scholarly journals Low-Cost, Computer Vision-Based, Prebloom Cluster Count Prediction in Vineyards

2021 ◽  
Vol 3 ◽  
Author(s):  
Jonathan Jaramillo ◽  
Justine Vanden Heuvel ◽  
Kirstin H. Petersen
Keyword(s):  
Computer Vision ◽  
Traditional Method ◽  
Low Cost ◽  
Maximum Error ◽  
Calibration Data ◽  
Traditional Methods ◽  
Growing Seasons ◽  
Highly Sensitive ◽  
Average Cluster ◽  
Selection Of

Traditional methods for estimating the number of grape clusters in a vineyard generally involve manually counting the number of clusters per vine in a subset of the vineyard and scaling by the total number of vines; a technique that can be laborious, costly, and with an accuracy that depends on the size of the sample. We demonstrate that traditional cluster counting has a high variance in yield estimate accuracy and is highly sensitive to the particular counter and choice of the subset of counted vines. We propose a simple computer vision-based method for improving the reliability of these yield estimates using cheap and easily accessible hardware for growers. This method detects, tracks, and counts clusters and shoots in videos collected using a smartphone camera that is driven or walked through the vineyard at night. With a random selection of calibration data, this method achieved an average cluster count error of 4.9% across two growing seasons and two cultivars by detecting and counting clusters. Traditional methods yielded an average cluster count error of 7.9% across the same dataset. Moreover, the proposed method yielded a maximum error of 12.6% while the traditional method yielded a maximum error of 23.5%. The proposed method can be deployed before flowering, while the canopy is sparse, which improves maximum visibility of clusters and shoots, generalizability across different cultivars and growing seasons, and earlier yield estimates compared to prior work in the area.

Robustness of NIR Calibrations for Soluble Solids in Intact Melon and Pineapple

1998 ◽  
Vol 6 (1) ◽  
pp. 259-265 ◽  
Author(s):  
John Guthrie ◽  
Brett Wedding ◽  
Kerry Walsh
Keyword(s):  
Near Infrared ◽  
Low Cost ◽  
Silicon Detector ◽  
Soluble Solids ◽  
Calibration Data ◽  
Stepwise Multiple Linear Regression ◽  
Growing Seasons ◽  
Harvest Dates ◽  
Pineapple Fruit ◽  
Solids Content

The soluble solids content of intact fruit can be measured non-invasively by near infrared spectroscopy, allowing “sweetness” grading of individual fruit. However, little information is available in the literature with respect to the robustness of such calibrations. We developed calibrations based on a restricted wavelength range (700–1100 nm), suitable for use with low-cost silicon detector systems, using a stepwise multiple linear regression routine. Calibrations for total soluble solids (°Brix) in intact pineapple fruit were not transferable between summer and winter growing seasons. A combined calibration (data of three harvest dates) validated reasonably well against a population set drawn from all harvest dates ( r2 = 0.72, SEP = 1.84 °Brix). Calibrations for Brix in melon were transferable between two of the three varieties examined. However, a lack of robustness of calibration was indicated by poor validation within populations of fruit harvested at different times. Further work is planned to investigate the robustness of calibration across varieties, growing districts and seasons.

UAV attitude estimation based on the dual filtering methods

2018 ◽  
Vol 6 (1) ◽  
pp. 21-31 ◽  
Author(s):  
Zhemin Zhuang ◽  
Zhijie Guo ◽  
Alex Noel Joseph Raj ◽  
Canzhu Guo
Keyword(s):  
Traditional Method ◽  
Low Cost ◽  
Attitude Estimation ◽  
Convergence Speed ◽  
Experimental Results ◽  
Convergence Time ◽  
Accelerometer Data ◽  
Traditional Methods ◽  
Content Type ◽  
Flight Data

