scholarly journals A non-destructive method for rapid acquisition of grassland aboveground biomass for satellite ground verification using UAV RGB images

2022 ◽  
pp. e01999
Author(s):  
Huifang Zhang ◽  
Zhonggang Tang ◽  
Binyao Wang ◽  
Baoping Meng ◽  
Yu Qin ◽  
...  
2020 ◽  
Vol 6 (11) ◽  
pp. 122
Author(s):  
Bernhard Althaus ◽  
Michael Blanke

(1) The objective of the present study was to identify suitable parameters to determine the (degree of) freshness of Bell pepper fruit of three colors (yellow, red, and green) over a two-week period including the occurrence of shrivel using non-destructive real-time measurements (2) Materials and methods: Surface glossiness was measured non-destructively with a luster sensor type CZ-H72 (Keyence Co., Osaka, Japan), a colorimeter, a spectrometer and a profilometer type VR-5200 (Keyence) to obtain RGB images. (3) Results: During storage and shelf life, bell pepper fruit of initially 230–245 g lost 2.9–4.8 g FW per day at 17 °C and 55% rh. Shriveling started at 6–8% weight loss after 4–5 days and became more pronounced. Glossiness decreased from 450–500 a.u. with fresh fruit without shrivel, 280–310 a.u. with moderately shriveled fruit to 80–90 a.u. with severely shriveled fruit irrespective of color against a background of <40 a.u. within the same color, e.g., light red and dark red. Non-invasive color measurements showed no decline in Lab values (chlorophyll content), irrespective of fruit color and degree of shrivel. RGB images, converted into false color images, showed a concomitant increase in surface roughness (Sa) from Sa = ca. 2 µm for fresh and glossy, Sa = ca. 7 µm for moderately shriveled to Sa = ca. 24 µm for severely shriveled rough surfaces of stored pepper fruit, equivalent to a 12-fold increase in surface roughness. The light reflectance peak at 630–633 nm was universal, irrespective of fruit color and freshness. Hence, a freshness index based on (a) luster values ≥ 450 a.u., (b) Sa ≤ 2 µm and (c) the difference in relative reflectance in % between 630 nm and 500 nm is suggested. The latter values declined from ca. 40% for fresh red Bell pepper, ca. 32% after 6 days when shriveling had started, to ca. 21% after 12 days, but varied with fruit color. (4) Conclusion: overall, it can be concluded that color measurements were unsuitable to determine the freshness of Bell pepper fruit, whereas profilometer, luster sensor, and light reflectance spectra were suitable candidates as a novel opto-electronic approach for defining and parametrizing fruit freshness.


2020 ◽  
Vol 48 (4) ◽  
pp. 2385-2398
Author(s):  
Piyanan PIPATSITEE ◽  
Apisit EIUMNOH ◽  
Rujira TISARUM ◽  
Kanyarat TAOTA ◽  
Sumaid KONGPUGDEE ◽  
...  

Rice is an important economic and staple crop in several developing countries. Indica rice cultivars, ‘KDML105’ and ‘RD6’ are clear favourites, popular throughout world for their cooking quality, aroma, flavour, long grain, and soft texture, thus consequently dominate major plantation area in Northeastern region of Thailand. The objective of present study was to validate UAV (unmanned aerial vehicle)-derived information of rice crop traits with ground truthing non-destructive measurements in these rice varieties throughout whole life span under field environment. Plant height of cv. ‘KDML105’ was more than cv. ‘RD6’ for each respective stage. Whereas, number of tillers per clump in ‘KDML105’ exhibited stability at each developmental stage, which was in contrast to ‘RD6’ (increased continuously). Moreover, 1,000 grain weight, total grain weight and aboveground biomass were higher in ‘KDML105’ than in ‘RD6’ by 1.20, 1.82 and 3.82 folds. Four vegetative indices, ExG, EVI2, NDVI and NDRE derived from UAV platform proved out to be excellent parameters to compare KDML105 and RD6, especially in the late vegetative and reproductive developmental stages. Positive relationships between NDVI and NDRE, NDRE and total yield traits, as well as NDVI and aboveground biomass were demonstrated. In contrast, total chlorophyll pigment in cv. ‘RD6’ was higher than in cv. ‘KDML105’ leading to negative correlation with NDVI. ‘KDML105’ reflected rapid adaptation to Northeastern environments, leading to maintenance of plant height and yield components. Vegetation indices derived from UAV platform and ground truth non-destructive data exhibited high correlation. ‘KDML105’ was rapidly adapted to NE environments when compared with ‘RD6’, leading to maintenance of physiological parameters (detecting by UAV), the overall growth performances and yield traits (measuring by ground truth method). This study advocates harnessing and adopting the approach of UAV platform along with ground truthing non-destructive measurements of assessing a species/cultivars performance at broad land-use scale.


