This article aims to provide a comprehensive review on the academic journey of Artificial Intelligence (AI) in agriculture and highlight the challenges and opportunities in adopting AI-based advancement in agricultural systems and processes. The domain of agriculture faces several challenges such as disease and pest infestation, mole treatment, inadequate drainage and irrigation, and many others. These lead to severe crop losses along with environmental risks due to excessive use of chemicals. Several researches have been conducted to tackle these issues. The field of artificial intelligence with its rigorous learning skills is a vital approach to fix different agriculture related problems. Systems are being developed to help agricultural experts for better solutions across the world. This paper addresses the software of artificial intelligent strategies in the most important subdomains of agriculture. Weather forecasting is one of the critical areas identified in agriculture. Any impact or distraction in those areas can give way to hamper the growth of the economy.
Weather forecasting is one of the critical areas identified in agriculture. Any impact or diversion in these sectors can result in disruptions to the expanding economy. Providing a service at a low cost and operating successfully at scale is what businesses and farmers want. Robots have undergone extensive testing in this field, and have gotten better throughout the years. Agriculture is one sector where automation is crucial as it eliminates complex and tedious human labor. From a consumer’s point of view, the goal has been to maximize production while minimizing chemical exposure and maintaining high quality. Such an application of AI in agriculture can inform or warn about disasters and follow the safest approach, thereby saving lives from natural disasters.
Introduction
Artificial Intelligence is one of the major areas of research in Computer Science because of its rapid technological advancement and vast field of applications. Agriculture is one of the industries where AI is absolutely vital. Despite having a very short history of development, experts and the government acknowledged the important role played by AI, different from traditional approaches. When Mackinnon and Lemmon developed GausSem, a cotton crop simulation model that employs expert systems to maximize cotton production under the influence of irrigation, fertilizer, weed control-cultivation, climate, and other factors, they made a first attempt to use AI to simulate cotton production.
John McCarthy first proposed a study based on the idea that “every aspect of learning or any other feature of artificial intelligence can, in principle, be so precisely described that a machine could be made to simulate it” at the 1955 Dartmouth Conference, where the term “artificial intelligence” was first used in whole world. Due to its ability to address issues that people struggle to solve effectively, artificial intelligence (AI), one of the major disciplines of computer science, has recently entered many industries, including manufacturing, healthcare, finance, and education.
Humans are still amazed at what AI is capable of. Robots are providing additional help to make work easier, but scientists and backend engineers found it difficult to integrate them into the agriculture sector. This was a situation where complex activities were involved as some robots were designed to be of limited value. Nonetheless, multipurpose robots have now been developed. Currently, agriculture activities are the main source of income, which is a major contributor to the GDP. Being a hub for international trade, reducing poverty, hard labour and unemployment, providing raw materials for other industries and machinery, and ultimately boosting the economy. Sowing seeds, planting, pulling weeds, applying fertilizer, harvesting, etc. are all sequential steps in the agricultural process.
Methodology
Every day, farms produce lots of facts on temperature, soil, water use, climate conditions, etc. With the help of artificial intelligence and laptop study models, this data is leveraged in real-time to get beneficial insights like selecting the proper time to sow seeds, to explore crop options, to create more and more yields.
AI systems are helping to improve normal crop extraordinary and accuracy – which is considered as precision agriculture. AI science helps to detect disorder in plants, pests, and poor nutrition of farms. AI sensors can be aware and target weeds and then determine which herbicide to follow inside the field. This helps in less use of herbicides and cost savings. Many tech organizations developed robots, which use computer vision and synthetic brain to demonstrate and spray on weeds. These robots are able to do away with 80% of the amount of chemical substances usually sprayed on crops and express herbicide expenses by 90%. These clever AI sprayers can significantly reduce the number of chemicals used in the fields and for this reason convey the first rate improvement of agricultural yield, and fee efficiency. Many groups are working on increasing agricultural efficiency. There are products like independent strawberry-picking machines and a vacuum apparatus that can harvest mature apples from trees. These machines use sensor fusion, computing device vision, and synthetic brain fashion to identify the area around harvestable produce and help select the appropriate fruits.
