1. Introduction

2. Materials and Methods

2.1. Animals and field of experiment

The study was conducted concurrently with the research by Krunt et al. (2022) [27]. The measurements were carried out from June to August 2021, specifically in Prague Horoměřice at GPS coordinates 50.1147819N, 14.2174647E. The length of the day was approximately 16 hours, and that of the night was 8 hours at this location. Moreover, the average temperatures were as follows: 17.9°C in June, 18.9°C in July, and 20.3°C in August. As part of the monitoring, we addressed the role of smaller animal breeding concerning their use in systems with multiple animals. Measurements commenced with goslings of domestic geese (Anser anser domesticus) at five weeks of age and concluded at 12 weeks of age. The measurements were conducted using a fully autonomous measuring and evaluation system, which monitored and alerted to anomalies during the growth of individual animals. In addition to regular one-week health checkups for the animals, the study aimed to monitor the animals without direct interaction.

Feeding was provided ad libitum through a pelleted diet containing 20% CP and 11.2 MJ/kg ME. Throughout the monitoring period, the geese had unrestricted access to water provided in the form of a water pond, which was part of the housing [27].

Measurements using the IoT system were conducted at two levels. The first and primary level focused solely on monitoring changes in physiological and behavioural parameters of geese using RFID technology, with the most important parameter being the monitoring of individual weight gain. At this level, observation of geese using a webcam is also possible to add. The second and secondary level monitored environmental changes using a weather station and sensors to monitor environmental parameters.

The monitoring of geese encompassed the entire lodge, with the lodge dimensions of 5 x 13 m. The block diagram illustrating the monitored area of goose movement is depicted in Figure 1. This image is complemented by a webcam view (Figure 2) of the outdoor goose enclosure (a) and the covered part of the enclosure (b). The locations of the webcams are outlined in the block diagram. The covered part of the enclosure is marked as the "Feeding area / electronic scale" in Figure 2 (a), and the camera location is denoted as "Cam 1". The outdoor enclosure is indicated in Figure 2 (b) as the area with the "Water pond and Weather Station," and the camera location is marked as "Cam 2".

Figure 1. Monitored goose movement area with main sections, apparent internal and external enclosures.

Figure 1. Monitored goose movement area with main sections, apparent internal and external enclosures.

Figure 1. (a) Camera view of the covered part of the enclosure; (b) Camera view of the outdoor area of the enclosure.

Figure 1. (a) Camera view of the covered part of the enclosure; (b) Camera view of the outdoor area of the enclosure.

2.2. Measurement system – hardware equipment

2.2.1. IoT system

The system was built on wireless data transmission using the Wi-Fi standard between individual main data collection nodes. The Wi-Fi signal was distributed across the area using an LTE router, which also served as a tool for remote control of the measurement system. The goose monitoring system was designed as a modular platform consisting of three main parts: a Data acquisition system for data collection and evaluation, a Feeding area and an Electronic Scale for monitoring the weight change during the consumption of the feed ration on individual measurement days, and finally, the Environmental monitoring system, which was tasked with monitoring environmental parameters. The block diagram of the individual parts of the entire IoT system is shown in Figure 3. A picture of the distribution board as part of the Data acquisition system is shown in Figure 4, the feeding area and electronic scale are shown in Figure 5, and a picture of the weather station as part of the Environmental monitoring system is shown in Figure 7.

Figure 2. Block diagram of the proposed monitoring system.

Figure 2. Block diagram of the proposed monitoring system.

