level cooccurrence matrix (GLCM) and the gray-level difference histogram statistical (GLDM) texture information were 17 × 17 and 21 × 21, respectively. The AIC method incorporating GLCM texture yielded satisfactory results, obtaining an average accuracy of 82.84% and an average Kappa value of 0.66 and outperforming the index screening method (59.64%, 0.19). Furthermore, the canopy structure feature (CSF) was more beneficial than other features for identifying maize lodging areas at the plot scale. Based on the AIC method, we achieved a positive maize lodging recognition result using the CSFs and BLRC. This study provides a highly robust and novel method for monitoring maize lodging in complicated plot environments.

scholarly journals Seasonal Variation of the Canopy Structure Parameters and Its Correlation with Yield-Related Traits in Sugarcane

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Jun Luo ◽  
Youxiong Que ◽  
Hua Zhang ◽  
Liping Xu
Keyword(s):  
Transmission Coefficient ◽  
Canopy Structure ◽  
Diffuse Radiation ◽  
Growth Stages ◽  
Beam Radiation ◽  
Area Index ◽  
Sugarcane Varieties ◽  
Sugarcane Yield ◽  
Leaf Distribution ◽  
Different Growth Stages

Population structure determines sugarcane yield, of which canopy structure is a key component. To fully understand the relations between sugarcane yield and parameters of the canopy structure, 17 sugarcane varieties were investigated at five growth stages. The results indicated that there were significant differences between characterized parameters among sugarcane populations at different growth stages. During sugarcane growth after planting, leaf area index (LAI) and leaf distribution (LD) increased, while transmission coefficient for diffuse radiation (TD), mean foliage inclination angle (MFIA), transmission coefficient for solar beam radiation penetration (TR), and extinction coefficient (K) decreased. Significant negative correlations were found between sugarcane yield and MFIA, TD, TR, andKat the early elongation stage, while a significant positive correlation between sugarcane yield and LD was found at the same stage. A regression for sugarcane yield, with relative error of yield fitting less than 10%, was successfully established: sugarcane yield = 2380.12 + 46.25 × LD − 491.82 × LAI + 1.36 × MFIA + 614.91 × TD − 1908.05 × TR − 182.53 ×  K+ 1281.75 × LD − 1.35 × MFIA + 831.2 × TR − 407.8 ×  K+ 8.21 × MFIA − 834.50 × TD − 1695.49 ×  K  (R2=0.94**).

Cytokinins from terminal buds of Euphoria longana during different growth stages

1997 ◽  
Vol 99 (1) ◽  
pp. 185-189
Author(s):  
Wen-Shaw Chen ◽  
Kuang-Liang Huang ◽  
Hsiao-Ching Yu
Keyword(s):  
Growth Stages ◽  
Terminal Buds ◽  
Different Growth Stages

Effects of Meteorological Factors at Different Growth Stages on Yield Traits of Maize (Zea maysL.) in Heilonggang Basin

2013 ◽  
Vol 39 (5) ◽  
pp. 919 ◽  
Author(s):  
Bo MING ◽  
Jin-Cheng ZHU ◽  
Hong-Bin TAO ◽  
Li-Na XU ◽  
Bu-Qing GUO ◽  
...  
Keyword(s):  
Meteorological Factors ◽  
Growth Stages ◽  
Yield Traits ◽  
Different Growth Stages

scholarly journals Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots

GigaScience ◽  
2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Teng Miao ◽  
Weiliang Wen ◽  
Yinglun Li ◽  
Sheng Wu ◽  
Chao Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Training Dataset ◽  
Growth Stages ◽  
3D Point Cloud ◽  
Plant Structure ◽  
Data Annotation ◽  
Cloud Data ◽  
Point Cloud Segmentation ◽  
Different Growth Stages

Abstract Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4–10 minutes to segment a maize shoot and consumes 10–20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.

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