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Crop and Animal Sensors for Agriculture 5.0
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Crop and Animal Sensors for Agriculture 5.0

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 23982

Special Issue Editor

Dr. Suresh Neethirajan

E-Mail Website
Guest Editor
Department of Animal Science & Aquaculture, Faculty of Agriculture & Computer Science, Dalhousie University, 6050 University Ave, Halifax, NS B3H 1W5, Canada
Interests: digital agriculture; artificial intelligence; big data analytics; animal-computer interaction; sensors & bio-instrumentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensors and sensing platforms are proven innovative tools that can break the bottlenecks in the agricultural value chain. From crops-focused precision agriculture, to farm animal and fisheries focused precision livestock farming, sensors have been playing a significant role in harnessing the power to drive food security and enhance production and welfare. As we are entering into the era of Agriculture 5.0 with the digitalization focus, there are still gaps in the usage of sensing technologies in measuring various factors, parameters, signals, and processes concerning crop and animal production. Future farming systems call for sensor fusion platforms; novel wearable sensors; digital twins; mechanistic models from sensors; visual animal biometrics; crop and animal biosensors; food safety biosensors; food processing biosensors; block chain tools; and many others. I invite all researchers from a multidisciplinary background to submit research papers and critical and insightful review articles for this Special Issue on “Crop and Animal Sensors for Agriculture 5.0”. I look forward to reading your contributions.

Dr. Suresh Neethirajan
Guest Editor

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Keywords

  • sensors
  • wearables
  • precision agriculture
  • precision livestock farming
  • digitalization
  • agriculture sensors

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Published Papers (5 papers)

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Research

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18 pages, 10683 KiB  
Article
Machine Learning Methods and Visual Observations to Categorize Behavior of Grazing Cattle Using Accelerometer Signals
by Ira Lloyd Parsons, Brandi B. Karisch, Amanda E. Stone, Stephen L. Webb, Durham A. Norman and Garrett M. Street
Sensors 2024, 24(10), 3171; https://doi.org/10.3390/s24103171 - 16 May 2024
Viewed by 1134
Abstract
Accelerometers worn by animals produce distinct behavioral signatures, which can be classified accurately using machine learning methods such as random forest decision trees. The objective of this study was to identify accelerometer signal separation among parsimonious behaviors. We achieved this objective by (1) [...] Read more.
Accelerometers worn by animals produce distinct behavioral signatures, which can be classified accurately using machine learning methods such as random forest decision trees. The objective of this study was to identify accelerometer signal separation among parsimonious behaviors. We achieved this objective by (1) describing functional differences in accelerometer signals among discrete behaviors, (2) identifying the optimal window size for signal pre-processing, and (3) demonstrating the number of observations required to achieve the desired level of model accuracy,. Crossbred steers (Bos taurus indicus; n = 10) were fitted with GPS collars containing a video camera and tri-axial accelerometers (read-rate = 40 Hz). Distinct behaviors from accelerometer signals, particularly for grazing, were apparent because of the head-down posture. Increasing the smoothing window size to 10 s improved classification accuracy (p < 0.05), but reducing the number of observations below 50% resulted in a decrease in accuracy for all behaviors (p < 0.05). In-pasture observation increased accuracy and precision (0.05 and 0.08 percent, respectively) compared with animal-borne collar video observations. Full article
(This article belongs to the Special Issue Crop and Animal Sensors for Agriculture 5.0)
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16 pages, 5230 KiB  
Article
Evaluation of a Respiration Rate Sensor for Recording Tidal Volume in Calves under Field Conditions
by Lena Dißmann, Petra Reinhold, Hans-Jürgen Smith, Thomas Amon, Alisa Sergeeva and Gundula Hoffmann
Sensors 2023, 23(10), 4683; https://doi.org/10.3390/s23104683 - 12 May 2023
Cited by 2 | Viewed by 2883
Abstract
In the assessment of pulmonary function in health and disease, both respiration rate (RR) and tidal volume (Vt) are fundamental parameters of spontaneous breathing. The aim of this study was to evaluate whether an RR sensor, which was previously developed for cattle, is [...] Read more.
In the assessment of pulmonary function in health and disease, both respiration rate (RR) and tidal volume (Vt) are fundamental parameters of spontaneous breathing. The aim of this study was to evaluate whether an RR sensor, which was previously developed for cattle, is suitable for additional measurements of Vt in calves. This new method would offer the opportunity to measure Vt continuously in freely moving animals. To measure Vt noninvasively, the application of a Lilly-type pneumotachograph implanted in the impulse oscillometry system (IOS) was used as the gold standard method. For this purpose, we applied both measuring devices in different orders successively, for 2 days on 10 healthy calves. However, the Vt equivalent (RR sensor) could not be converted into a true volume in mL or L. For a reliable recording of the Vt equivalent, a technical revision of the RR sensor excluding artifacts is required. In conclusion, converting the pressure signal of the RR sensor into a flow equivalent, and subsequently into a volume equivalent, by a comprehensive analysis, provides the basis for further improvement of the measuring system. Full article
(This article belongs to the Special Issue Crop and Animal Sensors for Agriculture 5.0)
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Review

