Cultivating a sustainable future in the artificial intelligence era: A comprehensive assessment of greenhouse gas emissions and removals in agriculture.

Agriculture is a leading sector in international initiatives to mitigate climate change and promote sustainability. This article exhaustively examines the removals and emissions of greenhouse gases (GHGs) in the agriculture industry. It also investigates an extensive range of GHG sources, including rice cultivation, enteric fermentation in livestock, and synthetic fertilisers and manure management. This research reveals the complex array of obstacles that are faced in the pursuit of reducing emissions and also investigates novel approaches to tackling them. This encompasses the implementation of monitoring systems powered by artificial intelligence, which have the capacity to fundamentally transform initiatives aimed at reducing emissions. Carbon capture technologies, another area investigated in this study, exhibit potential in further reducing GHGs. Sophisticated technologies, such as precision agriculture and the integration of renewable energy sources, can concurrently mitigate emissions and augment agricultural output. Conservation agriculture and agroforestry, among other sustainable agricultural practices, have the potential to facilitate emission reduction and enhance environmental stewardship. The paper emphasises the significance of financial incentives and policy frameworks that are conducive to the adoption of sustainable technologies and practices. This exhaustive evaluation provides a strategic plan for the agriculture industry to become more environmentally conscious and sustainable. Agriculture can significantly contribute to climate change mitigation and the promotion of a sustainable future by adopting a comprehensive approach that incorporates policy changes, technological advancements, and technological innovations.

Weed Detection from Unmanned Aerial Vehicle Imagery Using Deep Learning-A Comparison between High-End and Low-Cost Multispectral Sensors.

In order to meet the increasing demand for crops under challenging climate conditions, efficient and sustainable cultivation strategies are becoming essential in agriculture. Targeted herbicide use reduces environmental pollution and effectively controls weeds as a major cause of yield reduction. The key requirement is a reliable weed detection system that is accessible to a wide range of end users. This research paper introduces a self-built, low-cost, multispectral camera system and evaluates it against the high-end MicaSense Altum system. Pixel-based weed and crop classification was performed on UAV datasets collected with both sensors in maize using a U-Net. The training and testing data were generated via an index-based thresholding approach followed by annotation. As a result, the F1-score for the weed class reached 82% on the Altum system and 76% on the low-cost system, with recall values of 75% and 68%, respectively. Misclassifications occurred on the low-cost system images for small weeds and overlaps, with minor oversegmentation. However, with a precision of 90%, the results show great potential for application in automated weed control. The proposed system thereby enables sustainable precision farming for the general public. In future research, its spectral properties, as well as its use on different crops with real-time on-board processing, should be further investigated.

Delivery of Nematicides Using TMGMV-Derived Spherical Nanoparticles.

Spherical nanoparticles (SNPs) from tobacco mild green mosaic virus (TMGMV) were developed and characterized, and their application for agrochemical delivery was demonstrated. Specifically, we set out to develop a platform for pesticide delivery targeting nematodes in the rhizosphere. SNPs were obtained by thermal shape-switching of the TMGMV. We demonstrated that cargo can be loaded into the SNPs during thermal shape-switching, enabling the one-pot synthesis of functionalized nanocarriers. Cyanine 5 and ivermectin were encapsulated into SNPs to achieve 10% mass loading. SNPs demonstrated good mobility and soil retention slightly higher than that of TMGMV rods. Ivermectin delivery to Caenorhabditis elegans using SNPs was determined after passing the formulations through soil. Using a gel burrowing assay, we demonstrate the potent efficacy of SNP-delivered ivermectin against nematodes. Like many pesticides, free ivermectin is adsorbed in the soil and did not show efficacy. The SNP nanotechnology offers good soil mobility and a platform technology for pesticide delivery to the rhizosphere.

Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review.

