scholarly journals Affordable Imaging Lab for Noninvasive Analysis of Biomass and Early Vigour in Cereal Crops

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Rita Armoniené ◽  
Firuz Odilbekov ◽  
Vivekanand Vivekanand ◽  
Aakash Chawade
Keyword(s):  
Imaging System ◽  
Low Cost ◽  
Dry Weight ◽  
Automated Analysis ◽  
Coefficient Of Determination ◽  
Plant Phenotyping ◽  
Cereal Crops ◽  
Small Scale ◽  
Noninvasive Analysis ◽  
Early Vigour

Plant phenotyping by imaging allows automated analysis of plants for various morphological and physiological traits. In this work, we developed a low-cost RGB imaging phenotyping lab (LCP lab) for low-throughput imaging and analysis using affordable imaging equipment and freely available software. LCP lab comprising RGB imaging and analysis pipeline is set up and demonstrated with early vigour analysis in wheat. Using this lab, a few hundred pots can be photographed in a day and the pots are tracked with QR codes. The software pipeline for both imaging and analysis is built from freely available software. The LCP lab was evaluated for early vigour analysis of five wheat cultivars. A high coefficient of determination (R2 0.94) was obtained between the dry weight and the projected leaf area of 20-day-old wheat plants and R2 of 0.9 for the relative growth rate between 10 and 20 days of plant growth. Detailed description for setting up such a lab is provided together with custom scripts built for imaging and analysis. The LCP lab is an affordable alternative for analysis of cereal crops when access to a high-throughput phenotyping facility is unavailable or when the experiments require growing plants in highly controlled climate chambers. The protocols described in this work are useful for building affordable imaging system for small-scale research projects and for education.

scholarly journals Quantifying Variation in Soybean Due to Flood Using a Low-Cost 3D Imaging System

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2682 ◽  
Author(s):  
Wenyi Cao ◽  
Jing Zhou ◽  
Yanping Yuan ◽  
Heng Ye ◽  
Henry T. Nguyen ◽  
...  
Keyword(s):  
Plant Height ◽  
3D Imaging ◽  
Plant Architecture ◽  
Imaging System ◽  
Low Cost ◽  
Plant Phenotyping ◽  
Soybean Plant ◽  
Plant Canopy ◽  
Petiole Length ◽  
Dry Biomass

Flood has an important effect on plant growth by affecting their physiologic and biochemical properties. Soybean is one of the main cultivated crops in the world and the United States is one of the largest soybean producers. However, soybean plant is sensitive to flood stress that may cause slow growth, low yield, small crop production and result in significant economic loss. Therefore, it is critical to develop soybean cultivars that are tolerant to flood. One of the current bottlenecks in developing new crop cultivars is slow and inaccurate plant phenotyping that limits the genetic gain. This study aimed to develop a low-cost 3D imaging system to quantify the variation in the growth and biomass of soybean due to flood at its early growth stages. Two cultivars of soybeans, i.e. flood tolerant and flood sensitive, were planted in plant pots in a controlled greenhouse. A low-cost 3D imaging system was developed to take measurements of plant architecture including plant height, plant canopy width, petiole length, and petiole angle. It was found that the measurement error of the 3D imaging system was 5.8% in length and 5.0% in angle, which was sufficiently accurate and useful in plant phenotyping. Collected data were used to monitor the development of soybean after flood treatment. Dry biomass of soybean plant was measured at the end of the vegetative stage (two months after emergence). Results show that four groups had a significant difference in plant height, plant canopy width, petiole length, and petiole angle. Flood stress at early stages of soybean accelerated the growth of the flood-resistant plants in height and the petiole angle, however, restrained the development in plant canopy width and the petiole length of flood-sensitive plants. The dry biomass of flood-sensitive plants was near two to three times lower than that of resistant plants at the end of the vegetative stage. The results indicate that the developed low-cost 3D imaging system has the potential for accurate measurements in plant architecture and dry biomass that may be used to improve the accuracy of plant phenotyping.

scholarly journals Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3150
Author(s):  
Riccardo Rossi ◽  
Claudio Leolini ◽  
Sergi Costafreda-Aumedes ◽  
Luisa Leolini ◽  
Marco Bindi ◽  
...  
Keyword(s):  
Processing Time ◽  
Goodness Of Fit ◽  
Plant Traits ◽  
Low Cost ◽  
3D Models ◽  
Herbaceous Plants ◽  
Coefficient Of Determination ◽  
Plant Phenotyping ◽  
Phenotypic Traits ◽  
Olive Trees