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24 pages, 8440 KiB  
Review
Computer-Vision-Based Sensing Technologies for Livestock Body Dimension Measurement: A Survey
by Weihong Ma, Yi Sun, Xiangyu Qi, Xianglong Xue, Kaixuan Chang, Zhankang Xu, Mingyu Li, Rong Wang, Rui Meng and Qifeng Li
Sensors 2024, 24(5), 1504; https://doi.org/10.3390/s24051504 - 26 Feb 2024
Cited by 3 | Viewed by 2220
Abstract
Livestock’s live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant [...] Read more.
Livestock’s live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant research achievements. This paper presents a comprehensive review of the recent advancements in livestock live body dimension measurement, emphasizing the crucial role of computer-vision-based sensors. The discussion covers three main aspects: sensing data acquisition, sensing data processing, and sensing data analysis. The common techniques and measurement procedures in, and the current research status of, live body dimension measurement are introduced, along with a comparative analysis of their respective merits and drawbacks. Livestock data acquisition is the initial phase of live body dimension measurement, where sensors are employed as data collection equipment to obtain information conducive to precise measurements. Subsequently, the acquired data undergo processing, leveraging techniques such as 3D vision technology, computer graphics, image processing, and deep learning to calculate the measurements accurately. Lastly, this paper addresses the existing challenges within the domain of livestock live body dimension measurement in the livestock industry, highlighting the potential contributions of computer-vision-based sensors. Moreover, it predicts the potential development trends in the realm of high-throughput live body dimension measurement techniques for livestock. Full article
(This article belongs to the Special Issue Crop and Animal Sensors for Agriculture 5.0)
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22 pages, 1289 KiB  
Review
Measuring Farm Animal Emotions—Sensor-Based Approaches
by Suresh Neethirajan, Inonge Reimert and Bas Kemp
Sensors 2021, 21(2), 553; https://doi.org/10.3390/s21020553 - 14 Jan 2021
Cited by 48 | Viewed by 15004
Abstract
Understanding animal emotions is a key to unlocking methods for improving animal welfare. Currently there are no ‘benchmarks’ or any scientific assessments available for measuring and quantifying the emotional responses of farm animals. Using sensors to collect biometric data as a means of [...] Read more.
Understanding animal emotions is a key to unlocking methods for improving animal welfare. Currently there are no ‘benchmarks’ or any scientific assessments available for measuring and quantifying the emotional responses of farm animals. Using sensors to collect biometric data as a means of measuring animal emotions is a topic of growing interest in agricultural technology. Here we reviewed several aspects of the use of sensor-based approaches in monitoring animal emotions, beginning with an introduction on animal emotions. Then we reviewed some of the available technological systems for analyzing animal emotions. These systems include a variety of sensors, the algorithms used to process biometric data taken from these sensors, facial expression, and sound analysis. We conclude that a single emotional expression measurement based on either the facial feature of animals or the physiological functions cannot show accurately the farm animal’s emotional changes, and hence compound expression recognition measurement is required. We propose some novel ways to combine sensor technologies through sensor fusion into efficient systems for monitoring and measuring the animals’ compound expression of emotions. Finally, we explore future perspectives in the field, including challenges and opportunities. Full article
(This article belongs to the Special Issue Crop and Animal Sensors for Agriculture 5.0)
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Graphical abstract

Other

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8 pages, 562 KiB  
Technical Note
Field Evaluation of a Rising Plate Meter to Estimate Herbage Mass in Austrian Pastures
by Jose Maria Chapa, Barbara Pichlbauer, Martin Bobal, Christian Guse, Marc Drillich and Michael Iwersen
Sensors 2023, 23(17), 7477; https://doi.org/10.3390/s23177477 - 28 Aug 2023
Cited by 3 | Viewed by 1253
Abstract
Pasture management is an important topic for dairy farms with grazing systems. Herbage mass (HM) is a key measure, and estimations of HM content in pastures allow for informed decisions in pasture management. A common method of estimating the HM content in pastures [...] Read more.
Pasture management is an important topic for dairy farms with grazing systems. Herbage mass (HM) is a key measure, and estimations of HM content in pastures allow for informed decisions in pasture management. A common method of estimating the HM content in pastures requires manually collected grass samples, which are subjected to laboratory analysis to determine the dry matter (DM) content. However, in recent years, new methods have emerged that generate digital data and aim to expedite, facilitate and improve the measurement of HM. This study aimed to evaluate the accuracy of a rising plate meter (RPM) tool in a practical setting to estimate HM in Austrian pastures. With this study, we also attempted to answer whether the tool is ready for use by farmers with its default settings. This study was conducted on the teaching and research farm of the University of Veterinary Medicine in Vienna, Austria. Data were collected from May to October 2021 in five different pastures. To evaluate the accuracy of the RPM tool, grass samples were collected and dried in an oven to extract their DM and calculate the HM. The HM obtained from the grass samples was used as the gold standard for this study. In total, 3796 RPM measurements and 203 grass samples yielding 49 measurement points were used for the evaluation of the RPM tool. Despite the differences in pasture composition, the averaged HM from the RPM tool showed a strong correlation with the gold standard (R2 = 0.73, rp = 0.86, RMSE = 517.86, CV = 33.67%). However, the results may not be good enough to justify the use of the tool, because simulations in economic studies suggest that the error of prediction should be lower than 15%. Furthermore, in some pastures, the RPM obtained poor results, indicating an additional need for pasture-specific calibrations, which complicates the use of the RPM tool. Full article
(This article belongs to the Special Issue Crop and Animal Sensors for Agriculture 5.0)
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