Digital Twins serve as virtual counterparts, replicating the characteristics and functionalities of tangible objects, processes, or systems within the digital space, leveraging their capability to simulate and forecast real-world behavior. They have found valuable applications in smart farming, facilitating a comprehensive virtual replica of a farm that encompasses vital aspects such as crop cultivation, soil composition, and prevailing weather conditions. By amalgamating data from diverse sources, including soil, plants condition, environmental sensor networks, meteorological predictions, and high-resolution UAV and Satellite imagery, farmers gain access to dynamic and up-to-date visualization of their agricultural domains empowering them to make well-informed and timely choices concerning critical aspects like efficient irrigation plans, optimal fertilization methods, and effective pest management strategies, enhancing overall farm productivity and sustainability. This research paper aims to present a comprehensive overview of the contemporary state of research on digital twins in smart farming, including crop modelling, precision agriculture, and associated technologies, while exploring their potential applications and their impact on agricultural practices, addressing the challenges and limitations such as data privacy concerns, the need for high-quality data for accurate simulations and predictions, and the complexity of integrating multiple data sources. Lastly, the paper explores the prospects of digital twins in agriculture, highlighting potential avenues for future research and advancement in this domain.

Effects of intake-based weaning and forage type on feeding behavior and growth of dairy calves fed by automated feeders.

The objective of this study was to assess the effects of intake-based weaning methods and forage type on feeding behavior and growth of dairy calves. Holstein dairy calves (n = 108), housed in 12 groups of 9, were randomly assigned to 1 of 3 weaning treatments: milk reduction based on age (wean-by-age), individual dry matter intake (DMI; wean-by-intake), or a combination of individual DMI and age (wean-by-combination). Groups of calves were alternately assigned to 1 of 2 forage treatments: grass hay or a silage-based total mix ration (TMR; n = 6 groups per treatment). Until 30 d of age, all calves were offered 12 L/d of whole milk. Starting on d 31, milk was gradually reduced by 25% of the individual's average milk intake. For wean-by-age calves (n = 31), the milk allowance remained stable until d 62 when milk was again reduced gradually until weaning at d 70. For wean-by-intake calves (n = 35), milk allowance was reduced by a further 25% once calves consumed on average 200, 600, and (finally) 1,150 g of dry matter (DM) per day of calf starter and forage. For wean-by-combination calves (n = 35), milk intake remained stable until calves consumed on average 200 g of DM/d, at which point milk was reduced linearly until weaning at d 70. If calves failed to reach DMI targets by d 62 (n = 10), milk was then reduced gradually until weaning at d 70. Of the 35 wean-by-intake calves, 27 met all 3 DMI targets (successful-intake), and 33 of the 35 calves in the wean-by-combination treatment met the 200 g of DM/d target (successful-combination). Successful-intake and successful-combination calves had greater final body weight (BW) at 12 wk of age than wean-by-age calves (123.7 vs. 122.3 vs. 117.7 ± 3.1 kg, respectively). During weaning, successful-intake calves ate more starter and consumed less milk than successful-combination and wean-by-age calves (starter: 1.19 vs. 0.89 vs. 0.49 ± 0.07 kg of DM/d, respectively; milk: 2.7 vs. 4.2 vs. 5.9 ± 0.17 L/d, respectively). After weaning, successful-combination and successful-intake calves consumed similar amounts of starter; however, wean-by-age calves continued to consume less starter (2.85 vs. 2.78 vs. 2.44 ± 0.10 kg of DM/d, respectively). During weaning, hay and TMR calves ate similar amounts of forage, but hay calves consumed more starter (0.96 vs. 0.75 ± 0.07 kg of DM/d, respectively). After weaning, hay calves continued to consume more starter (2.88 vs. 2.50 ± 0.10 kg of DM/d, respectively), whereas TMR calves consumed more forage (0.33 vs. 0.15 ± 0.02 kg of DM/d, respectively). Hay calves had greater final BW at 84 d compared with TMR calves (124.0 vs. 119.0 ± 1.6 kg, respectively). These results show that the inclusion of a DMI target can improve starter intake and BW for calves that successfully wean, and that forage type can influence the transition onto solid feed. We also found that approximately 10% of calves failed to consume even 200 g of DM/d by 9 wk of age; more research is needed to better understand why some calves struggle to transition onto solid feed.

Digital Twinning of Hydroponic Grow Beds in Intelligent Aquaponic Systems.

The use of automation, Internet-of-Things (IoT), and smart technologies is being rapidly introduced into the development of agriculture. Technologies such as sensing, remote monitoring, and predictive tools have been used with the purpose of enhancing agriculture processes, aquaponics among them, and improving the quality of the products. Digital twinning enables the testing and implementing of improvements in the physical component through the implementation of computational tools in a 'twin' virtual environment. This paper presents a framework for the development of a digital twin for an aquaponic system. This framework is validated by developing a digital twin for the grow beds of an aquaponics system for real-time monitoring parameters, namely pH, electroconductivity, water temperature, relative humidity, air temperature, and light intensity, and supports the use of artificial intelligent techniques to, for example, predict the growth rate and fresh weight of the growing crops. The digital twin presented is based on IoT technology, databases, a centralized control of the system, and a virtual interface that allows users to have feedback control of the system while visualizing the state of the aquaponic system in real time.