Purpose A toy UAV performs tumbling, rolling, racing and other complex activities. It is based on low-cost hardware and hence requires a better algorithm to estimate the attitudes more accurately with low power consumption. The proposed technique based on optimized Madgwick filter and moving average filter (MAF) ensures improved convergence speed in estimating the attitude, achieves higher accuracy and provides robustness and stability of the toy UAV. The paper aims to discuss this issue. Design/methodology/approach Traditional methods are prone to problems such as slow convergence speed and errors in calculation of the attitude angles. These errors cause the vehicle to drift and tremble, thus affecting the overall stability of the vehicle. The proposed method combines the features of optimized Madgwick filter and MAF to provide better accuracy, achieved through the fusion of gyroscope and accelerometer data, and zero correction to eliminate the random drift error of the gyroscope and removal of high-frequency interference by MAF of the accelerometer data. The experimental results on actual flight data showed that the method was better than the conventional Madgwick and Mahony complementary filters. Findings The performance of the proposed method was analyzed by estimating the pitch and roll angles under the static and dynamic condition of the toy UAV. The results were compared with two traditional methods: Madgwick and Mahony complement filter. In the static condition, the variance and average error while estimating the attitudes was comparatively lower than the traditional method. For the dynamic conditions, the convergence time to achieve a prescribed swing angle was again lower than the traditional method. From these two experiments, it can be seen that the proposed method provides better attitude estimation at lower computation time. Originality/value The proposed method combines the optimized Madgwick filter and MAF to accuracy estimate the attitude of toy UAV. The algorithm mainly suits the toy UAVs which are based on low-cost hardware and require better control systems to ensure stability of the vehicle. The experimental results on real flight data illustrate that the method not only improves the convergence speed in estimating the attitude angle for large maneuvers of the toy UAV, but also achieves higher accuracy in the attitude estimation, thus ensuring the robustness and stability of the UAV.

Competition of Verb Aspects and Its Resolution as a Creative Act (Based on the Material of Aspects Variants in the Texts by F. M. Dostoevsky)

2020 ◽  
Vol 81 (2) ◽  
pp. 56-63
Author(s):  
S. A. Karpukhin
Keyword(s):  
Traditional Method ◽  
Contextual Analysis ◽  
Verb Aspect ◽  
Component Theory ◽  
Two Component ◽  
Artistic Interpretation ◽  
New Perspective ◽  
First Time ◽  
Selection Of ◽  
Aspectual Meaning

The article considers the competition of verbal aspects from a new perspective. Instead of employing the traditional method of demonstrating this phenomenon — an empirical replacement of the aspect of a verb in a phrase with the opposite — the author examines Dostoevsky’s choice between the variants found in different manuscripts of the same text. For the first time, based on a two-component theory of the semantic invariant of a verb type, the aspectual meaning of the selection of a verb aspect is revealed and, as a result of contextual analysis, an artistic interpretation of the selected type is proposed.

scholarly journals Design Considerations and Selection of Cost-Effective Switched Reluctance Drive for Radiator Cooling Fans

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 917
Author(s):  
Ickjin Son ◽  
Grace Firsta Lukman ◽  
Mazahir Hussain Shah ◽  
Kwang-Il Jeong ◽  
Jin-Woo Ahn
Keyword(s):  
Low Cost ◽  
Cost Effective ◽  
Magnetic Characteristics ◽  
Switched Reluctance Motors ◽  
Switched Reluctance ◽  
Design Considerations ◽  
Cooling Fan ◽  
Cooling Fans ◽  
Power Switches ◽  
Selection Of

Switched reluctance motors (SRMs) are simple in structure, easy to manufacture, magnet-less, brushless, and highly robust compared to other AC motors which makes them a good option for applications that operate in harsh environment. However, the motor has non-linear magnetic characteristics, and it comes with various pole-phase combinations and circuit topologies that causes many difficulties in deciding on which type to choose. In this paper, the viability of SRM as a low-cost, rugged machine for vehicle radiator cooling fan is considered. First, necessary design considerations are presented, then three commonly use types of SRM are analyzed: A 3-phase 6/4, 3-phase 12/8, and a 4-phase 8/6 to find their static and dynamic characteristics so the most suitable type can be selected. Simulation results show that the 8/6 SRM produces the highest efficiency with less phase current which reduces the converter burden. However, with asymmetric half bridge converter, eight power switches are required for 8/6 SRM and thus put a burden on the overall drive cost. As a solution, the Miller converter with only six switches for four phase SRM. To verify the proposed idea, the 8/6 SRM was manufactured and tested. The results show that Miller converter can be used for the proposed SRM with slightly reduced efficiency at 80.4%.

scholarly journals A rapid, accurate, scalable, and portable testing system for COVID-19 diagnosis

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Guanhua Xun ◽  
Stephan Thomas Lane ◽  
Vassily Andrew Petrov ◽  
Brandon Elliott Pepa ◽  
Huimin Zhao
Keyword(s):  
Limit Of Detection ◽  
Low Cost ◽  
High Volume ◽  
N Gene ◽  
Testing System ◽  
Target Sequence ◽  
One Pot ◽  
Sequence Detection ◽  
Highly Sensitive ◽  
Speed Accuracy

