AI-DISA (Artificial Intelligence-based Disease Identification System for Animals)

July 19, 2023

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Animal Welfare Monitoring and Identifying Diseased Individual Animals

AI-DISA (Artificial Intelligence-based Disease Identification System for Animals)

The digital age is upon us, and a new era of smart farming comes as follows. Machine learning and artificial intelligence are used to revolutionize how livestock is managed and monitored. From poultry and dairy cows to pigs, AI-based systems improve livestock production and management. Machine learning algorithms are an integral part of precision livestock farming. Farmers use it to streamline the monitoring of animal behavior and welfare, predict disease outbreaks, and optimize feeding schedules.

The use of artificial intelligence (AI) in livestock health management has revolutionized the dairy production industry. Farmers have struggled to identify and take timely action against animal diseases that threaten the lives of their livestock and significantly impact dairy production and food security. However, with AI-based early detection systems for livestock health management, farmers can easily identify sick cows and take prompt action to prevent further spread of disease.

Identification of symptoms, cattle diseases and providing proper treatments is difficult in the contemporary medical industry. Real-time management of the symptoms of cow illness and disease types as animals can’t explain their problems or pain that they are facing. In medical sector finding the cattle disease symptoms, diseases are a challenging task. Manual process of identifying the cattle disease and treatment is too complex and time consuming and also expensive. These technologies only gather information, store it in databases, and then retrieve it in the future; they do not extract any helpful data that enables medical professionals to manage the cattle disease in a better way. Existing system is a manual process where doctors diagnosis animals and identifies the diseases and gives the treatment. In foreign countries they use some advanced system such as IBM Watson, the MYCIN expert system , etc. These technologies merely gather information, store it in databases, and retrieves the same in the future, but no important information that aids medical professionals in handling the cattle disease in a better way.

The dairy production industry has long faced the threat of animal diseases that can quickly spread and cause significant livestock and economic losses. Not only do these diseases threaten the lives of livestock, but they also have a severe impact on dairy production and food security. Farmers have always been challenged with the task of identifying sick animals and taking timely action to prevent the spread of disease. However, with the help of artificial intelligence (AI), farmers can now use an early detection system to identify sick livestock and take prompt action to prevent further spread of disease.

System Requirements

In dairy production, AI-based early detection systems are a game-changer because they provide a non-invasive method of detecting potential health issues in cows. Using AI and infrared vision, the system can identify the profiled cattle and record the heat emitted. This allows for the detection of elevated temperatures that may indicate a fever or illness. A noninvasive approach to livestock health management ensures that no unnecessary procedures are carried out, making it a more humane and ethical method.

System Description

Accurate livestock image data is crucial to implementing the AI solution. To construct the mobile solution, farmers can build a portable framework and then install the AI system and an infrared camera. Within a limited space, the compact MIC-710AILX can be installed. It is powered by an ultra-compact AI inference system with an NVIDIA® Jetson Xavier™ NX supercomputer. A USB 3.0 infrared camera is utilized to capture the images. Since infrared images are quite different from general images, the infrared camera collects the images and sends them to a SKY- 640V2 GPU training server. The AI model is then trained to identify livestock profiles through the stored infrared images and the model is subsequently deployed on the MIC- 710AILX. The infrared camera can detect livestock with elevated temperatures in real-time, allowing farmers to take prompt action such as quarantine to prevent the spread of disease.

Project Implementation

The AI livestock temperature solution is easy to deploy, and its simple architecture consists of an edge AI system with a USB 3.0 camera that can ensure real-time temperature records for dairy production. The benefits of this early detection system are numerous. The system provides monthly scanning of farm animals and detailed reports that can be accessed on a user dashboard. It also includes training and access to partner animal health specialists.

Advances in Animal Farming and Agriculture

The progress we have seen in agriculture and animal farming in the last century is astonishing. Full automation of crop collection or environmental conditions monitoring would seem unreachable a few decades ago, and today, it has become a standard. With the help of technology and advanced machinery, we have found ways to maximize productivity in these sectors – not only in pursuit of profit but also in order to cope with the world’s growing population needs.

However, there are two sides to the coin. The progress came at a cost. With the development of large-scale farming, the conditions have worsened in some aspects. As the demand for animal products has been growing, farming facilities have been expanding in size while reducing space use in order to maintain cost efficiency. That does not have a great impact on animal health(to say the least).