This study aims to test the performances of a low-cost and automatic phenotyping platform, consisting of a Red-Green-Blue (RGB) commercial camera scanning objects on rotating plates and the reconstruction of main plant phenotypic traits via the structure for motion approach (SfM). The precision of this platform was tested in relation to three-dimensional (3D) models generated from images of potted maize, tomato and olive tree, acquired at a different frequency (steps of 4°, 8° and 12°) and quality (4.88, 6.52 and 9.77 µm/pixel). Plant and organs heights, angles and areas were extracted from the 3D models generated for each combination of these factors. Coefficient of determination (R2), relative Root Mean Square Error (rRMSE) and Akaike Information Criterion (AIC) were used as goodness-of-fit indexes to compare the simulated to the observed data. The results indicated that while the best performances in reproducing plant traits were obtained using 90 images at 4.88 µm/pixel (R2 = 0.81, rRMSE = 9.49% and AIC = 35.78), this corresponded to an unviable processing time (from 2.46 h to 28.25 h for herbaceous plants and olive trees, respectively). Conversely, 30 images at 4.88 µm/pixel resulted in a good compromise between a reliable reconstruction of considered traits (R2 = 0.72, rRMSE = 11.92% and AIC = 42.59) and processing time (from 0.50 h to 2.05 h for herbaceous plants and olive trees, respectively). In any case, the results pointed out that this input combination may vary based on the trait under analysis, which can be more or less demanding in terms of input images and time according to the complexity of its shape (R2 = 0.83, rRSME = 10.15% and AIC = 38.78). These findings highlight the reliability of the developed low-cost platform for plant phenotyping, further indicating the best combination of factors to speed up the acquisition and elaboration process, at the same time minimizing the bias between observed and simulated data.

scholarly journals The Impacts of Climate-Induced Agricultural Drought on Four Cereal Crops: A Case Study in Bako Tibe District, Oromia National Regional State, Ethiopia

2020 ◽  
Vol 35 (1) ◽  
pp. 135
Author(s):  
Terefa Adunya ◽  
Fedhasa Chalchisa Benti
Keyword(s):  
Crop Yields ◽  
Pearson Correlation ◽  
Agricultural Drought ◽  
Extreme Weather Events ◽  
Coefficient Of Determination ◽  
Cereal Crops ◽  
Drought Indices ◽  
Trend Test ◽  
Small Scale ◽  
The Impact

<p>Increasing temperature and altered precipitation patterns lead to the extreme weather events such as drought and flood, which severely affects the agricultural production. This study was aimed to assess the impact of climate change-induced agricultural drought on four cereal crops in Bako Tibe District. Time-series climate and crop yield data, recorded from 1989 to 2018, were acquired from NASA’s data portal and Bako Research Institute. The changes in temperature and precipitation were analyzed using Mann Kendall trend test. The agricultural drought index was analyzed using R-software. The correlation between the selected yield crops and drought indices were evaluated using Pearson correlation coefficient. The results show that trends of seasonal and annual maximum and minimum temperatures were significantly increased (P&lt;0.05). However, seasonal and annual precipitations were insignificantly decreased (P&gt;0.05). Moderate to severe agricultural drought intensities happened four times in the last three decades. These drought spells spatially covered about 36% of the total area of the district. Crop yields and drought indices were significantly correlated at p-values; 0.0034, 0.043, 0.003 and 0.001 for teff, wheat, barley and maize, respectively. The coefficient of determination (R2) values of crop yields were 28.3%, 30.9%, 28.5% and 34.6% for teff, wheat, barley and maize, correspondingly. The study clearly suggests that the increase in temperature and decrease in precipitation enhanced the frequency and intensity of drought events and these impacted the selected crop yields during the past three decades. The map-based results could be used as guides for governmental and non-governmental organizations concerning on drought impact mitigation activities in the district by encouraging farmers to adopt appropriate agricultural technologies, drought tolerant crop varieties and small scale irrigation.</p>

scholarly journals Enhancing Cabbage (Brassica oleraceae Var capitata) Yields and Quality Through Microclimate Modification and Physiological Improvement Using Agronet Covers

