Improved nitrogen retrievals with airborne-derived fluorescence and plant traits quantified from VNIR-SWIR hyperspectral imagery in the context of precision agriculture

Author(s):  
Carlos Camino ◽  
Victoria González-Dugo ◽  
Pilar Hernández ◽  
J.C. Sillero ◽  
Pablo J. Zarco‐Tejada
2020 ◽  
Vol 12 (12) ◽  
pp. 1930 ◽  
Author(s):  
Hengqian Zhao ◽  
Chenghai Yang ◽  
Wei Guo ◽  
Lifu Zhang ◽  
Dongyan Zhang

The timely monitoring of crop disease development is very important for precision agriculture applications. Remote sensing-based vegetation indices (VIs) can be good indicators of crop disease severity, but current methods are mainly dependent on manual ground survey results. Based on VI normalization, an automated crop disease severity grading method without the use of ground surveys was proposed in this study. This technique was applied to two cotton fields infested with different levels of cotton root rot in south Texas in the United States, where airborne hyperspectral imagery was collected. Six typical VIs were calculated from the hyperspectral imagery and their histograms indicated that VI normalization could eliminate the influences of variable field conditions and the VI value range variations, allowing a potentially broader scope of application. According to the analysis of the obtained results from the spectral dimension, spatial dimension and descriptive statistics, the disease grading results were in general agreement with previous ground survey results, proving the validity of the disease severity grading method. Although satisfactory results could be achieved from different types of VI, there is still room for further improvement through the exploration of more VIs. With the advantages of independence of ground surveys and potential universal applicability, the newly proposed crop disease grading method will be of great significance for crop disease monitoring over large geographical areas.


Author(s):  
Prabira Kumar Sethy ◽  
Chanki Pandey ◽  
Yogesh Kumar Sahu ◽  
Santi Kumari Behera

Author(s):  
Alexei Solovchenko ◽  
Boris Shurygin ◽  
Andrey Kuzin ◽  
Vitaly Velichko ◽  
Olga Solovchenko ◽  
...  

Hyperspectral reflectance imaging is an emerging method for rapid non-invasive quantitative screening of plant traits. This method is essential for high-throughput phenotyping and hence for accelerated breeding of crop plants as well as for precision agriculture practices. However, extraction of sensible information from reflectance images is hindered by the complexity of plant optical properties, especially when they are measured in the field. We propose using reflectance indices (Plant Senescence Reflectance Index, PSRI; Anthocyanin Reflectance Index, ARI; and spectral deconvolution) previously developed for remote sensing of vegetation and point-based reflectometers to infer the spatially resolved information on plant development and biochemical composition using ripening apple fruit as the model. Specifically, the proposed approach enables capturing data on distribution of chlorophylls and primary carotenoids as well as secondary carotenoids (both linked with fruit ripening and leaf senescence during plant development) as well as the information on spatial distribution of anthocyanins (known as stress pigments) over the plant surface. We argue that the proposed approach would enrich the phenotype assessments made on the base of reflectance image analysis with valuable information on plant physiological condition, stress acclimation state, and the progression of the plant development.


2021 ◽  
Author(s):  
Anna Brook ◽  
Yasmin Tal ◽  
Oshry Markovich ◽  
Nataliya Rybnikova

AbstractIrrigation and fertilization stress in plants are limitations for securing global food production. Sustainable agriculture is at the heart of global goals because threats of a rapidly growing population and climate changes are affecting agricultural productivity. Plant phenotyping is defined as evaluating plant traits. Traditionally, this measurement is performed manually but with advanced technology and analysis, these traits can be observed automatically and nondestructively. A high correlation between plant traits, growth, biomass, and final yield has been found. From the early stages of plant development, lack of irrigation and fertilization directly influence developing stages, thus the final crop yield is significantly reduced. In order to evaluate drought and fertilization stress, plant height, as a morphological trait, is the most common one used in precision-agriculture research. The present study shows that three-dimension volumetric approaches are more representative markers for alerting growers to the early stages of stress in young banana plants’ for fine-scale phenotyping. This research demonstrates two different group conditions: 1) Normal conditions; and 2) zero irrigation and zero fertilization. The statistical analysis results show a successfully distinguished early stress with the volumetric traits providing new insights on identifying the key phenotypes and growth stages influenced by drought stress.


Agronomy ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 258 ◽  
Author(s):  
Aakash Chawade ◽  
Joost van Ham ◽  
Hanna Blomquist ◽  
Oscar Bagge ◽  
Erik Alexandersson ◽  
...  

High-throughput field phenotyping has garnered major attention in recent years leading to the development of several new protocols for recording various plant traits of interest. Phenotyping of plants for breeding and for precision agriculture have different requirements due to different sizes of the plots and fields, differing purposes and the urgency of the action required after phenotyping. While in plant breeding phenotyping is done on several thousand small plots mainly to evaluate them for various traits, in plant cultivation, phenotyping is done in large fields to detect the occurrence of plant stresses and weeds at an early stage. The aim of this review is to highlight how various high-throughput phenotyping methods are used for plant breeding and farming and the key differences in the applications of such methods. Thus, various techniques for plant phenotyping are presented together with applications of these techniques for breeding and cultivation. Several examples from the literature using these techniques are summarized and the key technical aspects are highlighted.


2020 ◽  
pp. 637-656 ◽  
Author(s):  
Marco Medici ◽  
Søren Marcus Pedersen ◽  
Giacomo Carli ◽  
Maria Rita Tagliaventi

The purpose of this study is to analyse the environmental benefits of precision agriculture technology adoption obtained from the mitigation of negative environmental impacts of agricultural inputs in modern farming. Our literature review of the environmental benefits related to the adoption of precision agriculture solutions is aimed at raising farmers' and other stakeholders' awareness of the actual environmental impacts from this set of new technologies. Existing studies were categorised according to the environmental impacts of different agricultural activities: nitrogen application, lime application, pesticide application, manure application and herbicide application. Our findings highlighted the effects of the reduction of input application rates and the consequent impacts on climate, soil, water and biodiversity. Policy makers can benefit from the outcomes of this study developing an understanding of the environmental impact of precision agriculture in order to promote and support initiatives aimed at fostering sustainable agriculture.


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