Rising demand also creates challenges in terms of quality assurance. In the conditions that mass production dictates, it is much harder to keep the industrial farming environment entirely safe. The environmental impact of the industry is another aspect to tackle. The pollution it causes, soil exhaustion, and, even more importantly, its carbon footprint – all these problems have been escalating in recent decades despite increasingly strict regulations.

Artificial intelligence allows us finally target these issues without compromising productivity. Actually, rather the opposite – if used thoughtfully, AI stimulates it while fuelling sustainability and improving animal welfare. Now that the ethical aspects of the practices in this sector are gaining even more importance among customers, artificial intelligence can help farmers adjust to their evolving expectations.

Precision Livestock Farming Technology

In a nutshell, precision livestock farming (PLF) describes a set of tools that allow farmers to control and improve their animals’ health and welfare, the conditions in which they live, and their reproduction. In this article, we will take a look at all these areas through the lens of artificial intelligence technology. It is becoming increasingly common to engage AI in these processes as it allows the farmers to draw better conclusions from their data and combine all the elements (cameras, microphones, scanners, sensors) into a coherent system that supports decision-making, accurate predictions, and anomaly detection.

Smart Farming

While precision livestock farming refers to tools, smart farming expresses the approach to processing information. Its objective is to use the available data across the farming processes in order to optimize them. What’s worth noting, the smart approach does not necessarily involve the use of advanced machinery and full automation.

The data is the clou here – farmers working in a smart way try to gather as much relevant information as possible and make sense of it. For it to be possible, they integrate artificial intelligence-fuelled software into their processes, often paired with IoT devices that collect information regarding the conditions of the farming facilities, product quality, etc. Smart farming and precision livestock farming approaches complement each other, helping livestock producers improve animal health and outcomes.

Applications of Big Data and AI in Livestock Farming – Use Cases

Artificial intelligence can serve to streamline livestock farming in various ways and forms. Many of them involve computer vision and advanced predictive analytics. Let’s take a look at some of the most common use cases to understand the spectrum of AI’s potential in this sector.

Animal Identification

Identification of livestock is required by the regulatory organs but also guarantees safety and helps the farmers improve the quality of products and their animals’ welfare. In the past, the farmers would know their animals – their health history, age, reproduction, growth, birth date, nutrition habits, etc.

With the expansion of farming, we can maintain this good practice with a little help from technology. In many livestock farming units, each animal’s path is registered and monitored from the day of birth. That requires an identification system that will keep the track of different variables, expanding the information base about each individual animal.

Harness the full potential of AI for your business

Artificial intelligence facilitates processing this data and drawing insights out of it. By engaging computer vision, the farmers can entirely automatize the identification of livestock. It is enough to scan the identification number of the code to access all the relevant information regarding a particular animal’s state and history. With AI, even the smaller animals, like poultry, can be identified individually, instead of per flock. That lowers the epidemiological risk and facilitates improving welfare in these particularly challenging conditions.

Automated Weighing Systems

Weighing is crucial in terms of quality control. Animals can be weighed either individually or in groups (that, again, refers more to poultry and other small animals). What’s important, weighing is often a source of stress for the animals that try to avoid entering the scale. That affects both their welfare and the effectiveness of the process.

That’s why it is crucial to make the whole process as smooth and short as possible. Automation helps with that. Sensitive sensors can precisely detect weight in a split second and automatically register the results in the database, removing the necessity to scan them manually. What’s more, incorrect weight can indicate a health issue or inappropriate nutrition patterns.

An artificial intelligence system can process this data and draw insights on its basis, facilitating the improvement of farming conditions. By identifying a particular animal, the system is capable of finding correlations between its weight and its history. That streamlines quality assurance processes.

Animal Welfare Monitoring and Identifying Diseased Individual Animals

The large scale industrial farming, regardless of being subject to tight quality control norms, creates favorable conditions for epidemiological danger to appear. That makes thorough monitoring so crucial and necessary. Artificial intelligence has the power to significantly improve animal welfare while reducing epidemiological risks at the same time. Here’s a detailed look at how it’s possible.

Precision livestock farming technologies to support AI-based animal health and welfare monitoring with sound analysis, feeding behavior and water intake, animal activity and radio frequency identification.

Monitoring Drinking and Feeding Behaviors

The IoT devices powered with computer vision can register the patterns in drinking and feeding habits of the livestock, providing the farmers with valuable insights. The sensors support that process, registering the levels and tempo of consumption throughout the day and night to monitor animal behaviors and detect anomalies.

