Mapping Peanut Yield Variability with an Experimental Load Cell Yield Monitoring System

2015 ◽  
pp. 1131-1140
Author(s):  
J. S. Durrence ◽  
C. D. Perry ◽  
G. Vellidis ◽  
D. L. Thomas ◽  
C. K. Kvien
Keyword(s):  
Monitoring System ◽  
Load Cell ◽  
Cell Yield ◽  
Yield Variability ◽  
Yield Monitoring

scholarly journals An RFID-Based Automated Individual Perching Monitoring System for Group-Housed Poultry

2019 ◽  
Vol 62 (3) ◽  
pp. 695-704 ◽  
Author(s):  
Kailao Wang ◽  
Kai Liu ◽  
Hongwei Xin ◽  
Lilong Chai ◽  
Yu Wang ◽  
...  
Keyword(s):  
Body Weight ◽  
Monitoring System ◽  
Radio Frequency Identification ◽  
Laying Hens ◽  
Well Being ◽  
Load Cell ◽  
Commercial Application ◽  
Group Setting ◽  
The Relationship ◽  
Cell Module

Abstract. Perching is a natural behavior of poultry. Considerable research has been done to explore the relationship between group overall perch usage and well-being of laying hens. To quantify the potential cause-effect relationship on individual hens with different health or well-being status (e.g., keel bone deformation, foot pad lesion, social ranking) in a group, it is necessary to identify the perching behavior of individual birds. However, continuously monitoring individual birds in a group poses considerable challenges. To enable such research and potential commercial application, this study developed and validated a radio frequency identification (RFID) based automated perching monitoring system (APMS) for characterizing individual perching behaviors of group-housed poultry. The APMS consisted of an RFID module, a load cell module, and a round wooden perch. The RFID module was comprised of a high-frequency RFID reader, three customized rectangular antennas placed under the perch, and RFID transponders attached to the birds. The load cell module was comprised of a data acquisition system and two load cells supporting both ends of the perch. The daily number of perch visits (PV) and perching duration (PD) for individual birds were used to delineate perching behavior. Three identical experimental pens, five hens per pen, were equipped with the monitoring system. Two RFID transponders were attached to each hen (one per leg), and a distinct color was marked on the bird’s head for video or visual identification and validation. Performance of the APMS was validated by comparing the system outputs with manual observation and labeling over an entire day. Sensitivity and specificity of the system were shown to improve from 97.77% and 99.88%, respectively when using only the RFID module to 99.83% and 99.93% when incorporating weight information from the load cell module. Using this system, we conducted a preliminary trial on the relationship of perching behavior and body weight of laying hens, which revealed little effect of body weight but considerable variability in perching behavior among the individual hens. The study demonstrated that the APMS had excellent performance in measuring perching behaviors of individual birds in a group. The APMS offers great potential for delineating individual differences in perching behavior among hens with different social status or health conditions in a group setting. Keywords: Individual perching behavior, Laying hen, Load cell, Precision livestock farming, RFID, Welfare.

scholarly journals Multi sensor data and temporal image fusion cross validation technique for Agri yield monitoring system

2021 ◽  
Author(s):  
Swati V. Shinde ◽  
Rajveer Shastri ◽  
Atul Kumar Dwivedi ◽  
Anandakumar Haldorai ◽  
Varsha Sahni ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Monitoring System ◽  
Cross Validation ◽  
Sensor Data ◽  
Temporal Data ◽  
Multiple Sensors ◽  
Multi Sensor Data Fusion ◽  
Validation Technique ◽  
The Cross ◽  
Yield Monitoring

Abstract In recent years, the diverse application in various disciplines and the versatility has gained a huge interest for the researchers to research on the multi-sensor data fusion technology. The remote sensing process involves the measurement and recording of the data from a scene. Thus, the remote sensing systems are known to be a powerful tool as they help in the earth's atmosphere and surface monitor at different scales. The remote sensing of the data faces a serious challenge as the data captured by the multiple sensors are heterogeneous. This affects the efficient processing and the effectiveness of the data that is being sensed. Thus, the increase in the diversity in data increases the ancillary datasets. These multimodal datasets are used jointly to improve the processing performance as per the application requirement. Initially, the fusion of the temporal data with the backscattered/temporal data is possible from the data retrieved from remote sensing. Many researchers made several types of research on fusing the multi-temporal and multimodal data and gave different ideas for a different type of researchers. This paper presents the cross-validation technique for monitoring the yield. This monitoring system is developed by fusing the multi-sensor data and the temporal images. This fusion is performed, and the performance of the yield monitoring system is analyzed from the results obtained. By using the cross-validation technique, the efficiency of the system is found to be improved.

scholarly journals Internet of things (IoT) based smart garbage monitoring system

2020 ◽  
Vol 20 (2) ◽  
pp. 736
Author(s):  
Thangavel Bhuvaneswari ◽  
J. Hossen ◽  
NurAsyiqinbt. Amir Hamzah ◽  
P. Velrajkumar ◽  
Oo Hong Jack
Keyword(s):  
Internet Of Things ◽  
Monitoring System ◽  
Garbage Collection ◽  
Ultrasonic Sensor ◽  
Load Cell ◽  
The Internet ◽  
Iot Platform ◽  
Human Effort ◽  
Arduino Microcontroller ◽  
Collection And Management

<p>Garbage waste monitoring, collection and management is one of the primary concerns of the present era due to its detrimental effects on environment. The traditional way of manually monitoring and collecting the garbage is a cumbersome process as it requires considerable human effort and time leading to higher cost. In this paper, an IoT based garbage monitoring system using Thingspeak, an open IoT platform is presented. The system consists of an Arduino microcontroller, an ultrasonic sensor, a load cell and a Wi-Fi module. The Arduino microcontroller receives data from the ultrasonic sensor and load cell. The depth of the garbage in the bin is measured using ultrasonic sensor and the weight of the bin with garbage is measured from the load cell. The LCD screen is used to display the data. The Wi-Fi module transmits the above data to the internet. An open IoT platform Thingspeak is used to monitor the garbage system. With this system, the administrator can monitor and schedule garbage collection more efficiently. A prototype has been developed and tested. It has been found to work satisfactorily. The details are presented in this paper.</p>

Design and Evaluation of a Yield Monitoring System for Pistachios

2011 ◽  
Vol 54 (5) ◽  
pp. 1555-1567 ◽  
Author(s):  
U. A. Rosa ◽  
T. S. Rosenstock ◽  
H. Choi ◽  
D. Pursell ◽  
C. J. Gliever ◽  
...  
Keyword(s):  
Monitoring System ◽  
Yield Monitoring

Sugar Cane Yield Monitoring System

2001 ◽  
Author(s):  
Caryn E. Benjamin ◽  
Dr. Michael P. Mailander ◽  
Dr. Randy R. Price
Keyword(s):  
Monitoring System ◽  
Sugar Cane ◽  
Cane Yield ◽  
Yield Monitoring
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