AbstractThe need for rapid, accurate, and scalable testing systems for COVID-19 diagnosis is clear and urgent. Here, we report a rapid Scalable and Portable Testing (SPOT) system consisting of a rapid, highly sensitive, and accurate assay and a battery-powered portable device for COVID-19 diagnosis. The SPOT assay comprises a one-pot reverse transcriptase-loop-mediated isothermal amplification (RT-LAMP) followed by PfAgo-based target sequence detection. It is capable of detecting the N gene and E gene in a multiplexed reaction with the limit of detection (LoD) of 0.44 copies/μL and 1.09 copies/μL, respectively, in SARS-CoV-2 virus-spiked saliva samples within 30 min. Moreover, the SPOT system is used to analyze 104 clinical saliva samples and identified 28/30 (93.3% sensitivity) SARS-CoV-2 positive samples (100% sensitivity if LoD is considered) and 73/74 (98.6% specificity) SARS-CoV-2 negative samples. This combination of speed, accuracy, sensitivity, and portability will enable high-volume, low-cost access to areas in need of urgent COVID-19 testing capabilities.

scholarly journals Identification of the Optimum Environments for the High Yield and Quality Traits of Lentil Genotypes Evaluated in Multi-Location Trials

2021 ◽  
Vol 13 (15) ◽  
pp. 8247
Author(s):  
Dimitrios N. Vlachostergios ◽  
Christos Noulas ◽  
Anastasia Kargiotidou ◽  
Dimitrios Baxevanos ◽  
Evangelia Tigka ◽  
...  
Keyword(s):  
Crude Protein ◽  
Agronomic Traits ◽  
High Yield ◽  
Breeding Programs ◽  
Growing Seasons ◽  
Yield And Quality ◽  
Food And Feed ◽  
Superior Genotypes ◽  
Selection Of ◽  
The Ideal

Lentil is a versatile and profitable pulse crop with high nutritional food and feed values. The objectives of the study were to determine suitable locations for high yield and quality in terms of production and/or breeding, and to identify promising genotypes. For this reason, five lentil genotypes were evaluated in a multi-location network consisting of ten diverse sites for two consecutive growing seasons, for seed yield (SY), other agronomic traits, crude protein (CP), cooking time (CT) and crude protein yield (CPY). A significant diversification and specialization of the locations was identified with regards to SY, CP, CT and CPY. Different locations showed optimal values for each trait. Locations E4 and E3, followed by E10, were “ideal” for SY; locations E1, E3 and E7 were ideal for high CP; and the “ideal” locations for CT were E3 and E5, followed by E2. Therefore, the scope of the cultivation determined the optimum locations for lentil cultivation. The GGE-biplot analysis revealed different discriminating abilities and representativeness among the locations for the identification of the most productive and stable genotypes. Location E3 (Orestiada, Region of Thrace) was recognized as being optimal for lentil breeding, as it was the “ideal” or close to “ideal” for the selection of superior genotypes for SY, CP, CT and CPY. Adaptable genotypes (cv. Dimitra, Samos) showed a high SY along with excellent values for CP, CT and CPY, and are suggested either for cultivation in many regions or to be exploited in breeding programs.

scholarly journals An IoT-Based Participatory Antitheft System for Public Safety Enhancement in Smart Cities

Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 919-937
Author(s):  
Nikos Papadakis ◽  
Nikos Koukoulas ◽  
Ioannis Christakis ◽  
Ilias Stavrakas ◽  
Dionisis Kandris
Keyword(s):  
Smart Cities ◽  
Low Cost ◽  
Negative Influence ◽  
Asset Tracking ◽  
Tracking Tasks ◽  
Tracking Process ◽  
The Impact ◽  
Operation Center ◽  
Safety Enhancement ◽  
Selection Of