With the data processed by the AI system, the farmers are able to identify the animals with unusual nutrition habits, which can be a sign of health or behavioral issues. At the same time, they can use the acquired data to find correlations between the particular food and the health and weight of the livestock. This way, it becomes an important quality control tool.

Activity Patterns, Movement and Posture Analysis

The same tools listed above can serve to analyze other patterns essential to effective quality control. Such variables as activity throughout the day and night, movement, and posture can be significant indicators of the animal’s health. Machine learning algorithms paired with computer vision can identify them, classify them and link them to the symptoms of the particular problem, automatically issuing an alert.

Livestock farming technologies to improve animal handling – cow movement analysis with identifying cow behaviour and activity, feed intake, and rumination monitoring

Feces Identification

Feces can be a source of insightful information regarding animal welfare. With computer vision, the farmers can automatize their inspection instead of doing it manually in order to detect anomalies. In the case the animal is infected with bacteria, the feces will also contain it. The artificial intelligence system can quickly identify the potential danger of contamination based on the analyzed sample and provide insights to the farmer. Such a mechanism is a significant component of epidemic prevention practices.

Monitoring for Heat Stress with Temperature Analysis

Another way artificial intelligence can improve animal welfare is by monitoring heat stress. Due to the high concentration of livestock in a relatively limited space, the farm animals can be exposed to high temperatures, which often has a devastating effect on both their physical and mental health – particularly long term.

Livestock management system and camera technology for body temperature analysis to support early detection of heat distress and assessing health status

The sensors integrated with the artificial intelligence-based system can collect the temperature data, extracting insights regarding its rises and drops and linking it to particular events or behaviors. The machine learning model identifies patterns that elevate the risk of heat stress and issues an alert in real-time when the temperature reaches the level identified as risky. Also, the farmers can use the acquired data to introduce improvements in this area.

Monitoring Livestock Vocalizations

Just as feces, livestock vocalizations can be a valuable source of information helping the farmers assess animal welfare. A machine learning algorithm trained with audio data extracted from the recordings can identify the anomalies in the animal vocalizations and classify them based on the previously detected pattern.

By combining this source of information with the previously mentioned, the farmer is able to get a complete overview of his livestock state. Live recordings can also serve as a way to control the behaviors among the farm animals and the dynamics between them. If any pathological behaviors occur, the system provides the farmer with the capability to prevent their spreading and act right away.

Monitoring and Altering Shed and Aquaculture Conditions

Aside from controlling the welfare of livestock, artificial intelligence is also helpful when it comes to controlling conditions. It is crucial not only for the quality of their products but also the general safety and compliance with regulatory norms. Minor changes in conditions (humidity, temperature, space, brightness, etc.) can have major implications on the productivity of the breeding and other essential processes. Here we describe the main areas in this respect, that artificial intelligence can streamline.

Designing Feeding Patterns

We have already mentioned that feeding and drinking behaviors can serve as indicators of animal welfare. The data collected for monitoring purposes can also serve the purpose of finding the most optimal feeding patterns to increase productivity and maximize product quality while keeping expenses low.

Fed with an extensive dataset, the algorithm (preferably deep learning one) is capable of identifying the correlation between the desirable behaviors/quality of the product and particular feeding patterns. Advanced analytics allows the farmer to test various configurations and find the optimal ones. That, of course, affects the overall productivity of the farming process in a positive way.

Pasture Evaluation

With computer vision, livestock farming companies can control the pasture conditions automatically in order to verify whether it provides the animals with the appropriate feeding conditions. The AI recognition system powered by a trained algorithm can rate the pasture, issuing recommendations on this basis. It may, for instance, register empty spots and pasture that does not fulfill the quality norms because of fungal infections, dryness, or other relevant variables.

Based on the registered image, the farmers can also estimate the quantities of pasture available for the animals and analyze whether these numbers go in line with their recommended daily intake per their body weight.

Optimizing Hatcheries

Hatcheries enable farmers to create optimal conditions for the embryos in the eggs to develop. In industrial production, it is not possible to do it in the traditional way that involves animals. Thus, the farmers face the challenge of imitating the environmental parameters the eggs receive through natural hatching. It requires precision and constant monitoring, as any change in temperature and humidity may affect the incubation process.

The artificial intelligence system connected to sensors and incubators can extract and evaluate the relevant data to identify any condition changes that could interrupt the development of the embryos. Based on these insights, they can introduce improvements to maintain an incubation process. At the same time, it monitors the impact particular conditions has on fertility, learning on this basis and constantly improving its recommendations.