In-Field Automatic Identification of Pomegranates Using a Farmer Robot.

Ground vehicles equipped with vision-based perception systems can provide a rich source of information for precision agriculture tasks in orchards, including fruit detection and counting, phenotyping, plant growth and health monitoring. This paper presents a semi-supervised deep learning framework for automatic pomegranate detection using a farmer robot equipped with a consumer-grade camera. In contrast to standard deep-learning methods that require time-consuming and labor-intensive image labeling, the proposed system relies on a novel multi-stage transfer learning approach, whereby a pre-trained network is fine-tuned for the target task using images of fruits in controlled conditions, and then it is progressively extended to more complex scenarios towards accurate and efficient segmentation of field images. Results of experimental tests, performed in a commercial pomegranate orchard in southern Italy, are presented using the DeepLabv3+ (Resnet18) architecture, and they are compared with those that were obtained based on conventional manual image annotation. The proposed framework allows for accurate segmentation results, achieving an F1-score of 86.42% and IoU of 97.94%, while relieving the burden of manual labeling.

Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions.

The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the use and protection of soils, already play important roles in securing our planet for future generations and will become irreplaceable in the future. Technical solutions must encompass the entire agricultural and forestry value chain. The process of digital transformation is supported by cyber-physical systems enabled by advances in ML, the availability of big data and increasing computing power. For certain tasks, algorithms today achieve performances that exceed human levels. The challenge is to use multimodal information fusion, i.e., to integrate data from different sources (sensor data, images, *omics), and explain to an expert why a certain result was achieved. However, ML models often react to even small changes, and disturbances can have dramatic effects on their results. Therefore, the use of AI in areas that matter to human life (agriculture, forestry, climate, health, etc.) has led to an increased need for trustworthy AI with two main components: explainability and robustness. One step toward making AI more robust is to leverage expert knowledge. For example, a farmer/forester in the loop can often bring in experience and conceptual understanding to the AI pipeline-no AI can do this. Consequently, human-centered AI (HCAI) is a combination of "artificial intelligence" and "natural intelligence" to empower, amplify, and augment human performance, rather than replace people. To achieve practical success of HCAI in agriculture and forestry, this article identifies three important frontier research areas: (1) intelligent information fusion; (2) robotics and embodied intelligence; and (3) augmentation, explanation, and verification for trusted decision support. This goal will also require an agile, human-centered design approach for three generations (G). G1: Enabling easily realizable applications through immediate deployment of existing technology. G2: Medium-term modification of existing technology. G3: Advanced adaptation and evolution beyond state-of-the-art.

Effect of Tractor Speed and Spray Application Volume on Severity of Scab and Fruit Weight at Different Heights in the Canopy of Tall Pecan Trees.

Scab (caused by Venturia effusa) is the most important yield-limiting disease of pecan in the southeastern USA. On susceptible cultivars, the disease is managed using fungicides, but spray coverage is an issue in tall trees. In four experiments, we used an air-blast sprayer to compare scab severity on fruit at 5.0 to 15.0 m height in trees receiving the same dose of fungicide at 468, 935, and 1,871 liter/ha at 2.4 and 3.2 km/h (in two additional experiments fungicides were applied at 4.0 km/h at 470 liter/ha, 4.0 km/h at 940 liter/ha and 4.0 km/h at 1,100 liter/ha). An air-blast sprayer was used for the applications, which included typical recommended active ingredients (a.i.). Nozzles were selected to provide similar proportions of spray to the upper and lower canopy. The treatments (or subsets thereof) were repeated in 2015 to 2017 on cv. Schley and in 2017, 2019, and 2020 on cv. Desirable. All treatments reduced scab compared with the control. Overall, there was no consistent difference among the treatments for severity of scab on foliage, immature fruit, or mature fruit at any height in the canopy up to 15.0 m (maximum height sampled). Fungicide applied at 2.4 or 3.2 km/h at 470 liter/ha was as effective at reducing disease as were the higher volumes (sometimes more so). The scab epidemic severity affected control efficacy. Estimated cost and water savings based on faster speed and lower volume were considerable. These preliminary observations indicate no single volume or speed was consistently superior to control scab; this suggests that, in most seasons, low volumes (higher concentration of a.i.) may be similarly efficacious as high volumes (lower concentration of a.i.) for controlling scab in tall pecan trees and offer greater resource use efficiency.

Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method.

The research presented in this paper is based on the hypothesis that the machine learning approach improves the accuracy of soil properties prediction. The correlations obtained in this research are important for understanding the overall strategy for soil properties prediction using optical spectroscopy sensors. Several research results have been stated and investigated. A comparison is made between six commonly used techniques: Random Forest, Decision Tree, Naïve Bayes, Support Vector Machine, Least-Square Support Vector Machine and Artificial Neural Network, showing that the best prediction accuracy cannot always be achieved by the most common and complicated method. The influence of the chosen category for nutrient characterization was investigated, indicating better prediction when a multi-component strategy was used. In contrast, the prediction of single-component soil properties was less accurate. In addition, the influence of category levels was not as significant as expected when choosing between 3-level, 5-level or 13-level nutrient characterization for some nutrients, which can be used for a more precise nutrient characterization strategy. A comparative analysis was performed between soil from a local farm with similar texture and soils collected from different locations in Slovenia, which gave a better prediction for a local farm. Finally, the influence of principal component analysis was validated using 5, 10, 20 and 50 first principal components, indicating the better performance of machine learning when using the 50 principal components.

Determination of Nutrients in Liquid Manures and Biogas Digestates by Portable Energy-Dispersive X-ray Fluorescence Spectrometry.

Knowing the exact nutrient composition of organic fertilizers is a prerequisite for their appropriate application to improve yield and to avoid environmental pollution by over-fertilization. Traditional standard chemical analysis is cost and time-consuming and thus it is unsuitable for a rapid analysis before manure application. As a possible alternative, a handheld X-ray fluorescence (XRF) spectrometer was tested to enable a fast, simultaneous, and on-site analysis of several elements. A set of 62 liquid pig and cattle manures as well as biogas digestates were collected, intensively homogenized and analysed for the macro plant nutrients phosphorus, potassium, magnesium, calcium, and sulphur as well as the micro nutrients manganese, iron, copper, and zinc using the standard lab procedure. The effect of four different sample preparation steps (original, dried, filtered, and dried filter residues) on XRF measurement accuracy was examined. Therefore, XRF results were correlated with values of the reference analysis. The best R2s for each element ranged from 0.64 to 0.92. Comparing the four preparation steps, XRF results for dried samples showed good correlations (0.64 and 0.86) for all elements. XRF measurements using dried filter residues showed also good correlations with R2s between 0.65 and 0.91 except for P, Mg, and Ca. In contrast, correlation analysis for liquid samples (original and filtered) resulted in lower R2s from 0.02 to 0.68, except for K (0.83 and 0.87, respectively). Based on these results, it can be concluded that handheld XRF is a promising measuring system for element analysis in manures and digestates.

Reliability Analysis of Wireless Sensor Network for Smart Farming Applications.

Wireless Sensor Networks are subjected to some design constraints (e.g., processing capability, storage memory, energy consumption, fixed deployment, etc.) and to outdoor harsh conditions that deeply affect the network reliability. The aim of this work is to provide a deeper understanding about the way redundancy and node deployment affect the network reliability. In more detail, the paper analyzes the design and implementation of a wireless sensor network for low-power and low-cost applications and calculates its reliability considering the real environmental conditions and the real arrangement of the nodes deployed in the field. The reliability of the system has been evaluated by looking for both hardware failures and communication errors. A reliability prediction based on different handbooks has been carried out to estimate the failure rate of the nodes self-designed and self-developed to be used under harsh environments. Then, using the Fault Tree Analysis the real deployment of the nodes is taken into account considering the Wi-Fi coverage area and the possible communication link between nearby nodes. The findings show how different node arrangements provide significantly different reliability. The positioning is therefore essential in order to obtain maximum performance from a Wireless sensor network.

Reflectance Estimation from Multispectral Linescan Acquisitions under Varying Illumination-Application to Outdoor Weed Identification.

