scholarly journals Enrichment of the Information Extracted From Hyperspectral Reflectance Images for Noninvasive Phenotyping

2021 ◽  
Author(s):  
Alexei Solovchenko ◽  
Boris Shurygin ◽  
Andrey Kuzin ◽  
Vitaly Velichko ◽  
Olga Solovchenko ◽  
...  
Keyword(s):  
Plant Development ◽  
Precision Agriculture ◽  
Plant Traits ◽  
Apple Fruit ◽  
Hyperspectral Reflectance ◽  
Spatially Resolved ◽  
Stress Acclimation ◽  
Secondary Carotenoids ◽  
As Stress ◽  
Reflectance Indices

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.

scholarly journals Linking Tissue Damage to Hyperspectral Reflectance for Non-Invasive Monitoring of Apple Fruit in Orchards

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 310
Author(s):  
Alexei Solovchenko ◽  
Alexei Dorokhov ◽  
Boris Shurygin ◽  
Alexandr Nikolenko ◽  
Vitaly Velichko ◽  
...  
Keyword(s):  
Apple Fruit ◽  
Individual Plant ◽  
Hyperspectral Reflectance ◽  
Non Invasive ◽  
Spatially Resolved ◽  
Reflected Light ◽  
Tissue Damages ◽  
Apple Cultivars ◽  
Controlled Illumination ◽  
Point Type

Reflected light carries ample information about the biochemical composition, tissue architecture, and physiological condition of plants. Recent technical progress has paved the way for affordable imaging hyperspectrometers (IH) providing spatially resolved spectral information on plants on different levels, from individual plant organs to communities. The extraction of sensible information from hyperspectral images is difficult due to inherent complexity of plant tissue and canopy optics, especially when recorded under ambient sunlight. We report on the changes in hyperspectral reflectance accompanying the accumulation of anthocyanins in healthy apple (cultivars Ligol, Gala, Golden Delicious) fruits as well as in fruits affected by pigment breakdown during sunscald development and phytopathogen attacks. The measurements made outdoors with a snapshot IH were compared with traditional “point-type” reflectance measured with a spectrophotometer under controlled illumination conditions. The spectra captured by the IH were suitable for processing using the approaches previously developed for “point-type” apple fruit and leaf reflectance spectra. The validity of this approach was tested by constructing a novel index mBRI (modified browning reflectance index) for detection of tissue damages on the background of the anthocyanin absorption. The index was suggested in the form of mBRI = (R640−1 + R800−1) − R678−1. Difficulties of the interpretation of fruit hyperspectral reflectance images recorded in situ are discussed with possible implications for plant physiology and precision horticulture practices.

scholarly journals Linking Tissue Damage to Hyperspectral Reflectance for Non-Invasive Monitoring of Apple Fruit in Orchards

2020 ◽  
Author(s):  
Alexei Solovchenko ◽  
Alexei Dorokhov ◽  
Boris Shurygin ◽  
Alexandr Nikolenko ◽  
Vitaly Velichko ◽  
...  
Keyword(s):  
Apple Fruit ◽  
Hyperspectral Reflectance ◽  
Non Invasive ◽  
Spatially Resolved ◽  
Reflected Light ◽  
Grading Systems ◽  
Tissue Damages ◽  
Controlled Illumination ◽  
Insight Into