Precision livestock farming technology and AI in hatcheries to support embryos development, reproductive performance, and improve automated monitoring of laying hens and egg production

Identifying Live Embryos in Eggs

Early identification of non-hatchable and infertile eggs protects the farmers from unnecessary costs, optimizing the hatchery space use and improving its overall productivity. To be able to do that, they can combine near-infrared hyperspectral imaging techniques with machine learning. ML algorithms trained with the datasets containing images of fertile and infertile eggs classify them so that farmers can remove the latter from the hatchery as soon as possible.

Monitoring Embryos Development

Computer vision can serve not only for detecting the fertile eggs but also for their further monitoring. Poultry producers are starting to introduce incubators equipped with a time-lapse imaging system and appropriate software that can process the visual data and draw conclusions from it. The machine learning model, trained with images that document the correct embryo development, is capable of detecting anomalies at each stage and alerting the hatchery managers so that they can avoid waste and maintain their productivity as high as possible. Moreover, if such a system is paired with sensors, it provides them with insights into the conditions’ impact on embryo development.

Sex Determination

Another application of artificial intelligence that we will call out here could put an end to the controversial procedure of killing male chicks on farms. As Bayern states in its study, over 100 million male chicks are killed in German breeding farms every year since they are useless from the profit perspective. By determining the sex in the first days of incubation, the livestock producers can switch to a more ethical, but also more productive model. It is possible with a combination of magnetic resonance imaging (MRI) combined with artificial intelligence models that evaluate and classify the images.

Detecting Breeding Times and Reproduction Monitoring

The livestock producers can also engage the predictive analytics to optimize their breeding programs. The artificial intelligence system can monitor the cycle of the female animals and their heat times, suggesting the preferable insemination time. What’s important, various environmental variables also have an impact on the probability of fertilization. The system recommends the best breeding times based on the combination of all these data.

Benefits of Applying Machine Learning in Precision Livestock Farming Technologies

As you can see, machine learning technology provides livestock farmers with the capability to significantly improve the animals’ welfare. It is essential for the quality of their products, but also from the ethical and regulatory perspective. With smart devices and advanced software, it is easier than ever to monitor the living conditions of the animals and detect any anomalies that could affect them in a negative way. At the same time, the system records the behavioral patterns among animals and links them to particular variables and their combinations. That allows the farmers to constantly improve their conditions to stimulate production and maintain the highest quality.

Using artificial intelligence technology, livestock producers are capable of reducing the environmental impact of their activity and cutting controversial practices that are both ethically debatable and unsustainable. Particularly when it comes to reducing the carbon footprint, artificial intelligence is unbeatable support, allowing farmers to minimize the use of resources and improve the sustainability of their feeding patterns and overall farm productivity.

The Future of Animal Farming

As the world becomes increasingly more digitized, it’s important that the agricultural industry follows suit. With the use of new technologies and artificial intelligence, we’re able to optimize livestock farming and improve the welfare of animals. I think it’s crucial that we focus on animal welfare as it is a priority for many consumers. For the agricultural industry to remain sustainable and profitable, we must consider the well-being of animals.

Artificial intelligence provides them with tools that facilitate these changes without additional expenses and hiring more workforce. At the same time, it may solve the pressing problem of space by suggesting more optimal management of the farms and hatcheries.

The number of vegans worldwide has grown tremendously in the last decade, but on a global scale, they still constitute only 1% of all consumers. The consumption of animal products will likely continue high in the following decades, but the industry will not likely continue in the shape that we are used to. Aside from reducing its environmental impact, the farmers’ focus will likely shift to improving the conditions in which the animals are kept – also due to the consumers’ pressure.

WHY ACCURATE ANIMAL BIOMETRIC IDENTIFICATION AND TRACEABILITY IS CRUCIAL FOR HEALTH, SAFETY AND EFFICIENCY.

Livestock identification is a complex activity, strictly linked to the protectionof public health, as well as to the safeguard of livestock cattle. Identification and traceability plays a key role in every step of production of commercial animal products, from meat to milk. Animal health has a direct impact on public health, not only because some animal diseases are transmissible to humans, but also because they cause food safety concerns. Furthermore, animal epidemics can cause significant economic costs, the loss on both domestic and export markets, and the direct cost for disease control and prevention. Hence the growing need to support traditional identification and traceability systems, such as ear tags and boluses, with new tools based on modern technologies. The use of more modern technology can significantly reduce the risk of spreading diseases, including the risk of contamination in animal derived food products, which can be a threats to human health.

