Shadow effect on multi-spectral image for detection of nitrogen deficiency in corn

Noh, Hyun Ho; Zhang, Q.

doi:10.1016/j.compag.2012.01.014

Cited by 17 publications

(12 citation statements)

References 7 publications

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“…Major challenges with the use of sensors include: (a) Problems resulting from mixed pixels when a single pixel comprises both plant material and background soil (Jones and Sirault, submitted to this special issue). (b) Difficulties caused by variation in the solar illumination angle and the bi-directional reflectance distribution function (BRDF) (for example, the resulting variation in the amount of shadowing in a pixel and its dependence on canopy structure) [103]. (c) Although the simplest application of in-field remote sensing, especially of spectral reflectance, is to use simple vegetation indices (VI) as indicators of variables of interest (e.g., N or water content, chlorophyll, LAI or photosynthesis), the values of the quantities being estimated can be very subject to environmental conditions and to canopy structure; this leads to substantial imprecision in the estimates of variables of interest and the need for site-specific calibration [82].…”

Section: Some Technical Challenges In the Use Of Proximal Sensors Moumentioning

confidence: 99%

Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping

et al. 2014

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The achievements made in genomic technology in recent decades are yet to be matched by fast and accurate crop phenotyping methods. Such crop phenotyping methods are required for crop improvement efforts to meet expected demand for food and fibre in the future. This review evaluates the role of proximal remote sensing buggies for field-based phenotyping with a particular focus on the application of currently available sensor technology for large-scale field phenotyping. To illustrate the potential for the development of high throughput phenotyping techniques, a case study is presented with sample data sets obtained from a ground-based proximal remote sensing buggy mounted with the following sensors: LiDAR, RGB camera, thermal infra-red camera and imaging spectroradiometer. The development of such techniques for routine deployment in commercial-scale breeding and pre-breeding operations will require a multidisciplinary approach to leverage the recent technological advances realised in computer science, image analysis, proximal remote sensing and robotics.

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Section: Some Technical Challenges In the Use Of Proximal Sensors Moumentioning

confidence: 99%

Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping

et al. 2014

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“…There are other approaches that capture images at different wavelengths. In particular, multispectral and hyperspectral images with 300 to 1200 nm have been used . Other authors have used images with visible spectrum (300‐700 nm) information in order to determine a correlation value between chlorophyll/nitrogen content.…”

Section: Related Workmentioning

confidence: 99%

“…So, in order to obtain more efficient crops, most of the current food producers supervise their crops health because it is well‐known that stress conditions affect photosynthetic activity. In recent years, one popular approach consists in estimate the chlorophyll content in the plants leaves within a crop . This is because there is a high correlation between the chlorophyll content and the plants health then, in current literature, several approaches for chlorophyll estimation can be found .…”

Section: Introductionmentioning

confidence: 99%

“…In this context, image processing algorithms are applied with the assumption that chlorophyll content defines the leaf color, ie, low chlorophyll contents generate yellowish‐green colors while high chlorophyll content involves dark green colors. In previous works, several vision‐based approaches have been proposed, and several image processing algorithms have been developed; unfortunately, it was demonstrated that they achieve relatively low accuracy (compared with the SPAD device).…”

Section: Introductionmentioning

confidence: 99%

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An FPGA‐based smart camera for accurate chlorophyll estimations

Pérez-Patricio

Aguilar-González

Camas-Anzueto

et al. 2018

Circuit Theory & Apps

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In this work, a new chlorophyll estimation approach based on the reflectance/ transmittance from the leaf being analyzed is proposed. First, top/underside images from the leaf under analysis are captured, then, the base parameters (reflectance/transmittance) are extracted. Finally, a double-variable linear regression model estimates the chlorophyll content. To estimate the base parameters, a novel optical arrangement is presented. On the other hand, in order to provide a portable device suitable for chlorophyll estimation under large scale food crops, we have implemented our optical arrangement and our algorithmic formulation inside an field-programmable gate array (FPGA)-based smart camera fabric. Experimental results demonstrated that the proposed approach outperforms (in terms of accuracy and processing speed) most previous vision-based approaches, reaching more than 97% accuracy and delivering fast chlorophyll estimations (near 5 ms per estimation).

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“…In theory, the specific property of a target object on each pixel can be predicted by using a proper remote sensing inversion model based on their spectral signatures. With the development of hyperspectral imaging technology, imaging spectrometers are widely used in agriculture-related processes, such as estimating chlorophyll [13] , water [14,15] , and nutrient [16,17] contents of crop leaves, monitoring damages caused by pests and diseases in farmland [18] , and examining disease spots on crop fruits and seeds [19,20] . Huang et al [21] quantified the disease index of yellow rust in wheat with photochemical reflectance index derived from aerial hyperspectral images.…”

Section: Introductionmentioning

confidence: 99%

Estimating the severity of apple mosaic disease with hyperspectral images

Ban

Tian

Chang

2019

International Journal of Agricultural and Biological Engineering

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Soil Plant Analysis Development (SPAD) Chlorophyll Meter reading was used to effectively characterize chlorophyll content, which is an important indicator of the health status of plant leaves. In this study, the hyperspectral images of apple leaves infected by apple mosaic virus (ApMV) were captured, and their SPAD values were measured. The spectral reflectance of leaves with varying degree infection of disease is significantly different. In particular, the reflectance in visible wavebands of leaves with a more serious infection was higher than that of leaves with a less severe infection. Several hyperspectral vegetation indices were highly correlated with the SPAD values of apple leaves (correlation coefficient > 0.9). Models were established to estimate apple foliar SPAD values based on these vegetation indices. Among the models, the multivariate regression model with partial least square regression (PLSR) method achieved the highest accuracy. The SPAD value of a whole apple leaf was calculated from its SPAD distribution image and used as a quantitative index to represent the health status of an apple leaf. Furthermore, the SPAD value of a whole apple leaf could also be estimated rapidly and accurately by extracting the spectral average value of the whole leaf using a simple model. It can be used as a rapid detection method of SPAD values of apple leaves to monitor and describe the health conditions of apple leaves quantitatively.

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Shadow effect on multi-spectral image for detection of nitrogen deficiency in corn

Cited by 17 publications

References 7 publications

Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping

Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping

An FPGA‐based smart camera for accurate chlorophyll estimations

Estimating the severity of apple mosaic disease with hyperspectral images

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