2.2.2. Data acquisition system

The data acquisition system consisted of a TP-Link Wi-Fi router and a distribution board. The system was powered by a 230 V power supply. Figure 4 shows the distribution board, which consisted of circuit breakers (1), a 12 V source (2), a computer (3), an RFID module (4), an amplifier (5), and a measurement card (6). The 12 V source (2) powered the amplifier (5). Data were measured using an Advantech USB 4716 (6) 16-bit measurement card with a sampling rate of 200 ksps. The measurement card was connected via USB to the computer (3). The measurement card was utilised to measure the voltage from the strain gauge system for weighing animals. The ambient light intensity sensor was also connected to the card, and a relay for restarting the RFID module was connected to the digital output. The data were processed in a minicomputer (3) with an Intel Core i5 8259U Coffee Lake 3.8 GHz processor, Intel Iris Plus Graphics 655, 8 GB of RAM, and a 256 GB SSD. The computer was connected to the internet via Wi-Fi. The software that recorded and processed data from individual devices ran on the computer. The activation of the created software occurred when the operating system started. The distribution board also contained an RFID module (4) with an RFID reader, which was connected via Ethernet to the system. The amplifier (5) was placed in a shielded box.

Figure 3. Distribution board of the data acquisition system.

Figure 3. Distribution board of the data acquisition system.

2.2.3. Feeding area and electronic scale

The feeding area and electronic scale, which is shown in Figure 5, consisted of a feed reservoir with a feed inlet (1), an RFID antenna (2), a feed outlet from a feed reservoir (3), and a weighing system consisting of strain gauges (4) and a weighing plate (5). The weighing system allowed the weight of small animals up to 10 kg to be determined. The dimensions of the weighing chamber were b = 360 mm in width, h = 500 mm in height and l = 420 mm in length. The dimensions are given under the same designation in Figure 5. The chamber of the weighing system was attached to the main frame using deformation members in the shape of beams set with strain gauges connected to the full bridge (4). A total of four deformation members were used, which were placed in the upper corners of the weigh chamber. Each strain gauge was designed for a maximum weight of 5 kg. The strain gauge sensors were connected in parallel to one amplifier, which allowed the elimination of incorrect weighing. The amplifier amplified the input signal and gave the strain gauge bridge power with a constant current source. The constant current source was selected at 7 mA with regard to the load of the current source and its maintenance at the setting value. The change in load was determined on the basis of the voltage drop according to the change in resistance of the strain gauges under load. The signal was then amplified and recorded using an Advantech USB 4716 measurement card. A photoresistor for measuring light intensity and a temperature and humidity sensor were placed in the upper part of the feeding area so that it was out of reach of the geese. The temperature and humidity of the environment were measured using the TH2E IP temperature and humidity sensor.

Figure 4. Feeding area and electronic scale.

Figure 4. Feeding area and electronic scale.

The weight gain analysis system was designed for the detection of individual animals using wireless UHF RFID technology. For detection, a 4-channel UHF RFID reader module, Chainway UR4, with an integrated Impinj R2000 circuit was utilised. This module allows communication via a serial line RS-232 or via Ethernet RJ45, following the communication standard EPC C1 GEN2 / ISO18000-6C. It can optimally read up to 700 RFID tags per second, featuring four channels and an antenna connection via an SMA connector. The antenna used was a 5dBi UHF RFID antenna.

Moreover, an RFID tag made of ABS material was employed, enabling communication on three different frequency types: Low Frequency (LF) with a working frequency of 125 Hz, High Frequency (HF) with a working frequency of 13.56 MHz, and ultrahigh-frequency (UHF) with a working frequency range in Europe of 865-868 MHz and in the USA and Canada of 902-928 MHz. UHF with a fixed reader read range of 1 m was used for measurement. The RFID reading module was connected via Ethernet to the LTE router, to which the measuring PC was also connected. This approach allowed the creation of more extensive measuring systems by connecting several RFID readers.

Each goose was assigned an RFID tag with a unique serial number, which was then recorded in the created database. The system recorded the current weight during feeding along with the corresponding time and associated it with the individual RFID tag serial number. Additionally, other measured values, like light intensity, could also be assigned to these numbers. One RFID channel was dedicated to measurement, while the other three channels remained unoccupied.

In Figure 6, a detail of the UHF RFID tag attached to the animal's left leg is shown. The location of the tag, in the form of a ring, was chosen for the left leg, as the antenna was mounted on the left side, preventing possible signal shielding by the goose's body. The feeding area and electronic scale were equipped with a feeding section, allowing each animal to enter the weighing chamber with the correct body orientation and be identified individually. The goose feed served as an incentive for regular visits to the weighing area. The antenna was configured to measure only on the weighing system, and it had a small transmission power of 5 dBi. The antenna was shielded from the other side by the cage structure of the feed box. Therefore, only those geese on the weighing system were detected.