Agriculture is the second largest enterprise after defense where the service robot market has been deployed for professional use. The International Federation of Robotics estimates that more than 25,000 agricultural robots have been sold – narrowly edging out those used for military purposes. A Berlin-based agritech startup 3 used a multi-lingual plant disease and pest diagnostic app that uses different photos of the plant to search for diseases; a smartphone collects the photo which matches it with a server picture and then an analysis of that specific disease is supplied and the crop is treated using intelligent spraying technology. In this way, the utility uses AI and ML to address plant diseases. Over seven million farmers have downloaded this app and it has helped identify over 385 crop diseases among field crops, fruits, and vegetables.
Results and Discussion
Over the decades, the traditional practices of agriculture have been transformed. The growing population and scarcity of land have invited people to be creative and efficient. It has become important to utilize the available land to its optimum level. Earlier, people were reluctant to adapt to technological improvements. However, it is now imperative that they accommodate these advancements to survive in the industry. Technologies such as Artificial Intelligence, Machine Learning, etc. have emerged to bring growth in various industries. Artificial Intelligence (AI) technology is supporting various sectors to boost their productivity. AI solutions have helped overcome challenges faced by several industries and are now steadily making their way into the agriculture sector as well. AI technologies have a major impact on the agriculture sector. AI can provide farmers with real-time insights from their fields, allowing them to identify areas that require irrigation, fertilization, or pesticide treatment. Moreover, innovative farming practices like vertical farming can help increase food production while reducing the use of resources. This is the best way to describe the progress of AI in agriculture. The industry, despite being one of the least digitized, has finally seen momentum for the development and application of various AI technologies. Finally, more to more farmers and companies in whole world see the value of AI-ASSISTED processes. Healthier crops, real-time field condition monitoring, higher process efficiency, reduced need for manual labor, and improved crop quality—those are some of the benefits.
Conclusion
The mostly future of AI in farming largely depends on the adoption of various types of AI solutions. Although some large-scale research is in progress and some applications are already in the market, the industry in agriculture is still underserved. Moreover, creating predictive solutions to solve a real challenge faced by farmers in farming is still a work in progress at an early stage. Artificial intelligence in agriculture not only helps farmers automate their agricultural tasks but also turns into precision farming for better crop production and quality while using fewer resources. This article presents an overview of the various types of application of AI technology in agriculture. In line with the current social situation of a reduction in manual labor, limited usable agricultural land, and a greater gap between the total food and the world population, AI has been considered as one of the most feasible solutions to those problems and has been developed and improved by scientists all over the world over the years. In this review, definitions of AI are first introduced, with the highlight being the Turing test. Then two subfields that AI is playing an important role in are demonstrated, which are soil management, weed management, and the Internet of Things (IoT), a useful data analysis and storing technology that has wide applications in agriculture, is introduced.
This article also points out three major and practical challenges of AI in agriculture sector: firstly, due to certain geographical, social or political reasons, the distribution of modern technology is uneven, which foreshadows that the application of AI will have its limitation in certain areas; secondly, despite the significant improvements made in the past years, much more study and research is needed to transfer AI-based machines and algorithms from control experiments to real agricultural environments, and to be able to handle large sets of data and to interpret them accurately and quickly are two main challenges that need to be addressed to enable the application; finally, the security of the equipment used in the open spaces of the agricultural environment and the privacy of the data collected are also problems to be addressed. Then this article specifically introduces the development of agricultural robots. Firstly, some examples of robots designed to deal with various tasks in the agricultural industry are listed. There are autonomous mobile robots that can spray pesticides in greenhouses, tractors, which use GPS and machine vision and have a pre-programmed travel path, apple picking robots that use a Cartesian coordinate system to detect objects, two types of robots that manage to move physically, such as a kind of ambush and the review points out the challenges of implementing agricultural robots, which basically revolve around the question of unpredictability in the real environment, but underlines the considerable development in this field and a promising prospect.
Read Also:
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