ANIMAL IDENTIFICATION AND TRACEABILITY: FROM YESTERDAY TO TODAY

The traditional livestock marking systems (boluses and ear tags) are still the most common methods for the animal identification. Unfortunately, theses systems were motivated by health reason but instead, to know who their owners are. However, with the progressive intensification of animal production, they have been adopted to meet a multitude of new needs. Today, identification and traceability systemswhich become efficient and safe for both animals and products derived therefrom are important management tools for health and food safety, so much so that in many countries such as the EU and the US are considered a legal requirement. The pillars of a true animal traceability system are based on the identification of individual animals and / or homogeneous groups of animals, the ability to keep track of their movements, their health and production history and the recording of this information in appropriate registers.*

FACIAL RECOGNITION OF CATTLE

The need to identify and recognize a head of cattle is not only a need for authorities, but a daily necessity for all operators along the entire supply chain, from breeders to transporters, from slaughterhouse staff to those working in markets and stalls. In the perspective of giving a real contribution to this sector, Farm4Trade, in collaboration with the Experimental Zooprophylactic Institute of Abruzzo and Molise and the AiMagelab, research laboratory of the Department of Engineering Enzo Ferrari of the (UNIMORE) University of Modena and Reggio Emilia, have developed the first animal recognition system, completely contactless, capable of using images of cattle faces, by exploiting AI and Computer vision based technologies through the use of convolutive neural networks (CNN). To develop this biometric recognition system for individual cattle identification, we’ve created a mobile application to acquire images and video streams of “cattle faces”, and then archive the collection media. The use of a smartphone application for data collection has enabled us to reproduce, as faithfully as possible, real farm conditions, and therefore verify the system’s ability to identify and recognize the animal in any environmental condition. The developed recognition system combines an image of the front profile with at least one side image of the same animal. This is due to the fact that the left and right profiles do not share any symmetry – unlike human beings.The neural networks have been trained using over 13,000 images acquired from 700 different cattle.

THE STUDIES CARRIED OUT UNTIL NOW HAVE MADE POSSIBLE TO ACHIEVE A HIGH PERCENTAGE OF RECOGNITION ACCURACY. THE RELIABILITY OF RESULTS OF THE OFFICIAL EXPERIMENTS HAVE REACHED AN AVERAGE OF 85%, AND IN SOME CASES ACCURACY EXCEEDS 90%.

Having reached such a high percentage of accuracy in a short time proves the validity of the method, a safe and effective system capable of strengthening traditional identification systems, as well as a valid tool for animal recognition during daily operations in the company, animal transport or related loading and unloading operations. Ear tags and boluses (electronic and non-electronic) may in fact be lost or reveal malfunctions and errors. It is also very difficult to avoid and control fraudulent actions, such as removal and voluntary replacement of this identification systems. Biometric recognition systems are extremely hard to tamper, and have therefore been internationally adopted to verify the accurate identity of people. For example, the increasing worldwide adoption of electronic passports with integrated biometric data identification. The patented system as “Method and System for the univocal biometric identification of on animal, based on the use of deep learning techniques” represents a disruptive technology capable of redesigning the current scene in both public and private sectors.

BENEFITS OF BIOMETRIC ANIMAL IDENTIFICATION

In the health and livestock sectors there are many data points involved in the recognition of an animal to ensure identity, origin and to be able to trace the individual’s production and health history accurately. In Europe, livestock breeders and operators in the livestock sector have a key role to actively participating in the protection of both animal heritage and human health.

The main advantages of the combination of new technologies, user-friendly tools together with traditional information systems are:

Most robust method to avoid theft, fraud and identity exchanges.
Facilitate identification and reduces errors during transport, loading and unloading operations.
Guarantee and protection of animal welfare.
Easily distinguish an animal’s race.
Improves the ability to control infectious diseases and prevent their spread.
Greater control over the use of pharmaceutical doses. .
Better planning strategies for the development of the agricultural sector.

Artificial Intelligence in Indian Animal Husbandry & Agriculture

Animal Husbandry & Agriculture is one of the most fertile industries there are for artificial intelligence (AI) and machine learning (ML). AI, machine learning and the Internet of Things (IoT) sensors that provide real-time data for algorithms increase agricultural efficiencies, improve crop yields and reduce food production costs. Global spending on smart, connected agricultural technologies and systems, including AI and machine learning, is projected to triple in revenue by 2025, reaching $ 15.3 billion. IoT-enabled Agricultural (IoTAg) monitoring is smart, connected agriculture’s fastest-growing technology segment projected to reach $ 4.5 billion by 2025, according to PwC.