Author(s):  
Uoc Quang Ngo ◽  
Duong Tri Ngo ◽  
Hoc Thai Nguyen ◽  
Thanh Dang Bui

Increasingly <span>emerging technologies in agriculture such as computer vision, artificial intelligence technology, not only make it possible to increase production. To minimize the negative impact on climate and the environment but also to conserve resources. A key task of these technologies is to monitor the growth of plants online with a high accuracy rate and in non-destructive manners. It is known that leaf area (LA) is one of the most important growth indexes in plant growth monitoring system. Unfortunately, to estimate the LA in natural outdoor scenes (the presence of occlusion or overlap area) with a high accuracy rate is not easy and it still remains a big challenge in eco-physiological studies. In this paper, two accurate and non-destructive approaches for estimating the LA were proposed with top-view and side-view images, respectively. The proposed approaches successfully extract the skeleton of cucumber plants in red, green, and blue (RGB) images and estimate the LA of cucumber plants with high precision. The results were validated by comparing with manual measurements. The experimental results of our proposed algorithms achieve 97.64% accuracy in leaf segmentation, and the relative error in LA estimation varies from 3.76% to 13.00%, which could meet the requirements of plant growth monitoring </span>systems.


Wetlands ◽  
2002 ◽  
Vol 22 (3) ◽  
pp. 626-630 ◽  
Author(s):  
Glen B. Thursby ◽  
Marnita M. Chintala ◽  
Denise Stetson ◽  
Cathleen Wigand ◽  
Denise M. Champlin

2017 ◽  
Vol 200 ◽  
pp. 31-42 ◽  
Author(s):  
Atticus E.L. Stovall ◽  
Anthony G. Vorster ◽  
Ryan S. Anderson ◽  
Paul H. Evangelista ◽  
Herman H. Shugart

2021 ◽  
Author(s):  
Edina Csákvári ◽  
Melinda Halassy ◽  
Attila Enyedi ◽  
Ferenc Gyulai ◽  
József Berke

Abstract BackgroundEinkorn wheat (Triticum monococcum L. subs. monococcum) plays an increasingly important role in agriculture, promoted by organic farming. Although the number of comparative studies about modern and ancient types of wheats are increasing, there are still some knowledge gaps. The aim of the present study was to compare ancient, traditional and modern wheats using novel methods, including field study, laboratory stress experiment and vision-based digital image analysis. The yield and grain quality parameters based on the field experiment were measured with a near-infrared optical laboratory analyser. In order to predict the aboveground biomass production under nutrient deficiency and drought stress, a controlled experiment was set up in a growth chamber. Processing was performed by image segmentation using the Adobe Photoshop CC 20.04.4 Camera RAW 11.2 plug-in. Digital image parameters were determined with the open source software ImageJ and expressed in pixels of projected area, perimeter, bounding rectangle and Feret’s diameter.ResultsWe presented a fast, real-time, non-destructive and low-cost method for estimation of wheat quality. Based on the results, digital area is suitable to estimate aboveground biomass. Digital area outperformed other digital variables in biomass prediction in relation to stress, but height and Feret’s diameter better correlated with yield and grain quality parameters. The developed technique is easy to use to assess the growth and health status of plants. An RGB digital camera is easy to operate and image acquisition can be done at will, meanwhile conventional laboratory instruments are relatively expensive, very expert-intensive and time consuming.ConclusionOur study showed that digital image analysis could be a viable alternate means for the real-time estimation of aboveground biomass and for predicting yield and grain quality parameters. We suggest that the combination of various vision-based methods could improve the estimation of wheat performance in a non-destructive and real-time way. The results also demonstrated that modern wheats had better yield production and grain quality compared to einkorn wheats, but the latter were not far behind, thus the cultivation of various species could provide a diverse and sustainable agriculture.


2015 ◽  
Vol 37 (2) ◽  
pp. 157 ◽  
Author(s):  
Charity Mundava ◽  
Antonius G. T. Schut ◽  
Petra Helmholz ◽  
Richard Stovold ◽  
Graham Donald ◽  
...  

Current methods to measure aboveground biomass (AGB) do not deliver adequate results in relation to the extent and spatial variability that characterise rangelands. An optimised protocol for the assessment of AGB is presented that enables calibration and validation of remote-sensing imagery or plant growth models at suitable scales. The protocol combines a limited number of destructive samples with non-destructive measurements including normalised difference vegetation index (NDVI), canopy height and visual scores of AGB. A total of 19 sites were sampled four times during two growing seasons. Fresh and dry matter weights of dead and green components of AGB were recorded. Similarity of responses allowed grouping into Open plains sites dominated by annual grasses, Bunch grass sites dominated by perennial grasses and Spinifex (Triodia spp.) sites. Relationships between non-destructive measurements and AGB were evaluated with a simple linear regression per vegetation type. Multiple regression models were first used to identify outliers and then cross-validated using a ‘Leave-One-Out’ and ‘Leave-Site-Out’ (LSO) approach on datasets including and excluding the identified outliers. Combining all non-destructive measurements into one single regression model per vegetation type provided strong relationships for all seasons for total and green AGB (adjusted R2 values of 0.65–0.90) for datasets excluding outliers. The model provided accurate assessments of total AGB in heterogeneous environments for Bunch grass and Spinifex sites (LSO-Q2 values of 0.70–0.88), whereas assessment of green AGB was accurate for all vegetation types (LSO-Q2 values of 0.62–0.84). The protocol described can be applied at a range of scales while considerably reducing sampling time.


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