Figure 5. Detail of the used RFID tag with a working frequency of 865-868 MHz.

Figure 5. Detail of the used RFID tag with a working frequency of 865-868 MHz.

2.2.4. Environment monitoring system

The environmental monitoring system comprised a weather station and separate sensors. The weather station (Figure 7) included an anemometer (1), an anemoscope (2), a rain gauge (3), a distribution board with the electronics of the weather station (4), and a photovoltaic panel (5). The mechanical anemometer (1) utilised a WH-SP-WS01 wind speed sensor to detect wind speed. The sensor's revolutions were detected non-contactly using a magnetic reed contact. The anemoscope (2), made up of a WH-SP-WD wind direction sensor, was designed to detect wind direction. The sensor contained eight magnetic switches connected to a resistor with varying resistance values, allowing for a total of 16 different rotation positions to be indicated, as the vane magnet could switch two switches simultaneously. The rain gauge MS-WH-SP-RG (3) employed the principle of self-emptying for measuring rainfall using a tilting rainwater collector. The distribution board (4) with the weather station electronics housed a battery, thermometer, barometer, regulator, and a microprocessor with a communication module. An ESP-WROOM microprocessor with a communication module was utilised, and a DS18B20 digital sensor served as the temperature sensor. The module also included sensors for air quality, temperature, pressure, and humidity: BMP280 pressure sensor, CCS811 eCO2 sensor, and Si7021 module for temperature and humidity. The weather station was designed as an independent system and was powered by a photovoltaic panel (6) with a peak power output of 4.5 W, using a Samsung ICR18650-26J, 2600mAh, Li-Ion battery. Additionally, the system integrated a flip module for measuring precipitation, a pulse system for wind speed measurement, and a resistance system for wind direction measurement.

The interior and exterior areas of the enclosure were additionally equipped with temperature, humidity, and photoresistor sensors. This information was crucial for spatial arrangements, considering that one section of the breeding area was shaded by a roof while the other was not.

A Reolink RLC-522-5MP IP web camera was used to monitor the enclosure. Recording the enclosure with animals using the camera enables analysis of their behaviour and activities throughout the day, helping to tailor the breeding system accordingly. Not all activities can be deduced from feeding analysis alone. The camera, coupled with image processing, facilitates the analysis of the animals' movement activities during the day or over a specific observation period. This includes tracking their movements during rest and feeding. The IoT camera also served to verify the geese's feeding on the weighing system. While RFID registrations provide information about the goose's presence near the antenna, they do not capture the animal's behaviour at the site. Therefore, the camera allows for analysing geese' behaviour at the feeding place, particularly during RFID tag readings.

2.3. Measurement system – software equipment

The software utilised in the Intel Core i5 8259U mini computer was Windows 10. To handle data measurement and recording, a custom application named "Husa v. 1.2.0.0" was developed using Visual Studio 2019. For data post-processing, open-source software Scilab 6.1.0 was employed. The software was designed to extract data from the RFID module, the IP device for measuring temperature and humidity, and the Advantech USB-4716 measuring card.

The RFID module could accommodate 4 antennas. The application allowed the selection of any connected antenna to sense the proximity of the RFID tag. It was capable of functioning in an autonomous mode. The principle was based on triggering an event when the presence of an RFID tag was detected within the antenna's range. Upon reading the RFID tag, the information from the tag was cross-referenced with the record of the measured animals, each assigned a specific RFID tag number. This ensured that only intended tags meant for measurement were recorded. Consequently, measurement occurred only if information was successfully read from the RFID tag of the geese. Upon event activation, individual data such as voltage from the weighing device using the Advantech card (converted to weight), current temperature, humidity, and ambient light intensity were loaded. During weight measurement, five measurements were repeated, and the average data was saved.