Indian Government, during 2020-21 and 2021-22, has allocated funds to the tune of INR 1756.3 cores and INR 2422.7 crores to the States for introducing new technologies including drones, artificial intelligence, block chain, remote sensing and GIS etc in agriculture. Further, the Government also allocated INR 7302.50 crores and INR 7908.18 crores in 2020-21 and 2021-22 respectively to ICAR (Indian Agricultural Research Institute) for undertaking Research and Development in Agriculture for developing new technologies, their demonstration at farmer’s field and capacity building of farmers for adoption of new technology.

In addition to due focus on ensuring improved service delivery and facilitating market access to farmers, the government also accords adequate emphasis towards reducing transaction costs, promotion of Farmer Producer Organisations (FPOs) to improve their bargaining power. Development of infrastructure has also been given due attention to ensure better connectivity of farmers to national and international markets. High-yielding, cost-saving, disease/pest resistant and climate-resilient varieties and technologies in crops, horticulture, animal and fisheries science developed by ICAR have played important role in increasing production and productivity, reducing cost of production and enhancing income of the farmers. Adoption of Farming Systems Models developed by ICAR have also enabled farmers to enhance their income and strengthen their economic condition. Besides, State specific strategies for increasing farmers income, provided to States by ICAR, are also helping farmers to increase their incomes.

Some of the areas that exhibit maximum potential to improve agriculture, with the integration of artificial intelligence are described below:

Cognitive computing has become the most disruptive technology in agricultural services as it can learn, understand, and interact with different environments to maximize productivity. Microsoft is currently working with 175 farmers in Andhra Pradesh to provide agricultural, land and fertilizer advisory services. This initiative has already resulted in 30 per cent higher average yield per hectare last year. The pilot project was completed using agricultural AI applications to communicate dates, soil preparation, fertilization based on soil tests, seed treatment, optimal spreading depth, and more. Further, mobile robots and field sensors support digital agricultural robots, multidisciplinary cameras and laser scanners are used for facilities and areas of radiation that cannot be measured.
Proximity sensing, remote sensing, Internet of Things (IoT) and image-based Precision Farming are being used for intelligent data integration related to historical meteorology, soil reports, recent research, rainfall, insect infections, along with drone imagery is being used for in-depth field analysis, crop monitoring and field surveys.
The artificial use of image recognition using intelligence approaches for plant identification, cation, pest infestation and disease diagnosis is also becoming prevalent. Using AI and machine learning-based surveillance systems to monitor every crop field’s real-time video feed identifies animal or human breaches, sending an alert immediately can become very useful to prevent crop damages.
Yield mapping to find patterns in large-scale data sets and understand the orthogonality of them in real-time, and optimizing irrigation systems to measure effectiveness of frequent crop irrigation is invaluable for crop planning.
Today, there is a shortage of agricultural workers, making AI and machine learning-based smart tractors, agribots and robotics a viable option for many remote agricultural operations that struggle to find workers. These robots can harvest faster, locate and remove weeds more accurately, and thus reduce operating costs and dependence on labour. In the meantime, farmers are already turning towards chatbots for help. Chatbots help farmers by answering their questions and provide advice and guidance on specific agriculture and yield related quires.
Improving the track-and-traceability of agricultural supply chains by removing roadblocks to get fresher, safer crops to market can help reduce inventory shrinkage by providing greater visibility and control across supply chains.

Use of AI in agriculture, however promising, isn’t bereft of its challenges. Artificial Intelligence systems require a lot of data to train machines and make accurate predictions. It is difficult to find temporal data for large agricultural areas, although spatial data are easy to collect. Since data infrastructure requires maturity, it takes time to develop a powerful machine learning model. This is one of the reasons why Al is used in agricultural products like seeds, fertilizers, and pesticides, rather than in field solutions. Another important disadvantage is the inflated cost of the many different solutions available in the agricultural market. Solutions need to be more affordable and open-source so that technology can be accessed even at the farm level. With consistent efforts and scalable innovations by both public and private sector, these technological interventions can completely overhaul agriculture and change lives of farmers, for better.

Application of Artificial Intelligence for livestock disease prediction

Artificial Intelligence and its Application in Animal Disease Diagnosis

CATTLE DISEASE DETECTION USING MACHINE LEARNING TECHNIQUES