To reduce the amount of herbicides used to eradicate weeds and ensure crop yields, precision spraying can effectively detect and locate weeds in the field thanks to imaging systems. Because weeds are visually similar to crops, color information is not sufficient for effectively detecting them. Multispectral cameras provide radiance images with a high spectral resolution, thus the ability to investigate vegetated surfaces in several narrow spectral bands. Spectral reflectance has to be estimated in order to make weed detection robust against illumination variation. However, this is a challenge when the image is assembled from successive frames that are acquired under varying illumination conditions. In this study, we present an original image formation model that considers illumination variation during radiance image acquisition with a linescan camera. From this model, we deduce a new reflectance estimation method that takes illumination at the frame level into account. We experimentally show that our method is more robust against illumination variation than state-of-the-art methods. We also show that the reflectance features based on our method are more discriminant for outdoor weed detection and identification.

Advances in Unmanned Aerial System Remote Sensing for Precision Viticulture.

New technologies for management, monitoring, and control of spatio-temporal crop variability in precision viticulture scenarios are numerous. Remote sensing relies on sensors able to provide useful data for the improvement of management efficiency and the optimization of inputs. unmanned aerial systems (UASs) are the newest and most versatile tools, characterized by high precision and accuracy, flexibility, and low operating costs. The work aims at providing a complete overview of the application of UASs in precision viticulture, focusing on the different application purposes, the applied equipment, the potential of technologies combined with UASs for identifying vineyards' variability. The review discusses the potential of UASs in viticulture by distinguishing five areas of application: rows segmentation and crop features detection techniques; vineyard variability monitoring; estimation of row area and volume; disease detection; vigor and prescription maps creation. Technological innovation and low purchase costs make UASs the core tools for decision support in the customary use by winegrowers. The ability of the systems to respond to the current demands for the acquisition of digital technologies in agricultural fields makes UASs a candidate to play an increasingly important role in future scenarios of viticulture application.

Real-Time Electrical Resistivity Measurement and Mapping Platform of the Soils with an Autonomous Robot for Precision Farming Applications.

Soil electrical resistivity (ER) is an important indicator to indirectly determine soil physical and chemical properties such as moisture, salinity, porosity, organic matter level, bulk density, and soil texture. In this study, real-time ER measurement system has been developed with the help of an autonomous robot. The aim of this study is to provide rapid measurement of the ER in large areas using the Wenner four-probe measurement method for precision farming applications. The ER measurement platform consists of the Wenner probes, a y-axis shifter driven by a DC motor through a gear reducer, all installed on a steel-frame that mount to an autonomous robot. An embedded industrial computer and differential global positioning system (DGPS) were used to assist in real-time measuring, recording, mapping, and displaying the ER and the robot position during the field operation. The data acquisition software was codded in Microsoft Visual Basic.NET. Field experiments were carried out in a 1.2 ha farmland soil. ER and DGPS values were stored in Microsoft SQL Server 2005 database, an ordinary Kriging interpolation technique by ArcGIS was used and the average ER values were mapped for the soil depth between 0 and 50 cm. As a result, ER values were observed to be between 30.757 and 70.732 ohm-m. In conclusion, the experimental results showed that the designed system works quite well in the field and the ER measurement platform is a practical tool for providing real-time soil ER measurements.

A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution.

The proper spatial distribution of chickens is an indication of a healthy flock. Routine inspections of broiler chicken floor distribution are done manually in commercial grow-out houses every day, which is labor intensive and time consuming. This task requires an efficient and automatic system that can monitor the chicken's floor distributions. In the current study, a machine vision-based method was developed and tested in an experimental broiler house. For the new method to recognize bird distribution in the images, the pen floor was virtually defined/divided into drinking, feeding, and rest/exercise zones. As broiler chickens grew, the images collected each day were analyzed separately to avoid biases caused by changes of body weight/size over time. About 7000 chicken areas/profiles were extracted from images collected from 18 to 35 days of age to build a BP neural network model for floor distribution analysis, and another 200 images were used to validate the model. The results showed that the identification accuracies of bird distribution in the drinking and feeding zones were 0.9419 and 0.9544, respectively. The correlation coefficient (R), mean square error (MSE), and mean absolute error (MAE) of the BP model were 0.996, 0.038, and 0.178, respectively, in our analysis of broiler distribution. Missed detections were mainly caused by interference with the equipment (e.g., the feeder hanging chain and water line); studies are ongoing to address these issues. This study provides the basis for devising a real-time evaluation tool to detect broiler chicken floor distribution and behavior in commercial facilities.

