REGISTRO DOI: 10.69849/revistaft/ni10202412050727
André Luiz Marra
Abstract
Growth of the global population and the consequent demand for food put pressure on the agricultural sector, especially beef cattle farming, to increase production sustainably. This paper explores the use of emerging technologies such as remote sensing, Internet of Things (IoT), and artificial intelligence (AI) to enhance productivity, improve animal welfare, and reduce environmental impact. Remote sensing uses electromagnetic waves to monitor vegetation, while RGB and multispectral sensors provide detailed data on plant health. IoT, through interconnected devices, monitors animal health and water resource management. AI enhances data analysis, enabling accurate predictions and continuous improvements in management practices. These integrated technologies promote a more sustainable and efficient cattle industry.
Keywords: Remote sensing, IoT, artificial intelligence, beef cattle farming, sustainability.
1. Introduction
Growth of the global population and the increasing demand for food have placed unprecedented pressure on the agricultural sector, especially on beef cattle farming. This sector faces the challenge of not only boosting production to meet this rising demand but also doing so sustainably. In this context, critical issues emerge, such as reducing the carbon footprint associated with livestock, which is a significant contributor to greenhouse gas emissions. Sustainable herd management is therefore a priority, requiring technological innovations that can mitigate environmental impacts while enhancing production efficiency.
Increased productivity in beef cattle farming brings benefits not only to the profitability of the agricultural business but also to the environment and society. The environment benefits because higher production per unit area reduces the need to use native vegetation areas to increase production. Moreover, increasing productivity drastically reduces greenhouse gas emissions per ton of meat produced. Society benefits in the sense that the greater and cheaper the meat production, the better access vulnerable segments of society have to this commodity.
In this context, emerging technologies such as remote sensing, the Internet of Things (IoT), and artificial intelligence (AI) emerge as powerful allies in transforming beef cattle farming. Remote sensing, for instance, uses electromagnetic waves to monitor vegetation health and optimize the use of land and water resources. This technology is crucial for precision agriculture, allowing producers to obtain detailed data on pasture conditions and herd health, providing a foundation for informed decision-making.
Both RGB and multispectral sensors play central roles in agricultural monitoring, offering valuable insights ranging from plant disease detection to soil composition analysis. While RGB sensors provide high-resolution visual images, multispectral sensors capture a broader and more detailed range of data, including near-infrared, which is essential for assessing plant health and detecting water stress. The integration of these technologies with drones and satellites further expands possibilities, providing extensive and detailed coverage of the agricultural environment.
Additionally, IoT revolutionizes resource management in livestock through interconnected devices that monitor animal health and water resource efficiency in realtime. Biometric sensors, GPS tracking, and geofencing technologies enable precise control over animal welfare and pasture space utilization, promoting sustainability and efficiency.
Artificial intelligence enhances the analytical capacity of collected data, allowing for more accurate predictions and continuous improvements in management practices. From identifying animal health patterns to genetic selection for desirable herd traits, AI becomes an indispensable tool in modern agricultural management.
The aim of this work is to explore how these technologies are being applied in beef cattle farming, highlighting their impacts on productivity increase, animal welfare improvement, and the reduction of the environmental impact of agricultural production. A comprehensive discussion of these technological innovations will illustrate how they can be integrated to foster a more sustainable and efficient sector, meeting global food needs without compromising the planet’s future.
2. Remote Sensing in Agriculture
2.1 Electromagnetic Waves
Electromagnetic waves are a fascinating form of energy that permeates the entire universe, playing a crucial role in our understanding of the natural world and in numerous technological applications. These waves, which propagate through space at the speed of light, are composed of oscillating electric and magnetic fields that move perpendicularly to each other. A notable feature of electromagnetic waves is their ability to travel through a vacuum, distinguishing them from other types of waves, such as sound waves, which require a medium for propagation.