Reflected light carries ample information about biochemical composition, tissue architecture, and physiological condition of plants. Recent technical progress brought about affordable imaging hyperspectrometers (IH) providing spatially resolved spectral data on plants. The extraction of sensible information from hyperspectral reflectance images is difficult due to inherent complexity of plant tissue and canopy optics, especially when recorded by IH under ambient sunlight. We aimed at obtaining a deeper insight into plant optics as perceived by IH since there is a high demand for algorithms for fruit harvesting and grading systems equipped with computer vision and robotic systems capable of working in orchard. We report on the characteristic changes in hyperspectral reflectance accompanying the accumulation of anthocyanins in healthy fruit, pigment breakdown during sunscald and phytopathogen attacks. The measurements made outdoors with a snapshot IH were compared with traditional “point” reflectance measured with a conventional spectrophotometer under controlled illumination conditions. Most of the spectral features and patterns of plant reflectance were evident in the IH-derived reflectance images. As a step forward, a novel index for highlighting tissue damages on the background of the anthocyanin absorption, BRI-M = (1/Rorange – 1/Rred + 1/RNIR), is suggested. Difficulties of the interpretation of fruit hyperspectral reflectance images recorded in situ are discussed with possible implications for plant physiology and precision horticulture practices.

scholarly journals The diverse and unanticipated roles of histone deacetylase 9 in coordinating plant development and environmental acclimation

2020 ◽  
Vol 71 (20) ◽  
pp. 6211-6225
Author(s):  
Peter G H de Rooij ◽  
Giorgio Perrella ◽  
Eirini Kaiserli ◽  
Martijn van Zanten
Keyword(s):  
Histone Deacetylase ◽  
Plant Development ◽  
Transcriptional Activation ◽  
Plant Traits ◽  
Feedback Regulation ◽  
Molecular Networks ◽  
Environmental Biology ◽  
Negative Feedback Regulation ◽  
Lysine Residues ◽  
Histone Deacetylase 9

Abstract Plants tightly control gene transcription to adapt to environmental conditions and steer growth and development. Different types of epigenetic modifications are instrumental in these processes. In recent years, an important role for the chromatin-modifying RPD3/HDA1 class I HDAC HISTONE DEACETYLASE 9 (HDA9) emerged in the regulation of a multitude of plant traits and responses. HDACs are widely considered transcriptional repressors and are typically part of multiprotein complexes containing co-repressors, DNA, and histone-binding proteins. By catalyzing the removal of acetyl groups from lysine residues of histone protein tails, HDA9 negatively controls gene expression in many cases, in concert with interacting proteins such as POWERDRESS (PWR), HIGH EXPRESSION OF OSMOTICALLY RESPONSIVE GENES 15 (HOS15), WRKY53, ELONGATED HYPOCOTYL 5 (HY5), ABA INSENSITIVE 4 (ABI4), and EARLY FLOWERING 3 (ELF3). However, HDA9 activity has also been directly linked to transcriptional activation. In addition, following the recent breakthrough discovery of mutual negative feedback regulation between HDA9 and its interacting WRKY-domain transcription factor WRKY53, swift progress in gaining understanding of the biology of HDA9 is expected. In this review, we summarize knowledge on this intriguing versatile—and long under-rated—protein and propose novel leads to further unravel HDA9-governed molecular networks underlying plant development and environmental biology.

scholarly journals Segmentation of Apples in Aerial Images under Sixteen Different Lighting Conditions Using Color and Texture for Optimal Irrigation

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1634 ◽  
Author(s):  
Sajad Sabzi ◽  
Yousef Abbaspour-Gilandeh ◽  
Ginés García-Mateos ◽  
Antonio Ruiz-Canales ◽  
José Miguel Molina-Martínez
Keyword(s):  
Precision Agriculture ◽  
Irrigation Management ◽  
Color Space ◽  
Texture Features ◽  
Apple Fruit ◽  
Segmentation Algorithm ◽  
Aerial Images ◽  
Aerial Photographs ◽  
Local Standard ◽  
Image Pixels