2014 ◽  
Vol 3 (2) ◽  
pp. 24 ◽  
Author(s):  
Everlyne M. Muleke ◽  
Mwanarusi Saidi ◽  
Francis M. Itulya ◽  
Thibaud Martin ◽  
Mathieu Ngouajio
Keyword(s):  
Pore Diameter ◽  
Low Cost ◽  
Dry Weight ◽  
Sub Saharan Africa ◽  
Control Treatment ◽  
Small Scale ◽  
Fine Mesh ◽  
Brassica Oleraceae ◽  
Sub Saharan ◽  
Adverse Environmental Conditions

<p>Adverse environmental conditions have contributed to perpetual poor cabbage (<em>Brassica oleraceae var. capitata</em>) yields in sub-Saharan Africa. Elsewhere, net covers have been reported to provide a low-cost technology with the potential of modifying the microclimate around a crop for better performance. Two experiments were therefore conducted over a span of two seasons to determine the effects of agronet covers on microclimate modification and subsequent cabbage yield and quality. The treatments comprised cabbage plants grown under either fine mesh (0.4 mm pore diameter) or large mesh (0.9 mm pore diameter) agronet covers maintained permanently closed, or opened thrice weekly from 9 am to 3 pm and a control treatment where cabbage was grown in the open field. Net covering generally modified the microclimate by raising temperatures, relative humidity and volumetric water content but lowering photosynthetic active radiation and diurnal temperature range compared to control. The use of agronet covers resulted in better cabbage performance. The large mesh (0.9 mm) enhanced leaf stomatal conductance and chlorophyll content, and improved fresh and dry weight as well as head quality. Results of this study present the use of agronet covers as a potentially effective technology for use by small-scale farmers in protected cabbage culture in sub-Saharan Africa.</p>

scholarly journals A Low Cost Rokkaku Kite Setup for Aerial Photogrammetric System

2015 ◽  
Vol XL-3/W2 ◽  
pp. 103-108 ◽  
Author(s):  
A. F. Khan ◽  
K. Khurshid ◽  
N. Saleh ◽  
A. A. Yousuf
Keyword(s):  
Remote Sensing ◽  
Rural Areas ◽  
Large Scale ◽  
Imaging System ◽  
Low Cost ◽  
Control Point ◽  
Archaeological Site ◽  
Small Scale ◽  
Target Area ◽  
Small Areas

Orthogonally Projected Area (OPA) of a geographical feature has primarily been studied utilizing rather time consuming field based sampling techniques. Remote sensing on the contrary provides the ability to acquire large scale data at a snapshot of time and lets the OPA to be calculated conveniently and with reasonable accuracy. Unfortunately satellite based remote sensing provides data at high cost and limited spatial resolution for scientific studies focused at small areas such as micro lakes micro ecosystems, etc. More importantly, recent satellite data may not be readily available for a particular location. This paper describes a low cost photogrammetric system to measure the OPA of a small scale geographic feature such as a plot of land, micro lake or an archaeological site, etc. Fitted with a consumer grade digital imaging system, a Rokkaku kite aerial platform with stable flight characteristics is designed and fabricated for image acquisition. The data processing procedure involves automatic Ground Control Point (GCP) detection, intelligent target area shape determination with minimal human input. A Graphical User Interface (GUI) is built from scratch in MATLAB to allow the user to conveniently process the acquired data, archive and retrieve the results. Extensive on-field experimentation consists of multiple geographic features including flat land surfaces, buildings, undulating rural areas, and an irregular shaped micro lake, etc. Our results show that the proposed system is not only low cost, but provides a framework that is easy and fast to setup while maintaining the required constraints on the accuracy.