Electromagnetic spectrum encompasses the entire range of possible wave frequencies, from low-frequency radio waves to high-energy gamma rays. This continuous spectrum is conventionally divided into distinct regions, each with specific characteristics and applications. Radio waves, with the longest wavelengths, are used for long-distance communications. Microwaves find applications in everyday technologies such as microwave ovens and satellite communications. Infrared radiation, associated with heat, is crucial in night vision technologies.
At the center of the spectrum is visible light, the only part that the human eye can directly detect. This narrow region, spanning approximately from 380 to 700 nanometers, is responsible for the entire range of colors we perceive. Each color corresponds to a specific wavelength, with red at the longer wavelength end and violet at the opposite extreme. Beyond the visible spectrum, we have ultraviolet radiation, X-rays, and gamma rays, each with progressively higher energies and applications ranging from medical sterilization to high-energy astronomy.
Source: J. Amila Yusuf Isse and Chaimae el Ghouch, “Information Theoretic Similarity Measures for Robust Image Matching,” Stockholm, Sweden, 2016.
NIR = Near Infrared; MIR = Mid Infrared; FIR = Far Infrared
Interaction of electromagnetic waves with matter is fundamental to many areas of science and technology, being particularly relevant in the study of vegetation through remote sensing. Plants have a unique spectral signature, absorbing and reflecting light differently across various parts of the spectrum. This property is exploited in vegetation indices, mathematical tools that combine measurements from different spectral bands to extract valuable information about plant health and condition.
The most important spectral regions for vegetation indices include the red band, where chlorophyll strongly absorbs light for photosynthesis, and the near-infrared (NIR), where leaves strongly reflect due to their internal structure. The contrast between red absorption and NIR reflection is the basis for the Normalized Difference Vegetation Index (NDVI), one of the most widely used indices. Other relevant regions include the shortwave infrared (SWIR), sensitive to plant water content, and the “red edge,” a transition region between red and NIR that is highly sensitive to chlorophyll content.
Beyond NDVI, other important indices include the Enhanced Vegetation Index (EVI), which seeks to reduce atmospheric influences and improve sensitivity in areas of high biomass, and the Soil-Adjusted Vegetation Index (SAVI), which minimizes the influence of soil brightness in sparsely vegetated areas. Each of these indices uses different combinations of spectral bands to highlight specific aspects of vegetation, such as density, health, or water stress.
At a molecular level, the interaction of light with plants involves several components. Chlorophyll, responsible for absorbing red and blue light for photosynthesis, is the primary pigment defining the spectral signature of plants. Carotenoids, which absorb blue and green light, contribute to the reflection of yellow and orange colors, especially visible in autumn. The cellular structure of leaves, particularly the spongy mesophyll, accounts for high near-infrared reflectance. Water content in leaves strongly influences absorption in certain infrared regions.
Understanding these interactions between electromagnetic waves and vegetation has crucial applications in areas such as precision agriculture, forest management, environmental studies, and climate change monitoring. Vegetation indices allow scientists and environmental managers to monitor plant health, detect early stress, estimate agricultural productivity, and assess changes in vegetation cover over large areas.
2.2 Sensors
RGB sensors, widely used in conventional digital cameras, are designed to capture images that resemble human vision. These sensors use three color channels – red, green, and blue – to create color images. Each pixel on the sensor is covered by a filter that allows the passage of either red, green, or blue light. This arrangement, known as a Bayer filter array, typically consists of 50% green filters, 25% red, and 25% blue, reflecting the human eye’s greater sensitivity to green light.
One of the main advantages of RGB sensors is their ability to produce highresolution images that are easily interpretable by the human eye. They are excellent at capturing visual details and textures, making them ideal for applications such as conventional aerial photography and visual mapping. Additionally, RGB sensors are relatively inexpensive and widely available, which makes them accessible for a variety of applications. However, RGB sensors also have significant limitations, especially when it comes to more detailed spectral analyses. The main drawback is the spectral overlap between color channels. The filters used in RGB sensors are not perfectly selective, allowing some light from adjacent wavelengths to pass through each filter. This overlap can result in a loss of precise spectral information, hindering more refined analyses such as plant stress detection or specific mineral identification.