Figure 6. The weather station for monitoring environmental parameters.

Figure 6. The weather station for monitoring environmental parameters.

Upon launching the application, establishing communication with the RFID module via Ethernet was made possible. After connecting to the device, a request was sent for the hardware (HW) information of the RFID module, including details about the firmware version, hardware version, module number, temperature, and the set RFID frequency standard. This command acted as a watchdog in the program, triggering a device restart if no response was received. After connecting to the RFID module, the corresponding USB measuring card was selected, and the input channel number was configured. Subsequently, the number of channels of the RFID module could be chosen. As part of the measurement, one antenna was connected to the first channel. Once set up, measurement could be initiated when the RFID module sent a signal and awaited the reaction of the RFID tags. After the response, a comparison was made with the stored identification numbers. A record was created with the measured weight, ambient temperature, and ambient lighting if a match was found.

In addition to data measurement and processing, the application facilitated sending notifications, results, and anomalies regarding the condition of the animals during feeding. The system dispatched them every day at a set time, either via email or as an SMS message. Among the measured anomalies were, for instance, low attendance at the feeding place by animals or the mutual interaction of animals during feeding, leading to more than one RFID registration.

2.4. Measurement Algorithm

The flowchart illustrating the data measurement algorithm is presented in Figure 8. Upon the goose's arrival at the designated measuring point with the assigned RFID tag, the RFID tag is read. A valid code is identified in the database if the RFID tag is successfully read. This is followed by recording the goose's serial number, date, and time. Subsequently, the weight of the goose is recorded multiple times, and data from the external environment (temperature, pressure, precipitation, humidity, light intensity) are stored. All data is associated with a single time record and the animal's presence. If the RFID tag is not initially read, the system will make repeated attempts until a valid code is found in the database.

Figure 7. The algorithm of the measurement.

Figure 7. The algorithm of the measurement.

3. Results

3.1. System Validation and Monitoring Results

The observation period was from 20/06/2021 to 11/08/2021, totalling 52 days. This period corresponded to the fifth to twelfth week of the goose's life. Weight data linked to a specific code were transmitted during RFID registrations. In Table 1, the left column presents a graph evaluating the average weight values of goose number 4 per day. The X-axis displays the age of the goose in weeks, and the Y-axis indicates the weight value in kilograms. The graph clearly shows weight decreases during the rearing period. These declines were caused by medical checkups conducted once a week. This interaction was a stressful factor for the geese, resulting in reduced food intake and, consequently, weight loss. Weight loss, attributed to moulting, was also observed during the eighth week of life. From the ninth week onwards, a decline in the weight gain slope is evident. The weight gain could be described by the equation (5) With the determination coefficient of R² = 0.9916.

$$y=-0.0428x^2+1.0329x-0.4953$$

(5)

The left column also displays the RFID code for goose number 4. In the right column, goose number 4 is presented in an image, marked at the moment it was on the weighing system, i.e., when it was detected by the RFID antenna. The right column also indicates the time and date of the given image.

From the collected data, the percentage of registrations occurring during monitored visits to the feeding system was calculated. Throughout the observation period, there were a total of 323 678 RFID registrations for geese that had just entered the weighing system, with an average of 15 804 ± 3 809 (mean ± SD) registrations per goose. As the RFID tag of goose number 6 broke on 06/08/2021, goose number 6 was prematurely removed from the experiment. Therefore, it was also omitted from evaluating the average measurement value.

A web camera was utilised to validate RFID registrations. With the assistance of the web camera, it was possible to ascertain whether the goose was actually present in the weighing system at a specific time. In evaluating the entire system, the sensitivity was 93.87%, specificity 99.94%, accuracy 99.85%, and precision 95.09%. The indicated data were obtained from one monitored day 13/07/2021 from 9:00:00 to 17:00:00.

The resulting sensitivity, specificity, accuracy, and precision values for individual geese are presented in Table 2. The table also includes a comparison of the initial weight, final weight for individual geese, and their weight gain during the monitored period. Subsequently, the table shows the number of RFID registrations for the observed period and the percentage of time from the total monitored period that the goose spent on the weighing system.

