Monitoring maize canopy chlorophyll density under lodging stress based on UAV hyperspectral imagery

Sun, Qian; Gu, Xiaohe; Chen, Liping; Xu, Xiru; Zhang, Wei; Pan, Yuchun; Gao, Yunbing

doi:10.1016/j.compag.2021.106671

Cited by 37 publications

(18 citation statements)

References 29 publications

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“…Lodging has a huge impact on both yield and grain quality. Lodging caused a maize yield loss of approximately 0-50% at different lodging angles [3]. In general, the smaller the lodging angle, the smaller the yield loss.…”

Section: Data Acquisitionmentioning

confidence: 90%

“…It is stated as the displacement of the above-ground stems from their upright position or failure of root-soil attachment [1]. Lodging is generally caused by rainstorms, loose soil, high planting density and unreasonable fertilization [2][3][4]. Lodging hinders the growth of maize [5], reduces grain quality [6] and affects mechanized harvesting [7], which is becoming an important restricting issue to increase maize yield [8].…”

Section: Introductionmentioning

confidence: 99%

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Classification of Maize Lodging Extents Using Deep Learning Algorithms by UAV-Based RGB and Multispectral Images

et al. 2022

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Lodging depresses the grain yield and quality of maize crop. Previous machine learning methods are used to classify crop lodging extents through visual interpretation and sensitive features extraction manually, which are cost-intensive, subjective and inefficient. The analysis on the accuracy of subdivision categories is insufficient for multi-grade crop lodging. In this study, a classification method of maize lodging extents was proposed based on deep learning algorithms and unmanned aerial vehicle (UAV) RGB and multispectral images. The characteristic variation of three lodging extents in RGB and multispectral images were analyzed. The VGG-16, Inception-V3 and ResNet-50 algorithms were trained and compared depending on classification accuracy and Kappa coefficient. The results showed that the more severe the lodging, the higher the intensity value and spectral reflectance of RGB and multispectral image. The reflectance variation in red edge band were more evident than that in visible band with different lodging extents. The classification performance using multispectral images was better than that of RGB images in various lodging extents. The test accuracies of three deep learning algorithms in non-lodging based on RGB images were high, i.e., over 90%, but the classification performance between moderate lodging and severe lodging needed to be improved. The test accuracy of ResNet-50 was 96.32% with Kappa coefficients of 0.9551 by using multispectral images, which was superior to VGG-16 and Inception-V3, and the accuracies of ResNet-50 on each lodging subdivision category all reached 96%. The ResNet-50 algorithm of deep learning combined with multispectral images can realize accurate lodging classification to promote post-stress field management and production assessment.

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Section: Data Acquisitionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Classification of Maize Lodging Extents Using Deep Learning Algorithms by UAV-Based RGB and Multispectral Images

et al. 2022

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“…The reflectance of maize crop stalk is higher than that of leaf. The more serious the lodging, the higher the straw exposure [67], and the higher the reflectance (Fig. 6).…”

Section: B Spectral Features Analysismentioning

confidence: 99%

Monitoring Lodging Extents of Maize Crop Using Multitemporal GF-1 Images

Shi

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Maize crop lodging is a recurrent phenomenon which results in significant reduction of grain yield and quality in addition to the impediment of mechanical harvesting. The large-scale monitoring of maize crop lodging is important for production policy adjustment and agricultural insurance compensation. In this study, we derived a variety of features from multi-temporal GaoFen-1(GF-1) images before and after maize crop lodging. We screened the most sensitive features of the spectrum, texture, and vegetation index to monitor maize crop lodging. The recursive feature elimination method based on cross-validation (RFECV) and Mutual information (MI) were compared to obtain the optimal feature combination for monitoring the lodging extents of maize crop. The random forest (RF) classifier was used to classify the lodging extents. The results showed that the most sensitive features of the spectrum, texture, and vegetation indices of lodging extents included the difference of reflectance in blue, green and red bands, the difference of normalized difference vegetation index, the difference of ratio vegetation index, the difference of enhanced vegetation index difference, the difference of mean value of blue band, the difference of mean value of green band, and the difference of mean value of red band. The total accuracy of lodging extents classification was 87.50%, and the Kappa coefficient was 0.83 for testing samples. Based on multiple features derived from GF-1 images before and after lodging, the lodging extents of maize crop can be monitored on a large scale.

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“…The monitoring systems for leaves and also agricultural applications using aerial systems were mostly vision-based monitoring systems without interaction with the plant [26]- [28]. The problem associated with automatic plant disease identification using visible range images has received considerable attention in the last two decades [29].…”

Section: Introductionmentioning

confidence: 99%

A 79.7g Manipulator Prototype for E-Flap Robot: A Plucking-Leaf Application

et al. 2022

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The manipulation capabilities of flapping-wing flying robots (FWFRs) is a problem barely studied. This is a direct consequence of the load-carrying capacity limitation of the flapping-wing robots. Ornithopters will improve the existent multirotor unmanned aerial vehicles (UAVs) since they could perform longer missions and offer a safe interaction in proximity to humans. This technology also opens the possibility to perch in some trees and perform tasks such as obtaining samples from nature, enabling biologists to collect samples in remote places, or assisting people in rescue missions by carrying medicines or first-aid kits. This paper presents a very lightweight manipulator (79.7g) prototype to be mounted on an ornithopter. The distribution of the mass on the flapping-wing robot is sensitive and an extra lumped mass far from the center-of-mass (CoM) of the robot deteriorates the flight stability. A configuration was proposed to avoid changing the CoM. Flight experiments show that adding the arm to the robot only moved the CoM 6mm and the performance of the flight with the manipulator has been satisfactory. Plucking leaf is chosen as an application to the designed system and several experimental tests confirmed successful sampling of leaves by the prototype.

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Monitoring maize canopy chlorophyll density under lodging stress based on UAV hyperspectral imagery

Cited by 37 publications

References 29 publications

Classification of Maize Lodging Extents Using Deep Learning Algorithms by UAV-Based RGB and Multispectral Images

Classification of Maize Lodging Extents Using Deep Learning Algorithms by UAV-Based RGB and Multispectral Images

Monitoring Lodging Extents of Maize Crop Using Multitemporal GF-1 Images

A 79.7g Manipulator Prototype for E-Flap Robot: A Plucking-Leaf Application

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