On the other hand, multispectral sensors offer a more sophisticated approach to capturing spectral information. These sensors are designed to capture light in several narrow and well-defined spectral bands, usually including not only the visible spectrum but also near-infrared and, in some cases, shortwave infrared regions. The primary advantage of multispectral sensors is their ability to collect much more detailed and accurate spectral information. By using narrowband filters, these sensors minimize spectral overlap between bands, resulting in purer data for each spectral region. This allows for more sophisticated analyses, such as precise vegetation index calculations, water stress detection in crops, or pollutant identification in bodies of water.
Source: www.ultralytics.com
Multispectral sensors are particularly valuable in scientific and environmental monitoring applications. For example, in precision agriculture, they can provide detailed information about crop health, allowing for targeted interventions and resource use optimization. In forestry, they can help detect tree diseases before they become visible to the naked eye. In geological studies, they can assist in identifying different types of rocks and minerals.
However, multispectral sensors also have their disadvantages. They are generally more expensive than conventional RGB sensors and may have lower spatial resolution. Additionally, processing and interpreting multispectral data often require specialized knowledge and specific software, which can limit their accessibility for non-specialist users.
An interesting development in the field of remote sensing is the modification of commercial RGB cameras to capture information in the near-infrared. This process involves removing the infrared-cut filter (also known as a hot mirror filter) that is normally installed in digital cameras to block infrared light. By removing this filter, the sensor becomes sensitive to near-infrared light, in addition to the visible bands. This modification has gained popularity, especially for use in drones, due to its relatively low cost compared to dedicated multispectral sensors. Modified cameras can capture images in the visible and near-infrared spectrum, allowing for the calculation of simple vegetation indices such as NDVI (Normalized Difference Vegetation Index).
However, it is important to note that this approach has limitations. The spectral overlap between channels is significant, and the near-infrared sensitivity is not as precise as in a dedicated multispectral sensor. Additionally, calibrating these modified cameras can be challenging, which may affect measurement accuracy.
Quality of information obtained by RGB and multispectral sensors is strongly influenced by the overlap of wavelengths in the sensors. In RGB sensors, the significant overlap between color channels can lead to a loss of subtle spectral information. This can result in difficulties in detecting small variations in reflectance, which can be crucial for certain applications such as early detection of plant diseases. In contrast, multispectral sensors are designed to minimize this overlap. By using narrowband filters and, in some cases, technologies like diffraction gratings or prisms to separate wavelengths, these sensors can capture much more precise spectral information. This results in higher quality data, allowing for more refined and reliable analyses.
There are also hyperspectral sensors that capture even narrower and continuous bands of light, similar to multispectral cameras. The choice between RGB and multispectral sensors, therefore, depends on the specific needs of the application. For applications requiring visually appealing and high-resolution images, RGB sensors may suffice. For detailed spectral analyses and scientific applications requiring precise information on specific properties of objects or surfaces, multispectral sensors are generally the preferred choice.
2.3 Drones vs. Satellites
Acquisition of images for monitoring agricultural areas is an essential practice that utilizes both satellites and drones, each with its own technical advantages and disadvantages. Satellites are often chosen to cover vast stretches of land due to their ability to efficiently cover large areas. This wide coverage is particularly useful in studies that require a regional or even global view. One of the great attractions of satellites is the availability of historical data, exemplified by programs such as Landsat, which offers decades of essential records for temporal analyses and the assessment of changes over time. Moreover, satellites orbit the Earth at regular intervals, ensuring consistency and regularity in data collection.
However, satellites face significant limitations. Spatial resolution can be challenging, as it often is not sufficient for detailed studies in small areas. Additionally, cost can be a barrier, especially when seeking high-resolution images. Another obstacle is the dependence on weather conditions, as cloud cover can interfere with data collection, particularly affecting satellites that use optical sensors.