The risk of theft of goods is certainly an important source of negative influence in human psychology. This article focuses on the development of a scheme that, despite its low cost, acts as a smart antitheft system that achieves small property detection. Specifically, an Internet of Things (IoT)-based participatory platform was developed in order to allow asset-tracking tasks to be crowd-sourced to a community. Stolen objects are traced by using a prototype Bluetooth Low Energy (BLE)-based system, which sends signals, thus becoming a beacon. Once such an item (e.g., a bicycle) is stolen, the owner informs the authorities, which, in turn, broadcast an alert signal to activate the BLE sensor. To trace the asset with the antitheft tag, participants use their GPS-enabled smart phones to scan BLE tags through a specific smartphone client application and report the location of the asset to an operation center so that owners can locate their assets. A stolen item tracking simulator was created to support and optimize the aforementioned tracking process and to produce the best possible outcome, evaluating the impact of different parameters and strategies regarding the selection of how many and which users to activate when searching for a stolen item within a given area.

scholarly journals An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 343
Author(s):  
Kim Bjerge ◽  
Jakob Bonde Nielsen ◽  
Martin Videbæk Sepstrup ◽  
Flemming Helsing-Nielsen ◽  
Toke Thomas Høye
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Low Cost ◽  
Light Trap ◽  
Automatic Monitoring ◽  
Light Sources ◽  
Monitoring Methods ◽  
Computer Vision System ◽  
Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

scholarly journals Immobilization of Pseudomonas aeruginosa static biomass on eggshell powder for on-line preconcentration and determination of Cr (VI)

2020 ◽  
Vol 18 (1) ◽  
pp. 303-313 ◽  
Author(s):  
Aamir Rasheed ◽  
Tahseen Ghous ◽  
Sumaira Mumtaz ◽  
Muhammad Nadeem Zafar ◽  
Kalsoom Akhter ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Low Cost ◽  
Colorimetric Detection ◽  
Time Flow ◽  
Highly Sensitive ◽  
Glass Column ◽  
On Line ◽  
Preconcentration Time ◽  
Ultra Trace

AbstractIn the present work, a novel continuous flow system (CFS) is developed for the preconcentration and determination of Cr (VI) using Pseudomonas aeruginosa static biomass immobilized onto an effective and low-cost solid support of powdered eggshells. A mini glass column packed with the immobilized biosorbent is incorporated in a CFS for the preconcentration and determination of Cr (VI) from aqueous solutions. The method is based on preconcentration, washing and elution steps followed by colorimetric detection with 1,5-diphenyl carbazide in sulphuric acid. The effects of several variables such as pH, retention time, flow rate, eluent concentration and loaded volume are studied. Under optimal conditions, the CFS method has a linear range between 10 and 100 μg L-1 and a detection limit of 6.25 μg L-1 for the determination of Cr (VI). The sampling frequency is 10 samples per hour with a preconcentration time of 5 mins. Furthermore, after washing with a 0.1 M buffer (pH 3.0), the activity of the biosorbent is regenerated and remained comparable for more than 200 cycles. Scanning electron microscopy reveals a successful immobilization of biomass on eggshells powder and precipitation of Cr (VI) on the bacterial cell surface. The proposed method proves highly sensitive and could be suitable for the determination of Cr (VI) at an ultra-trace level.

scholarly journals Genotype-Dependent Tipburn Severity During Lettuce Hydroponic Culture Is Associated with Altered Nutrient Leaf Content

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 616
Author(s):  
Virginia Birlanga ◽  
José Ramón Acosta-Motos ◽  
José Manuel Pérez-Pérez
Keyword(s):  
Low Cost ◽  
Nutrient Content ◽  
Leafy Vegetables ◽  
Production Methods ◽  
Growing Seasons ◽  
Lactuca Sativa L ◽  
Hydroponic Cultivation ◽  
Highly Correlated ◽  
Nutrient Solutions ◽  
Nutrient Traits

Cultivated lettuce (Lactuca sativa L.) is one of the most important leafy vegetables in the world, and most of the production is concentrated in the Mediterranean Basin. Hydroponics has been successfully utilized for lettuce cultivation, which could contribute to the diversification of production methods and the reduction of water consumption and excessive fertilization. We devised a low-cost procedure for closed hydroponic cultivation and easy phenotyping of root and shoot attributes of lettuce. We studied 12 lettuce genotypes of the crisphead and oak-leaf subtypes, which differed on their tipburn resistance, for three growing seasons (Fall, Winter, and Spring). We found interesting genotype × environment (G × E) interactions for some of the studied traits during early growth. By analyzing tipburn incidence and leaf nutrient content, we were able to identify a number of nutrient traits that were highly correlated with cultivar- and genotype-dependent tipburn. Our experimental setup will allow evaluating different lettuce genotypes in defined nutrient solutions to select for tipburn-tolerant and highly productive genotypes that are suitable for hydroponics.

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