What are the challenges of artificial intelligence in agriculture and application of robotics in changing the future of agriculture?
Challenges of AI in Agri:
Farmers side:
1. The exorbitant expenses associated with investment.
2.The absence of technical expertise
3.Resistance to the Changes (like adopting newer Tech)
Others:
1.Technological limitations
2.Lack of Technology Infrastructure (AI)
3. AI needed other technologies to work in sync.
3.Privacy and Security Issues
Answering to second part of Question:
- Computer vision can be employed in conjunction with robotics and unmanned aerial vehicles (UAVs) to classify crops and detect illnesses and pests.
- Emerging technologies such as "Digital Twins" hold significant potential in enhancing the optimization of agricultural processes.
- The term "precision agriculture" has often been juxtaposed with frameworks such as "smart farming," "agriculture 4.0," and "agriculture 5.0."
- The latter frameworks utilize telecommunications and data infrastructure in the agricultural supply chain, as well as incorporate artificial intelligence (AI) and unmanned aerial vehicles (UAVs) to enhance the acquisition of information.
Yes, artificial intelligence (AI) is the technology will have constraints. Accurate models depend on diverse, high-quality data, which can be scarce in agriculture. For robots with sensors, limitations can make adapting to changing farming environments difficult. Data quality and availability are among the most significant challenges businesses face when implementing AI solutions. AI systems require large amounts of high-quality data to learn and make accurate predictions. One of the main challenges for farmers is the high investment cost of AI technology. AI systems require significant investments in hardware and software, as well as training and support. This can be a significant barrier for small-scale farmers, who often have limited resources.Moreover, AI-powered machines can also determine soil and crop health, provides fertilizer recommendations, monitor the weather, and can also determine the quality of crop. All such benefits of AI in agriculture enable the farmers to make better decisions and do efficient farming. One of the main technical challenges for AI adoption and scaling is the availability and quality of data. Data is the fuel for AI, and without enough, relevant, and reliable data, AI models cannot perform well or generalize to new situations. The application of robotics will help in various field operations for movement, localization, capturing, targeting and moving to the next target using drones for addressing spatial as well as temporal management of crops. Same operation can be used in spraying, weeding as well as harvesting of fruits. The application of robotics will help in various field operations for movement, localization, capturing, targeting and moving to the next target using drones for addressing spatial as well as temporal management of crops. Same operation can be used in spraying, weeding as well as harvesting of fruits. Mobile manipulation through collaborative arms (harvesting, fruit handling) and collection and conversion of useful information for the farmer so that elective application of pesticides. It is a multifunctional robotic system that can perform tasks like ploughing, sowing seeds, applying fertilizers, and spraying pesticides with precision. Krishibot: Developed by a startup based in Kerala, Krishibot is an autonomous weeding robot that identifies and removes unwanted weeds from crops. By the 2030s, the future of robotics in manufacturing could be completely autonomous, from assembly to quality control, thanks to advancements in AI and the Internet of Things (IoT). Robot maintenance and operation will replace traditional jobs in this industry.
Challenges to use of artificial intelligence and application of robotics are two different things and are diverse.
Both of these are to be used as tools to -
-increase efficiency and improve agricultural economy
-Improve yield and productivity
-make life easier for dependents on agriculture
-reduce negative environmental impact and increase positive impact
In any trade, use of artificial intelligence is for the purpose of effective decision making.
In the context of agriculture, effective decision making encompasses diverse activities such as :
- predicting weather and taking up activities such as sowing and harvesting
- predicting onset of crop diseases and finding remedies for the same
- predicting variations in input costs such as fertilisers, pesticides etc. and make decisions on timely purchases
- predicting market conditions and decide on best time to harvest crops and take the harvest to the market
Artificial intelligence basically depends on deriving information based on historical data.
Assuming the practitioners have developed relevant algorithms and effectively deployed, success is possible only based on availability of historical data.
So, for deployment of artificial intelligence in agriculture
-Available of historical data is a big challenge
-Collection of historical data is another big challenge
-Availability of skilled manpower to develop and maintain and to implement such systems is also a challenge
-Cost of such implementations and return on investment, which can take a long time, is another challenge
Usage of robotics in agriculture is for the purpose of increasing productivity and make life easier for dependents of agriculture.