On the other hand, drones have gained prominence due to their ability to capture high-resolution images by flying at low altitudes, making them ideal for studies requiring a superior level of detail. They offer remarkable operational flexibility, as they can be quickly deployed and operated during specific periods, adjusting according to the immediate needs of the study. For smaller areas, using drones can be more economical compared to acquiring satellite data.
Source: blog.sensix.ag
However, drones also have their limitations. They are less efficient at covering large areas due to their limited coverage and face flight time restrictions, as their battery autonomy requires frequent recharges or replacements. Additionally, drone operation is subject to regulations that may restrict their use in certain areas, requiring special attention to local and national laws.
Both satellites and drones play crucial roles in agricultural monitoring, and the choice between them depends on the specific needs of the project in terms of scale, resolution, and budget. As technology advances, it is expected that both tools will continue to evolve, offering even more efficient and integrated solutions for environmental and agricultural monitoring.
3. IoT
Internet of Things (IoT) holds significant potential to transform pasture-based beef cattle farming, offering solutions ranging from improving operational efficiency to enhancing animal welfare and optimizing the use of natural resources. Here are some ways IoT can be applied in this activity:
3.1 Animal Health Monitoring
Biometric sensors monitor the health and well-being of animals. Among the most common are temperature sensors, which are inserted into the animal’s body (such as the ears) to measure body temperature. These sensors work through digital thermometers and transmit data via radio frequency. Another example is heart rate sensors, which use electrodes or contact devices to monitor heartbeats in real time. This data is typically transmitted via Bluetooth or Wi-Fi to a database, allowing farmers to access the animal’s health status remotely.
Tracking sensors are used to monitor the location and movement of animals within the property. GPS collars are widely used as they provide precise information about the animals’ location. The GPS system works through satellites, allowing location data to be sent to a central hub that can be accessed via the internet. Additionally, RFID (Radio Frequency Identification) sensors are used to identify and track animals as they pass specific points on the farm.
Temperature and heart rate sensors, for example, provide detailed health monitoring of animals, allowing for quick interventions in case of anomalies. However, they may have disadvantages, such as the need for frequent maintenance and replacement, as well as potential discomfort for the animals. GPS collars are highly effective for tracking in large areas, but their cost can be high, and reliance on satellite signals can be problematic in remote regions or areas with limited coverage. RFID sensors are economical and efficient for identification but have a limited range, requiring the animal to be near a reader for data to be captured.
Most modern sensors use wireless networks, such as Wi-Fi, Bluetooth, or mobile networks, to send data to centralized management systems. This allows data to be analyzed in real-time or stored for future analysis, helping producers make informed decisions about managing their livestock properties.
3.2 Water Resource Management
Water level sensors on livestock farms function as automatic devices that monitor the amount of water in reservoirs or troughs. These sensors use technologies such as ultrasound, pressure, or capacitance to measure the height or volume of water, transmitting real-time data to central control systems. The main advantage is the automation of monitoring, which reduces the need for manual inspections and ensures that animals always have access to sufficient water, essential for their well-being and productivity. These sensors allow early detection of problems like leaks or contamination, which can be quickly corrected, preventing waste or risks to animal health.
However, there are challenges, such as the need for regular maintenance to prevent failures due to dirt or corrosion, initial installation costs, and the reliance on reliable network connections for data transmission. Despite this, the benefits include better water resource management, increased operational efficiency, and contributions to environmental sustainability. In terms of benefits for beef cattle farming, these sensors can improve feed conversion rates and weight gain in animals, as constant access to clean and adequate water is crucial for digestion and metabolism in cattle. Additionally, the ability to monitor remotely allows farmers to react quickly to any issues, which can result in cost reductions and increased profitability for the business.
3.3 Virtual Fences
Geofencing is a technology that is revolutionizing the way we manage geographical spaces and their interaction with the physical environment, especially in animal and farm management. Essentially, geofencing uses GPS (Global Positioning System) technology to create virtual boundaries around a specific area. When a device, such as a collar or tracker attached to an animal, enters or exits this delineated area, an alert system is triggered, allowing those responsible to be notified in real-time.