The number of RFID registrations made it possible to determine the time spent on the weighing system by multiplying the number of registrations by the length of the time step, which was 3 s. By multiplying these values for goose number six, it was found that the total time spent on the weighing system was 22 794 s. In contrast, goose number 13, which had the most RFID registrations, spent 73 554 s on the weighing system. Overall, goose number six spent the least time on the weighing system. The compared time spent on the weighing system was from 20/06/2021 to 04/08/2021.

Figure 9 and 10 show a graph comparing the average time that geese spent on the weighing system each day. All twenty-one geese were compared over two chosen weeks. The first chosen week was in the period from 21/06/2021 to 27/06/2021, from the second day of observation. This was chosen because some geese started feeding on the second day, likely due to the stress caused by transportation and a new environment. The total time the geese spent on the weighing system on the first day of observation, 20/06/2021, was only 11 754 s. In comparison, they spent 22 704 s on the weighing system on the second day. Figure 9 shows the comparison of the average daily time spent on the weighing system during the first week. The X-axis shows the goose number, and the Y-axis shows the time the goose spent on the weighing system. The standard deviations for individual geese are also indicated in the graph. It is evident from the graph that goose number 6 spent the least time on the weighing system. This goose spent an average of 562 s with a standard deviation of 172 s, and its weight gain was 0.401 kg. Although her frequency on the scale was the lowest, her weight gain did not correspond. The lowest value of weight gain was for goose number 7, with a value of 0.234 kg; its average time spent on the scale was 942 s, and its standard deviation was 264 s. The highest weight gain was for goose number 2, which was 1.011 kg; this goose had an average time of 1102 s and a standard deviation of 278 s. Thus, the low value of RFID registrations for goose number 6 could be due to a faulty RFID tag or less need to spend time on the weighing system without the intention of feeding. The most time was spent on the weighing system by goose number 9, which spent an average of 1995 s. The weight gain of this goose was 0.399 kg, which was a relatively lower value compared to other geese. The goose could spend time on the scale even without her needing to feed. Goose number 9 shows a significant standard deviation value (σ = 1345 s). This was due to significant variations in the time spent on the weighing system during the week. The shortest time a goose spent on the weighing system was 612 s on 26/06/2021. The longest time a goose spent here was 4368 s on 27/06/2021. The smallest standard deviation was for goose number 16 (σ = 105 s). The shortest time spent was 573 s on 26/06/2021. The longest time a goose spent was 864 s on 21/06/2021. The average time goose number 16 spent on the weighing system was 693 s.

Figure 8. The average daily time spent on the weighing system during the week from 21/06/2021 to 27/6/2021.

Figure 8. The average daily time spent on the weighing system during the week from 21/06/2021 to 27/6/2021.

The second chosen week for comparing the average time geese spent on the weighing system was the period from 29/07/2021 to 04/08/2021, which was approximately the sixth week of monitoring. This period was chosen because the RFID tag of one of the geese (Goose number 6) broke on 05/08/2021. From Figure 10, it is evident that goose number 13 spent the most time on the weighing system, averaging 1337 s with a standard deviation of 347 s. The weight gain of goose number 13 was 0.465 kg. The lowest was 729 s, and its standard deviation was 484 s. The highest weight gain was for goose number 9, 0.801 kg; this goose had an average time of 1056 s and a standard deviation of 220 s. Goose number 21 spent the least time here. This goose spent an average of 513 s on the weighing system, and its weight gain was 0.677 kg. The graph shows that the lowest standard deviation was for goose number 7, with a value of σ = 1225 s. The average time spent on the weighing system was 1227 s. The shortest duration a goose spent here was 480 s on 01/08/2021. The longest time was 4194 s on 03/08/2021. The smallest standard deviation was for goose number 10 (σ = 61 s). The shortest time spent on the weighing system was 483 s on 04/08/2021. The longest time a goose spent on the weighing system was 660 s on 29/07/2021. The average time was 574 s.