The problems which can be addressed by robotics are:
- automation in harvesting crops
- automation in feeding fertilisers/ dispensing pesticides
- automation in irrigation
- automation in despatching harvest to market
The challenges for deploying robotics benefically in the context of agriculture are:
-technical ability to understand productivity related problems and design solutions
-availability of suitable manpower for such activities
-cost involved in designing and deploying such solutions
-dependency on electrical / battery power and costs involved
-safety of personnel
One of the main challenges for farmers is the high investment cost of AI technology. AI systems require significant investments in hardware and software, as well as training and support. This can be a significant barrier for small-scale farmers, who often have limited resources. The impact score 2022 of Artificial Intelligence in Agriculture is 10.26, which is computed in 2023 as per its definition. Artificial Intelligence in Agriculture is decreased by a factor of 14.41 and approximate percentage change is -58.41% when compared to preceding year 2021, which shows a falling trend. Farmers can use AI-powered systems to detect insects and plant diseases more quickly than humans. For example, an AI-powered system could detect an infestation of aphids on a crop of strawberries, send the data back to the farmer's mobile phone, and then suggest what action should be taken next.Robots powered by AI can undertake labour-intensive tasks, such as weeding, fruit picking and pruning, with precision and care. By reducing the reliance on manual labor, AI-driven robotics contribute to sustainable agriculture by minimizing the environmental impact associated with conventional farming practices.The application of robotics will help in various field operations for movement, localization, capturing, targeting and moving to the next target using drones for addressing spatial as well as temporal management of crops. Same operation can be used in spraying, weeding as well as harvesting of fruits. Electric farm and factory robots with interchangeable tools, including low-tillage solutions, novel soft robotic grasping technologies and sensors, will support the sustainable intensification of agriculture, drive manufacturing productivity and underpin future food security. Agricultural robots have advanced sensors, cameras, and imaging technologies that enable them to analyze and assess crops with precision. They can identify diseased plants, detect weeds, and apply treatments in a targeted manner. An agricultural robot is defined as any robotic device that can improve agricultural processes, by taking over many of the farmer's duties that are slow or labour intensive. Using robots in agriculture makes many tasks simpler, faster, and more effective.
Dr Rk Naresh, regarding your question, one of the main challenges is the adoption of these technologies by farmers, many of whom are still reluctant to incorporate robots or machine learning algorithms into their work. Facilitating this transition is required by clearly demonstrating the benefits and advantages they provide. Another important challenge is the high initial costs of AI hardware and software, which can be prohibitive for small and medium producers. Here, innovative business models are needed to make the technology more accessible. Issues of connectivity in rural areas, data security of collected data, possible resistance to change from workers, compliance with regulations, and ensuring high levels of accuracy and reliability of AI and robotics solutions before widespread implementation must also be considered.
Dr. RK Naresh, There are many applications that require working with high resolution images, on the order of centimeters, so the images are very large. Even for a single plot, the number of pixels to process is extremely large to make a prescription map. If the processing is to be scaled to a large enterprise, rigorous planning of the surveys and processing on a good hardware infrastructure is required. On the other hand, internet connection capabilities are required in the field and a friendly way of explaining the results to the farmers. Although progress is being made in self-learning, annotating images for supervised learning is still tedious and requires expert knowledge.
Trained machines in the Lab produced 99% . When comes to the real environment, can't get the same results.
Your question, in my opinion, is very relevant in our time, with such a rapidly progressing development of Artificial Intelligence. After reading the comments I would like to add.
Lack of standardization and interoperability: The agricultural sector covers a wide range of systems, practices and technologies that vary depending on regions and farms. This lack of standardization and interoperability creates problems with the smooth integration of solutions for artificial intelligence and robotics. Compatibility problems between different technologies and artificial intelligence platforms hinder their effective implementation and cooperation.
Ethical and legal considerations: The introduction of artificial intelligence and robotics in agriculture causes ethical and legal problems. Privacy and data security issues arise when collecting and analyzing confidential farm data. Ethical considerations include ensuring transparency and fairness in decision-making processes and eliminating potential biases in artificial intelligence algorithms. It is necessary to develop a reliable regulatory framework and guidelines governing the ethical and responsible use of artificial intelligence in agriculture.
One of the main challenges for farmers is the high investment cost of AI technology. AI systems require significant investments in hardware and software, as well as training and support. This can be a significant barrier for small-scale farmers, who often have limited resources. One of the main technical challenges for AI adoption and scaling is the availability and quality of data. Data is the fuel for AI, and without enough, relevant, and reliable data, AI models cannot perform well or generalize to new situations. AI provides farmers with the forecasting and predictive analytics to reduce errors and minimize the risk of crop failures. Weather forecasting. AI enables farmers to forecast temperatures and predict how many fruits or vegetables a harvest will yield. IoT devices, such as drones and ground-based sensors, can capture high-resolution images and data on crop health. AI algorithms can then analyze this data to identify early signs of stress, pests, or diseases, allowing farmers to protect their crops and maximize yields proactively. The application of robotics will help in various field operations for movement, localization, capturing, targeting and moving to the next target using drones for addressing spatial as well as temporal management of crops. Same operation can be used in spraying, weeding as well as harvesting of fruits. From autonomous weeding to precision seeding and harvesting, these agbots are revolutionising the agricultural landscape in India. As technology continues to evolve, these robots will play an increasingly crucial role in addressing farmers' challenges and ensuring food security for the growing population. AZO Robotics proposed that the global agricultural robot market was valued at over $43 billion in 2021. It is estimated that the market will reach a total market value of more than $81 billion by the end of 2028. Agricultural robots are revolutionizing the world of farming in unprecedented ways. Crop condition identification and corresponding chemical application, spraying or harvesting, as required by the fruit or plant. Mobile manipulation through collaborative arms (harvesting, fruit handling). Collection and conversion of useful information for the farmer. Selective application of pesticides. Farmers can use AI-powered systems to detect insects and plant diseases more quickly than humans. For example, an AI-powered system could detect an infestation of aphids on a crop of strawberries, send the data back to the farmer's mobile phone, and then suggest what action should be taken next.
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