Benefits of this technology are numerous. On one hand, it offers a more sustainable and less invasive solution compared to traditional physical fences. It allows for more efficient and constant monitoring of animals, reducing fencing maintenance costs and enabling more agile and adaptive management. A clear example of application is its use in large farms, where the manual task of monitoring the entire perimeter would be impractical. Properties that cover large pasture areas or are located in regions with difficult access benefit the most from this innovation.
Source: onlinelibrary.wiley.com
However, geofencing also presents significant challenges. The dependence on GPS technology implies potential accuracy issues in areas with limited signal coverage or adverse weather conditions. Additionally, there is the issue of device batteries, which need to be recharged periodically, and the need for initial investment in technological infrastructure and staff training to operate the system.
Technology usually uses sound alerts or mild stimuli, such as vibrations, to signal to animals that they are approaching the virtual boundary. With time and proper training, many animals learn to associate these signals with the space they should not cross, effectively functioning without the need for physical barriers. This approach not only keeps animals within the desired area but also promotes their well-being by eliminating risks associated with physical fences, such as injuries.
In the future, the prospects are promising with the continuous evolution of technology. Improvements in tracking systems and GPS are expected to bring even more precision and reliability, allowing for the application of geofencing in a wider range of contexts. Additionally, advancements in artificial intelligence promise to integrate geofencing data with other environmental and animal behavior information, providing increasingly detailed and useful insights for efficient herd management.
3.4 Automated Gates
Automatic gate activation system on beef cattle properties uses the Internet of Things (IoT) to enable remote and automated control of gates. An electric motor, controlled by a microcontroller with internet connectivity, operates the opening and closing of gates. Position sensors ensure the gate functions correctly, while the system receives commands from a central platform, which can be accessed via mobile devices or computers. Communication can be conducted through mobile networks, satellite internet, or LoRaWAN networks, depending on the available infrastructure.
4. AI
4.1 Vegetation Monitoring Using AI
Artificial intelligence (AI) combined with big data and remote sensing is deeply transforming how the quality and quantity of pastureland are managed in agriculture. This technology not only allows for precise and efficient analysis of large volumes of data but also provides significant benefits to the agricultural sector.
One of the main benefits is resource optimization. With accurate and up-to-date data, farmers can make informed decisions about when and where to graze their animals, increasing productivity and reducing waste. Additionally, this technology facilitates the early detection of issues such as soil degradation or pest infestations, allowing for swift and effective interventions.
Technology also enables more sustainable pasture management. With detailed insights, farmers can implement practices that preserve soil and vegetation health, ensuring the longevity of pastures and contributing to environmental sustainability.
NASA has been using satellite data to monitor global vegetation changes, while the FAO (Food and Agriculture Organization of the United Nations) promotes the use of these advanced technologies to improve agricultural practices. Universities like the University of California and Wageningen University in the Netherlands are conducting research to develop these applications and integrate new AI techniques for more efficient agricultural management.
With the integration of artificial intelligence (AI) and big data, forecasting pasture production becomes increasingly accurate, enabling more efficient and sustainable agricultural resource management. These technologies process large volumes of data, such as satellite imagery, vegetation indices, and weather information, to create predictive models that anticipate the availability and productivity of pastures.
Technological solutions offer direct benefits to ranchers. By forecasting forage availability in advance, producers can anticipate the purchase of hay or silage in years with predicted shortages during winter. This not only helps ensure adequate cattle feed but also allows ranchers to make these purchases when prices are lower, significantly saving on operational costs. Additionally, more precise pasture management can enhance livestock productivity and quality, resulting in higher long-term profits.
The use of machine learning, a branch of AI, is essential for identifying patterns in data that indicate pasture health. These predictive models offer farmers the ability to better plan pasture management, adjusting rotation and fertilizer use, and mitigating risks associated with adverse weather conditions.