AgriLogger: A New Wireless Sensor for Monitoring Agrometeorological Data in Areas Lacking Communication Networks.

The use of wireless technologies in the field of agriculture, or so-called smart or precision agriculture, is considered as one of the main efforts applied nowadays to multiply the food production on earth. However, wireless sensor network (WSN) technology is still at its early development stage and its application in agriculture and food industry is still rare due to the lack of farmers' awareness and outreach about the matter. This paper presents a new agro-sensor named AgriLogger with an aim to collect, store for long periods and transmit agrometeorological data represented by temperature and relative humidity in remote areas hard to reach and not served by telecommunication networks. The sensor exhibits long battery life, in the order of 10 years, thanks to low consumption technologies and to hardware sleep/wake up approach. It can be remotely placed on preselected sites through a customized drone. This latter, equipped with a dedicated payload, can then return on the sites where sensors have been placed, and, while hovering, wakes up the single devices and uploads their collected data through local wireless network. Field tests have demonstrated that the sensor, after being placed manually in two different positions, inside and outside a vineyard canopy, is able to collect and store successfully agrometeorological data like temperature and relative humidity. Moreover, the use of a drone potentially allows the collection of data from remote areas and, therefore, is able to provide a periodical monitoring of agro-ecological conditions.

Metrology for Agriculture and Forestry 2019.

This Special Issue is focused on recent advances in integrated monitoring and modelling technologies for agriculture and forestry. The selected contributions cover a wide range of topics, including wireless field sensing systems, satellite and UAV remote sensing, ICT and IoT applications for smart farming.

Infrared thermography reveals surface body temperature changes during proestrus and estrus reproductive phases in Gyr heifers (Bos taurus indicus).

Our aim was to evaluate the application of infrared thermography (IRT) to detect body surface temperature variation of body regions during the proestrus and estrus phases of the reproductive cycle of Gyr heifers and investigate environmental factors that could affect these measurements. Fifty-seven heifers were submitted to an ovulation synchronization protocol. This was followed by monitoring the heifers every 12 h over 60 h. Heifers were monitored for rectal and vaginal temperature using a digital thermometer. The surface temperature of the eye, vulva, and muzzle regions were monitored by IRT. Meteorological data was recorded for temperature and humidity. Observation of sexual behavior was performed to monitor estrus onset. Transrectal ultrasonography was used to identify the dominant follicle and confirm ovulation of all heifers. We observed a decrease in temperature of the rectum and vagina, as well as in the eye and vulva regions within the first 12 h after the completion of the synchronization. This period coincides with the expected proestrus phase of the estrous cycle. A progressive increase in all temperatures was noticed in the following 36 h, which coincides with the estrus phase of the reproductive cycle. The regions evaluated around the vulva and eye exhibited the highest temperature and experienced less environmental distortion than the muzzle area thermographs. Environmental factors, such as rainfall and temperature-humidity index, influenced the IRT readings altering the radiation patterns detected. In conclusion, IRT is an effective method to detect temperature variation during the proestrus and estrus phases in Gyr heifers. Furthermore, biological and environmental effects should be considered when collecting and interpreting IRT data in livestock.

Validation of the RumiWatch Converter V0.7.4.5 classification accuracy for the automatic monitoring of behavioural characteristics in dairy cows.

The objective of the present study was to validate the accuracy of algorithms, implemented in the currently available RumiWatch Converter (RWC) version V0.7.4.5 of the RumiWatch System (RWS), for the classification of behavioural characteristics from jaw and head movements which are monitored by a noseband halter comprising a pressure sensor and a triaxial accelerometer. The accurate classification of behavioural characteristics in different time resolutions is critical for the usage of the RWS for scientific and practical purposes as chewing behaviour provides essential indicators for the assessment of diet adequacy in dairy cows. To validate the RWC V0.7.4.5 classification accuracy for behavioural characteristics of rumination, eating, drinking, other activity and ruminating chews per bolus by direct observation as reference method, 14 dairy cows participated in the trial. Concordance between the consolidated 1-min and 1-h classification results was assessed. The RWC V0.7.4.5 classified only rumination and ruminating chews per bolus precisely, whereas an algorithm optimisation for the classification of eating, drinking and other activity is required. Additionally, classification results from the 1-min and 1-h time summaries were not in agreement with each other except for rumination.