Tools of Transformation: Appropriate Technology in U.S. Countercultural Literature

2012 ◽  
Vol 44 (2) ◽  
pp. 75-93
Author(s):  
Peter Mortensen
Keyword(s):  
Low Cost ◽  
Appropriate Technology ◽  
Small Scale ◽  
Environmental Transformation ◽  
Buckminster Fuller ◽  
1960S And 1970S ◽  
The 1960S ◽  
Environmentally Sound ◽  
User Friendly ◽  
Second Wave

This essay takes its cue from second-wave ecocriticism and from recent scholarly interest in the “appropriate technology” movement that evolved during the 1960s and 1970s in California and elsewhere. “Appropriate technology” (or AT) refers to a loosely-knit group of writers, engineers and designers active in the years around 1970, and more generally to the counterculture’s promotion, development and application of technologies that were small-scale, low-cost, user-friendly, human-empowering and environmentally sound. Focusing on two roughly contemporary but now largely forgotten American texts Sidney Goldfarb’s lyric poem “Solar-Heated-Rhombic-Dodecahedron” (1969) and Gurney Norman’s novel Divine Right’s Trip (1971)—I consider how “hip” literary writers contributed to eco-technological discourse and argue for the 1960s counterculture’s relevance to present-day ecological concerns. Goldfarb’s and Norman’s texts interest me because they conceptualize iconic 1960s technologies—especially the Buckminster Fuller-inspired geodesic dome and the Volkswagen van—not as inherently alienating machines but as tools of profound individual, social and environmental transformation. Synthesizing antimodernist back-to-nature desires with modernist enthusiasm for (certain kinds of) machinery, these texts adumbrate a humanity- and modernity-centered post-wilderness model of environmentalism that resonates with the dilemmas that we face in our increasingly resource-impoverished, rapidly warming and densely populated world.

scholarly journals Development of a simplified gas ultracleaning process: experiments in biomass residue-based fixed-bed gasification syngas

2021 ◽  
Author(s):  
Christian Frilund ◽  
Esa Kurkela ◽  
Ilkka Hiltunen
Keyword(s):  
Activated Carbons ◽  
Low Cost ◽  
Fixed Bed ◽  
Carbonyl Sulfide ◽  
Gas Analysis ◽  
Small Scale ◽  
Cleaning Process ◽  
Medium Temperature ◽  
Gas Cleaning ◽  
Adsorption Affinity

AbstractFor the realization of small-scale biomass-to-liquid (BTL) processes, low-cost syngas cleaning remains a major obstacle, and for this reason a simplified gas ultracleaning process is being developed. In this study, a low- to medium-temperature final gas cleaning process based on adsorption and organic solvent-free scrubbing methods was coupled to a pilot-scale staged fixed-bed gasification facility including hot filtration and catalytic reforming steps for extended duration gas cleaning tests for the generation of ultraclean syngas. The final gas cleaning process purified syngas from woody and agricultural biomass origin to a degree suitable for catalytic synthesis. The gas contained up to 3000 ppm of ammonia, 1300 ppm of benzene, 200 ppm of hydrogen sulfide, 10 ppm of carbonyl sulfide, and 5 ppm of hydrogen cyanide. Post-run characterization displayed that the accumulation of impurities on the Cu-based deoxygenation catalyst (TOS 105 h) did not occur, demonstrating that effective main impurity removal was achieved in the first two steps: acidic water scrubbing (AWC) and adsorption by activated carbons (AR). In the final test campaign, a comprehensive multipoint gas analysis confirmed that ammonia was fully removed by the scrubbing step, and benzene and H2S were fully removed by the subsequent activated carbon beds. The activated carbons achieved > 90% removal of up to 100 ppm of COS and 5 ppm of HCN in the syngas. These results provide insights into the adsorption affinity of activated carbons in a complex impurity matrix, which would be arduous to replicate in laboratory conditions.

scholarly journals The grades and freshness assessment of eggs based on density detection using machine vision and weighing sensor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Supakorn Harnsoongnoen ◽  
Nuananong Jaroensuk
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Low Cost ◽  
Measuring System ◽  
Coefficient Of Determination ◽  
Water Displacement ◽  
Non Invasive ◽  
Future Potential ◽  
Non Destructive ◽  
Egg Freshness