4.2 Integration of AI with Sensors and Imaging
Previously, the use of biometric and tracking sensors in livestock was mentioned, as well as their applications. But how are the data from these sensors used? How are they transformed into information? These data are collected in large volume and variety, from biometric information to location and animal behavior data. By employing a large correlational database between sensor information and real aspects of the animals, it is possible to develop curves and algorithms that create a correlation between a specific sensor signal, or a particular pattern of sensor signals, with an animal’s physical or behavioral aspect, such as the presence of heat or a health-related issue. With the use of Big Data, these data can be stored and processed at scale, enabling complex analyses that would have been impractical otherwise.
AI plays a crucial role in extracting patterns and insights from these data. Machine learning algorithms can analyze animal behavior in real-time, detecting anomalies that may indicate health problems or stress. When integrated with imaging systems, AI can identify subtle changes in animal behaviors that would be difficult to detect visually. For example, video cameras combined with computer vision algorithms can monitor animal movement and alert producers to atypical behaviors, such as reduced activity or abnormal locomotion patterns.
At the University of Wisconsin, research projects are exploring the use of imaging algorithms to correlate animal weight with their health and well-being. Using computer vision techniques, it’s possible to estimate animal weight non-invasively by analyzing images captured in real-time. This approach promises greater accuracy and efficiency, reducing the need for frequent physical weighings, which can be stressful for the animals.
Combination of AI and Big Data in livestock not only improves animal health monitoring but also optimizes operational efficiency, offering farmers actionable data to enhance herd management. This emerging technology is set to transform livestock farming into a more sustainable and profitable sector while prioritizing animal welfare.
4.3 Reproductive Management
As explored previously, sensors implanted or attached to animals can monitor various physiological and behavioral metrics, such as motor activity, body temperature, and feeding patterns, which are crucial indicators of the reproductive cycle.
To identify estrus, activity sensors detect increases in movement in females, who often exhibit more active behaviors during this period. AI processes these data in realtime, using algorithms that learn and recognize patterns associated with estrus. These algorithms can differentiate between normal behaviors and those indicative of sexual receptivity, providing producers with precise data on the timing of estrus.
Additionally, AI analyzes historical and diverse data sources to predict the optimal time for artificial insemination. By integrating information on body temperature, which can vary slightly during the reproductive cycle, and activity patterns, AI can help define the best period for insemination, increasing success rates. This precision in determining the timing of insemination improves reproductive efficiency, maximizing productivity and economic returns.
4.4 Nutrition
Another significant contribution of AI in livestock is the formulation of diets for cattle, integrating remote sensing data with laboratory analyses of vegetation and soil, thus providing a comprehensive view of the nutrition available in pastures. While georeferencing offers information on location and extent, the precise nutritional composition of pastures is obtained through in-depth analyses.
AI optimizes diet formulation by analyzing these integrated data and suggesting precise adjustments in dietary supplements. One of the significant benefits of this technology is the ability to automatically adjust diets daily, considering variations in the prices of raw materials used in the formulation. This allows ranchers to adapt cattle nutrition efficiently and economically, ensuring the diet offered always has the best costbenefit ratio and meets pre-established performance goals for animals. This flexibility not only optimizes animal performance in terms of growth and production but also helps keep feeding costs under control.
Use of AI for these applications enables producers to make informed decisions, reducing reliance on human observations, which can be less accurate and more timeconsuming.
4.5 Genetic Selection of Animals
AI applied to the genetic improvement of beef cattle offers opportunities to optimize desirable traits in animals, such as weight gain, meat quality, disease resistance, and feed efficiency. This technology operates through a detailed process of genetic prediction, where AI models, such as neural networks and machine learning algorithms, analyze large volumes of genomic, phenotypic, and environmental data to identify complex patterns and predict the future performance of animals.
The first step involves collecting comprehensive data, including genomic information such as DNA sequencing, phenotypic data, which are observable traits like weight and meat quality, and environmental data related to breeding and feeding conditions. The analysis of genetic variability seeks to determine which genetic variants are associated with desirable traits, identifying genetic markers or SNPs (Single Nucleotide Polymorphisms).
