UAV-based hyperspectral analysis and spectral indices constructing for quantitatively monitoring leaf nitrogen content of winter wheat

Zhu, Hao; Liu, Haiying; Xu, Yuexue; Yang, Guijun

doi:10.1364/ao.57.007722

Cited by 26 publications

(18 citation statements)

References 34 publications

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“…With these, crop monitoring and diagnosis are improved and targeted, once again facilitating the optimum fertilizer application for desirable production outcomes (Zheng et al 2018). Examples of these techniques include hyperspectral imaging, successfully utilized in discriminating N nutritional levels in tea plants (Wang et al 2018b), as well as N, P, K, S, Cu, Zn, Fe and Mn levels in maize and soybean plants (Pandey et al 2017); unmanned aerial vehicle based multispectral imagery applied in the estimation of plant nitrogen concentration and management of N fertilizer application in rice (Zheng et al 2018) and in wheat (Zhu et al 2018a); and reflectance spectroscopy through which authors were able to characterize Fe deficiency symptoms in grapevine and prospect the possibility of detecting in field Fe deficiency conditions (Rustioni et al 2017).…”

Section: Strategies To Preserve the Nutritional Content In Future CLImentioning

confidence: 99%

Preserving the nutritional quality of crop plants under a changing climate: importance and strategies

et al. 2019

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Background Global climate is changing more rapidly than ever, threatening plant growth and productivity while exerting considerable direct and indirect effects on the quality and quantity of plant nutrients. Scope This review focuses on the global impact of climate change on the nutritional value of plant foods. It showcases the existing evidence linking the effects of climate change factors on crop nutrition and the concentration of nutrients in edible plant parts. It focuses on the effect of elevated CO 2 (eCO 2), elevated temperature (eT), salinity, waterlogging and drought stresses, and what is known regarding their direct and indirect influence on nutrient availability. Furthermore, it provides possible strategies to preserve the nutritional composition of plant foods under changing climates. Conclusions Climate change has an impact on the accumulation of minerals and protein in crop plants, with eCO 2 being the underlying factor of most of the reported changes. The effects are clearly dependent on the type, intensity and duration of the imposed stress, plant genotype and developmental stage. Strong interactions (both positive and negative) can be found between individual climatic factors and soil availability of nitrogen (N), potassium (K), iron (Fe) and phosphorous (P). The development of future interventions to ensure that the world's population has access to plentiful, safe and nutritious food may need to rely on breeding for nutrients under the context of climate change, including legumes in cropping systems, better farm management practices and utilization of microbial inoculants that enhance nutrient availability.

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Section: Strategies To Preserve the Nutritional Content In Future CLImentioning

confidence: 99%

Preserving the nutritional quality of crop plants under a changing climate: importance and strategies

et al. 2019

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“…The vast majority of the studies found in the literature extracts vegetation indices (VI) from the images and relates them with nutrient content using a regression model (usually linear). Although less common, other types of variables have also been used to feed the regression models, such as the average reflectance spectra [103], selected spectral bands [114,119,127], color features [118,123], and principal components [122]. All of these are calculated from hyperspectral images, except the color features, which are calculated from RGB images.…”

Section: Nutrition Disordersmentioning

confidence: 99%

“…Finally, Benincasa et al [102] did not observe significant differences in the accuracy between wheat crops at early and late seasons; however, they remarked that atypically intense rainfalls and preexisting soil conditions may have affected the results. Some studies have brought evidence that exploring the high spectral resolution of hyperspectral images to select the wavelengths that are more representative of each growing stage may be an effective way to address this issue and reduce inconsistencies [127].…”

Section: Nutrition Disordersmentioning

confidence: 99%

“…Such an angular movement alters the overlapping between images and deflect sensors from nadir, thus damaging the mosaicking process and introducing a variety of distortions. Correcting those distortions is not a trivial task, but it is unavoidable if they are very prominent or if the method to be applied relies heavily on the geometrical characterization of the image [19,55,59,64,67,81,107,114,127,142,153,159,167]. Another problem associated to high wind speeds is that canopies will move and change appearance, which may cause inconsistencies and may impede proper image alignment due to the impossibility of finding enough common key points between the photographs.…”

Section: Revisited Issuesmentioning

confidence: 99%

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A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses

Barbedo

2019

Drones

218

117

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Unmanned aerial vehicles (UAVs) are becoming a valuable tool to collect data in a variety of contexts. Their use in agriculture is particularly suitable, as those areas are often vast, making ground scouting difficult, and sparsely populated, which means that injury and privacy risks are not as important as in urban settings. Indeed, the use of UAVs for monitoring and assessing crops, orchards, and forests has been growing steadily during the last decade, especially for the management of stresses such as water, diseases, nutrition deficiencies, and pests. This article presents a critical overview of the main advancements on the subject, focusing on the strategies that have been used to extract the information contained in the images captured during the flights. Based on the information found in more than 100 published articles and on our own research, a discussion is provided regarding the challenges that have already been overcome and the main research gaps that still remain, together with some suggestions for future research.

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“…However, due to spatial resolution, spectral resolution, and temporal resolution, the remote data limit the value of agricultural applications and cannot meet the real-time requirements for crop growth monitoring [10,11,12]. In particular, light detection and ranging (LIDAR), hyperspectral, and multispectral sensors on unmanned aerial vehicles (UAVs) are not easily applied in practice due to their high price and complicated data processing requirements [13,14,15,16,17]. It could be seen that the fast, non-destructive, and high spatial resolution characteristics of UAVs have led agriculture to move toward quantitative refinement [18,19,20,21].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Aboveground Nitrogen Content of Winter Wheat Using Digital Imagery of Unmanned Aerial Vehicles

Yang

Wang

Sha

et al. 2019

Sensors

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Nitrogen (N) content is an important basis for the precise management of wheat fields. The application of unmanned aerial vehicles (UAVs) in agriculture provides an easier and faster way to monitor nitrogen content. Previous studies have shown that the features acquired from UAVs yield favorable results in monitoring wheat growth. However, since most of them are based on different vegetation indices, it is difficult to meet the requirements of accurate image interpretation. Moreover, resampling also easily ignores the structural features of the image information itself. Therefore, a spectral-spatial feature is proposed combining vegetation indices (VIs) and wavelet features (WFs), especially the acquisition of wavelet features from the UAV image, which was transformed from the spatial domain to the frequency domain with a wavelet transformation. In this way, the complete spatial information of different scales can be obtained to realize good frequency localization, scale transformation, and directional change. The different models based on different features were compared, including partial least squares regression (PLSR), support vector regression (SVR), and particle swarm optimization-SVR (PSO-SVR). The results showed that the accuracy of the model based on the spectral-spatial feature by combining VIs and WFs was higher than that of VIs or WF indices alone. The performance of PSO-SVR was the best (R2 = 0.9025, root mean square error (RMSE) = 0.3287) among the three regression algorithms regardless of the use of all the original features or the combination features. Our results implied that our proposed method could improve the estimation accuracy of aboveground nitrogen content of winter wheat from UAVs with consumer digital cameras, which have greater application potential in predicting other growth parameters.

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UAV-based hyperspectral analysis and spectral indices constructing for quantitatively monitoring leaf nitrogen content of winter wheat

Cited by 26 publications

References 34 publications

Preserving the nutritional quality of crop plants under a changing climate: importance and strategies

Preserving the nutritional quality of crop plants under a changing climate: importance and strategies

A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses

Evaluation of Aboveground Nitrogen Content of Winter Wheat Using Digital Imagery of Unmanned Aerial Vehicles

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