Dynamic laboratory measurement with dielectric sensor for forage mass flow determination

Kumhála, F.; Kvíz, Z.; Kmoch, J.; Prošek, V.

doi:10.17221/1959-rae

Cited by 9 publications

(3 citation statements)

References 4 publications

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“…For example Kumhála et al (2007) designed a parallel plate capacitance sensor for forage mass determination. Kandala and Sundaram (2010) estimated mass ratio of the total kernels within a sample of in-shell peanuts using RF impedance method.…”

Section: Introductionmentioning

confidence: 99%

Moisture content prediction of Iranian wheat using dielectric technique

Firouz

Alimardani

2012

J Food Sci Technol

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The moisture measurement is the most important criteria in harvesting and post harvesting processing of wheat. This paper investigates the moisture dependent dielectric constant and calibration equation of three most cultivated wheat varieties in Iran. An instrument with a cylindrical capacitive sensor was used to measure the dielectric constant of grain at different moisture contents. Samples were prepared at five levels of moisture content. To validate the proposed equations, the dried material dielectric constant was estimated and compared with the measured ones. Homographic regression models showed an acceptable prediction of dried wheat dielectric constant. Results showed that the calibration equation obtained for Shahriar variety was able to predict the moisture content of other varieties confidently. The lowest value of R 2 00.985 between predicted and reference moisture content for Tajan variety is still a good result.

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Section: Introductionmentioning

confidence: 99%

Moisture content prediction of Iranian wheat using dielectric technique

Firouz

Alimardani

2012

J Food Sci Technol

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“…Capacitive seeding detection is grounded on the concept that the equivalent dielectric constant of the capacitive sensor alters when seeds pass through. Yet, this method is less responsive to single seeds, making it unsuitable for the precise counting of minor seed quantities, and it displays a high output impedance with a subpar load capacity [15][16][17][18][19]. Machine vision sensor detection revolves around capturing real-time seed data using an industrial camera, which then forwards this information to an image-processing center.…”

Section: Introductionmentioning

confidence: 99%

An Online Method for Detecting Seeding Performance Based on Improved YOLOv5s Model

Zhao,

Xi,

Shi

et al. 2023

Agronomy

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Prior to dispatch from manufacturing facilities, seeders require rigorous performance evaluations for their seeding capabilities. Conventional manual inspection methods are notably less efficient. This study introduces a wheat seeding detection approach anchored in an enhanced YOLOv5s image-processing technique. Building upon the YOLOv5s framework, we integrated four CBAM attention mechanism modules into its model. Furthermore, the traditional upsampling technique in the neck layer was superseded by the CARAFE upsampling method. The augmented model achieved an mAP of 97.14%, illustrating its ability to elevate both the recognition precision and processing speed for wheat seeds while ensuring that the model remains lightweight. Leveraging this advanced model, we can effectively count and locate seed images, enabling the precise calculation and assessment of sowing uniformity, accuracy, and dispersion. We established a sowing test bench and conducted experiments to validate our model. The results showed that after the model was improved, the average accuracy of wheat recognition was above 97.55% under different sowing rates and travel speeds. This indicates that this method has high precision for the total number of seed particles. The sowing rate and sowing travel speed were consistent with manual measurements and did not significantly affect uniformity, accuracy, or dispersion.

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“…The results showed a strong relationship between the weight of material and the sensor's signal. They claimed that it is possible to recommend this experimental method for further research and development [13]. Kumhala et al (2010) tested a parallel plate capacitive throughput sensor for the purpose of chopped maize throughput measurement.…”

Section: Introductionmentioning

confidence: 99%

Feasibility Study of a Pivoted-Plate Sensor for Silage Corn Yield Monitoring

Maharlouie¹,

Loghavi²,

Kamgar³

2012

Int J of Agri Scie

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Crop yield map is a useful means to quantify yield variations within the fields and to provide a scientific basis for implementing site -specific crop management strategies. This research explores the feasibility of developing a new silage yield monitoring system by employing a new mass flow sensor based on using the momentum of chopped material impacting a pivoted plate loaded by a retractable spring installed at the end of a chopper discharge spout. A precise rotary potentiometer was used to measure the degree of pivoted plate rotation. To save and process the output voltage of the potentiometer, a programmable circuit comprising of AVR microcontroller ATMEGA series and MAX 232 microchip was used and the output data was sent to a portable computer for further processing. To calibrate the output signal of the sensor versus instantaneous chopped material flow rate, the chopper was fed artificially with predetermined mass flow rates of 6, 8 and 10 kg s -1 of silage corn at five replications for each feeding rate. The results of statistical analysis showed high correlation between degrees of the pivoted-plate rotation and instantaneous feed rates with adjusted R 2 of 0.98. To evaluate the accuracy of mass flow rate estimation, a platform scale weighing system was used during the field tests. The statistical analysis did not show any significant difference between sensor mass flow rate estimation and platform scale weighing system output. By using the resulted calibration equation, yield map of a silage cornfield was generated.

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Dynamic laboratory measurement with dielectric sensor for forage mass flow determination

Cited by 9 publications

References 4 publications

Moisture content prediction of Iranian wheat using dielectric technique

Moisture content prediction of Iranian wheat using dielectric technique

An Online Method for Detecting Seeding Performance Based on Improved YOLOv5s Model

Feasibility Study of a Pivoted-Plate Sensor for Silage Corn Yield Monitoring

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