AbstractThe water displacement and flotation are two of the most accurate and rapid methods for grading and assessing freshness of agricultural products based on density determination. However, these techniques are still not suitable for use in agricultural inspections of products such as eggs that absorb water which can be considered intrusive or destructive and can affect the result of measurements. Here we present a novel proposal for a method of non-destructive, non-invasive, low cost, simple and real—time monitoring of the grading and freshness assessment of eggs based on density detection using machine vision and a weighing sensor. This is the first proposal that divides egg freshness into intervals through density measurements. The machine vision system was developed for the measurement of external physical characteristics (length and breadth) of eggs for evaluating their volume. The weighing system was developed for the measurement of the weight of the egg. Egg weight and volume were used to calculate density for grading and egg freshness assessment. The proposed system could measure the weight, volume and density with an accuracy of 99.88%, 98.26% and 99.02%, respectively. The results showed that the weight and freshness of eggs stored at room temperature decreased with storage time. The relationship between density and percentage of freshness was linear for the all sizes of eggs, the coefficient of determination (R2) of 0.9982, 0.9999, 0.9996, 0.9996 and 0.9994 for classified egg size classified 0, 1, 2, 3 and 4, respectively. This study shows that egg freshness can be determined through density without using water to test for water displacement or egg flotation which has future potential as a measuring system important for the poultry industry.

scholarly journals Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 256
Author(s):  
Pengfei Han ◽  
Han Mei ◽  
Di Liu ◽  
Ning Zeng ◽  
Xiao Tang ◽  
...  
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Short Term Memory ◽  
Hot Spot ◽  
Low Cost ◽  
Field Evaluation ◽  
Pollution Monitoring ◽  
Coefficient Of Determination ◽  
Air Pollution Monitoring ◽  
National Monitoring

Pollutant gases, such as CO, NO2, O3, and SO2 affect human health, and low-cost sensors are an important complement to regulatory-grade instruments in pollutant monitoring. Previous studies focused on one or several species, while comprehensive assessments of multiple sensors remain limited. We conducted a 12-month field evaluation of four Alphasense sensors in Beijing and used single linear regression (SLR), multiple linear regression (MLR), random forest regressor (RFR), and neural network (long short-term memory (LSTM)) methods to calibrate and validate the measurements with nearby reference measurements from national monitoring stations. For performances, CO > O3 > NO2 > SO2 for the coefficient of determination (R2) and root mean square error (RMSE). The MLR did not increase the R2 after considering the temperature and relative humidity influences compared with the SLR (with R2 remaining at approximately 0.6 for O3 and 0.4 for NO2). However, the RFR and LSTM models significantly increased the O3, NO2, and SO2 performances, with the R2 increasing from 0.3–0.5 to >0.7 for O3 and NO2, and the RMSE decreasing from 20.4 to 13.2 ppb for NO2. For the SLR, there were relatively larger biases, while the LSTMs maintained a close mean relative bias of approximately zero (e.g., <5% for O3 and NO2), indicating that these sensors combined with the LSTMs are suitable for hot spot detection. We highlight that the performance of LSTM is better than that of random forest and linear methods. This study assessed four electrochemical air quality sensors and different calibration models, and the methodology and results can benefit assessments of other low-cost sensors.

scholarly journals Understanding Spatial Variability of NO2 in Urban Areas Using Spatial Modelling and Data Fusion Approaches

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 179
Author(s):  
Said Munir ◽  
Martin Mayfield ◽  
Daniel Coca
Keyword(s):  
Data Fusion ◽  
Spatial Variability ◽  
Urban Areas ◽  
Road Traffic ◽  
Dispersion Model ◽  
Low Cost ◽  
Spatial Modelling ◽  
City Centre ◽  
Small Scale ◽  
The City

Small-scale spatial variability in NO2 concentrations is analysed with the help of pollution maps. Maps of NO2 estimated by the Airviro dispersion model and land use regression (LUR) model are fused with measured NO2 concentrations from low-cost sensors (LCS), reference sensors and diffusion tubes. In this study, geostatistical universal kriging was employed for fusing (integrating) model estimations with measured NO2 concentrations. The results showed that the data fusion approach was capable of estimating realistic NO2 concentration maps that inherited spatial patterns of the pollutant from the model estimations and adjusted the modelled values using the measured concentrations. Maps produced by the fusion of NO2-LCS with NO2-LUR produced better results, with r-value 0.96 and RMSE 9.09. Data fusion adds value to both measured and estimated concentrations: the measured data are improved by predicting spatiotemporal gaps, whereas the modelled data are improved by constraining them with observed data. Hotspots of NO2 were shown in the city centre, eastern parts of the city towards the motorway (M1) and on some major roads. Air quality standards were exceeded at several locations in Sheffield, where annual mean NO2 levels were higher than 40 µg/m3. Road traffic was considered to be the dominant emission source of NO2 in Sheffield.

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