Figure 9. Average daily time spent on the weighing system in the week from 29/07/2021 to 04/08/2021.

Figure 9. Average daily time spent on the weighing system in the week from 29/07/2021 to 04/08/2021.

In the week from 29/07/2021 to 04/08/2021, there was 19.99% less time spent on the weighing system compared to the week from 21/06/2021 to 27/06/2021. The difference in frequency was determined by comparing the total time spent by all geese in the first week and the total time spent in the week at the end of the monitoring period. The total time in the week from 21/06/2021 to 27/06/2021 was 154 668 s. The total time in the week from 29/07/2021 to 04/08/2021 was 123 744 s. This difference could be attributed to the fact that the geese no longer needed to be fed as they were at the beginning of the breeding, corresponding to the observation that the weight of geese in this period was not increasing as rapidly as up to the ninth week of breeding (Table 1). Furthermore, it was an open breeding, allowing the geese to freely graze on available vegetation.

3.2. Comparison of video duration and RFID registrations

Figure 11 shows a comparison of how RFID registrations correspond to actual goose presence on the weighing system. Based on these comparisons, values for sensitivity, specificity, accuracy, and precision were calculated. The occurrences detected from the video recording are indicated in red in the graph, while the RFID registrations are indicated in black. For illustration, a time interval of 1 hour from 10:30:00 to 11:30:00 on 13/07/2021 was chosen for geese 3, 5, 10, 9, 13, and 16. In the video evaluation, only instances where the goose stood with both feet on the weighing board were considered positive results. This caused falsely positive results in RFID registration, for example, when the goose stood on the weighing board with only one foot. This case occurred, for example, with goose number 10, where it is evident that the goose left the weighing chamber at 10:48:44 and returned at 10:48:57. Nevertheless, the RFID antenna continued to register her absence for 13 s. The opposite case occurred with goose number 5, who, according to the video recording, arrived at the weighing board at 10:37:27 and left at 10:38:07. According to RFID registrations, the goose arrived at the weighing board at 10:37:28 and left at 10:37:46. It is evident that the goose was on the weighing board; however, the RFID antenna did not detect it. This case could have occurred due to shading, manufacturing quality, or incorrect placement of the RFID tag.

Figure 10. Comparison of correlation between RFID registrations and actual goose presence on the weighing system for geese 3, 5, 9, 10, 13, and 16.

Figure 10. Comparison of correlation between RFID registrations and actual goose presence on the weighing system for geese 3, 5, 9, 10, 13, and 16.

Figure 12 provides a detailed comparison of RFID registrations and actual occurrences from the video recording of goose number 9. It is evident from the graph that, although the goose left the weighing system twice, the RFID antenna continued to detect its RFID tag. The RFID antenna detected the tag because the goose was standing at the edge of the weighing chamber. However, the recorded weight results using the software after reading the RFID tag did not correspond to reality. This incorrect result can subsequently be eliminated by removing weight values that are not realistic for the specific age of the goose.

Figure 11. Detail comparison of correlation between RFID registrations and actual goose presence on the weighing system for goose number 9.

Figure 11. Detail comparison of correlation between RFID registrations and actual goose presence on the weighing system for goose number 9.

Figure 13 shows linear regression for the dependence of RFID registrations on the time the geese spent on the weighing system. The time the geese spent on the weighing system is deduced from the video recording. The values were obtained from the monitored period from 9:00:00 to 17:00:00 on 13/07/2021. The total number of RFID registrations was 1 110. The coefficient of determination for all geese was R² = 0.9813 with the regression equation of $y=0.3272\mathrm{x}$.

Figure 12. The linear regression for the dependence of the number of RFID registrations on the time the geese spent on the weighing system with the coefficient of determination of R² = 0.9813 and the regression equation of $y=0.3272x$.

Figure 12. The linear regression for the dependence of the number of RFID registrations on the time the geese spent on the weighing system with the coefficient of determination of R² = 0.9813 and the regression equation of $y=0.3272x$.

4. Discussion

5. Conclusions

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