Due to the changes in the lighting intensity and conditions throughout the day, machine vision systems used in precision agriculture for irrigation management should be prepared for all possible conditions. For this purpose, a complete segmentation algorithm has been developed for a case study on apple fruit segmentation in outdoor conditions using aerial images. This algorithm has been trained and tested using videos with 16 different light intensities from apple orchards during the day. The proposed segmentation algorithm consists of five main steps: (1) transforming frames in RGB to CIE L*u*v* color space and applying thresholds on image pixels; (2) computing texture features of local standard deviation; (3) using intensity transformation to remove background pixels; (4) color segmentation applying different thresholds in RGB space; and (5) applying morphological operators to refine the results. During the training process of this algorithm, it was observed that frames in different light conditions had more than 58% color sharing. Results showed that the accuracy of the proposed segmentation algorithm is higher than 99.12%, outperforming other methods in the state of the art that were compared. The processed images are aerial photographs like those obtained from a camera installed in unmanned aerial vehicles (UAVs). This accurate result will enable more efficient support in the decision making for irrigation and harvesting strategies.

scholarly journals Reflectance indices as a diagnostic tool for weed control performed by multipurpose equipment in precision agriculture

2012 ◽  
Vol 30 (2) ◽  
pp. 437-447 ◽  
Author(s):  
A. Merotto JR. ◽  
C. Bredemeier ◽  
R.A. Vidal ◽  
I.C.G.R. Goulart ◽  
E.D. Bortoli ◽  
...  
Keyword(s):  
Weed Control ◽  
Precision Agriculture ◽  
Weed Management ◽  
Dry Matter ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Weed Interference ◽  
Reflectance Indices ◽  
The Relationship

Several tools of precision agriculture have been developed for specific uses. However, this specificity may hinder the implementation of precision agriculture due to an increasing in costs and operational complexity. The use of vegetation index sensors which are traditionally developed for crop fertilization, for site-specific weed management can provide multiple utilizations of these sensors and result in the optimization of precision agriculture. The aim of this study was to evaluate the relationship between reflectance indices of weeds obtained by the GreenSeekerTM sensor and conventional parameters used for weed interference quantification. Two experiments were conducted with soybean and corn by establishing a gradient of weed interference through the use of pre- and post-emergence herbicides. The weed quantification was evaluated by the normalized difference vegetation index (NDVI) and the ratio of red to near infrared (Red/NIR) obtained using the GreenSeekerTM sensor, the visual weed control, the weed dry matter, and digital photographs, which supplied information about the leaf area coverage proportions of weed and straw. The weed leaf coverage obtained using digital photography was highly associated with the NDVI (r = 0.78) and the Red/NIR (r = -0.74). The weed dry matter also positively correlated with the NDVI obtained in 1 m linear (r = 0.66). The results indicated that the GreenSeekerTM sensor originally used for crop fertilization could also be used to obtain reflectance indices in the area between rows of crops to support decision-making programs for weed control.

scholarly journals GEOMETRIC AND REFLECTANCE SIGNATURE CHARACTERIZATION OF COMPLEX CANOPIES USING HYPERSPECTRAL STEREOSCOPIC IMAGES FROM UAV AND TERRESTRIAL PLATFORMS

2016 ◽  
Vol XLI-B7 ◽  
pp. 77-82
Author(s):  
E. Honkavaara ◽  
T. Hakala ◽  
O. Nevalainen ◽  
N. Viljanen ◽  
T. Rosnell ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Data Sets ◽  
Hyperspectral Reflectance ◽  
Model Inversion ◽  
Sensing Applications ◽  
Sensing Technology ◽  
Remote Sensing Applications ◽  
Airborne Vehicle

Light-weight hyperspectral frame cameras represent novel developments in remote sensing technology. With frame camera technology, when capturing images with stereoscopic overlaps, it is possible to derive 3D hyperspectral reflectance information and 3D geometric data of targets of interest, which enables detailed geometric and radiometric characterization of the object. These technologies are expected to provide efficient tools in various environmental remote sensing applications, such as canopy classification, canopy stress analysis, precision agriculture, and urban material classification. Furthermore, these data sets enable advanced quantitative, physical based retrieval of biophysical and biochemical parameters by model inversion technologies. Objective of this investigation was to study the aspects of capturing hyperspectral reflectance data from unmanned airborne vehicle (UAV) and terrestrial platform with novel hyperspectral frame cameras in complex, forested environment.

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