AI predictive models process these data to predict which animals are most likely to express beneficial traits. Techniques such as regression, decision trees, and neural networks are used at this stage. These models are continuously refined and validated as new data become available to improve their accuracy and reduce prediction errors.
In genetic prediction, various factors are considered to generate precise estimates. Genetics, including lineage analysis and the presence of alleles associated with specific traits, is fundamental. Additionally, phenotype, which involves direct observations of the animals’ physical characteristics, and the environment, which includes climatic conditions, type of feed, management, and breeding practices, play essential roles. The interaction between genotype and environment is also analyzed to understand how animals’ genetics influence their phenotypic traits in different environmental contexts.
For effective implementation of this system, parameters such as high-quality genomic databases, detailed phenotypic information, robust technological infrastructure for data storage and processing, as well as technical expertise in bioinformatics, genetics, and data science are necessary for developing and maintaining these models.
For farmers, the use of this technology translates into selecting breeders with higher genetic potential, efficient herd management based on performance data, reduced costs associated with feeding and health through more selective breeding, and access to precise information for strategic decisions on breeding and investment in genetics.
4.6 Combating Invasive Plants
Laser Weeds®, a selective laser weed control equipment, represents a crucial advancement in managing invasive species in grazing areas, particularly with its ability to differentiate weeds from cultivated grasses, a notorious challenge for traditional methods.
The equipment operates by using high-energy laser beams to directly target unwanted plants, damaging their cellular tissues and eventually eliminating them. This technique is especially effective for weeds that share metabolic characteristics with cultivated grasses, such as sourgrass, foxtail, bermudagrass, etc. One of the main advantages of using lasers is precision, which minimizes damage to desirable plants, a complex task when dealing with chemical herbicides.
AI system is trained to identify different plant species through machine learning. It analyzes real-time images captured by onboard cameras, associating specific features with known weeds. This recognition is based on patterns such as leaf shape, height, color, and even texture, allowing precise differentiation between weeds and crops. AI also allows dynamic adjustments in laser targeting, ensuring it only acts on necessary areas, optimizing energy consumption and increasing process efficiency.
Source: newsroom.co.nz
Alongside laser control, other equipment, like autonomous robots, utilize similar detection systems but apply mechanical methods or other types of energy, such as microwaves, to control weeds. However, the use of lasers is particularly promising in pastures due to its capability for large-scale operation and its effectiveness across varied terrains, a common reality in grazing environments.
The main technical challenge currently is ensuring the AI system maintains a low error rate, avoiding damage to cultivated grasses. This requires continuous training of the AI model with updated and diversified data. Additionally, the equipment’s adaptability to different climatic and soil conditions is crucial for its success across various pasture environments.
5. Conclusion
In summary, integrating advanced technologies such as remote sensing, IoT, and artificial intelligence in beef cattle farming represents a milestone in the sector’s evolution, offering viable solutions to many of the challenges currently faced. The use of electromagnetic waves and sophisticated sensors not only improves pasture health monitoring but also optimizes resource allocation, resulting in more sustainable production. These technologies enable more precise and efficient management, essential for meeting the growing demand for food in an environmentally responsible manner.
Internet of Things and artificial intelligence, in turn, expand producers’ ability to quickly respond to changes in the environment and animal health conditions. Continuous monitoring and predictive analyses provide deeper insights into herd management and practice optimization, from water management to feeding and reproduction, ensuring decisions are based on solid and reliable data.
As these technologies continue to evolve, it is expected that beef cattle farming will become increasingly efficient and less environmentally impactful. This will not only contribute to global food security but also to climate change mitigation as more sustainable practices are widely adopted. It is the responsibility of industry stakeholders to take a proactive approach, integrating these innovations into their production systems, aiming for a more sustainable and prosperous future for agriculture. Collaboration among researchers, producers, and policymakers will be essential to maximize the benefits of these technologies and ensure the sector can continue to grow responsibly in the 21st century.
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