Artificial Intelligence In Agriculture

Artificial Intelligence (AI) is intelligence demonstrated by machines. The term artificial intelligence applies when a machine mimics tasks that humans perform with other human minds, such as problem solving”. Capabilities currently classified as AI include successfully understanding human speech, competing at high levels in strategic game systems (such as chess and Go), self-driving cars, intelligent routing in content delivery networks, military simulations, and interpreting complex data. The goals of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception, and the ability to move and manipulate objects. Robotics, on the other hand, is a branch of technology that deals with the design, construction, operation, and application of robots, as well as computer systems for their control, sensory feedback, and information processing. These technologies deal with automated machines that can replace humans in hazardous environments or manufacturing processes, or resemble humans in appearance, behavior, and cognition. Today robotics is rapidly researching, designing and building new robots that serve a variety of practical purposes. As it has made expeditions in agriculture and biotechnology.

Introduction

Agriculture is man’s oldest and yet it is the most important economic activity, providing food, feed, fiber and fuel essential for our survival. With limited land, water and labor resources the global population is expected to reach 10 billion in the next 30 years from now by 2050. Governments and businesses around the world recognized the importance of food security and the consequences of environmental degradation, pollution and water scarcity, creating an urgency to overcome some of the obstacles. In this context, I think, twenty-first century AI and robotics and sensing technologies have the potential to solve farming problems by increasing yields by several folds. By moving to a robotic agricultural system, we can make crop production significantly more efficient and more sustainable in the days to come. Agricultural production must double if the growing demand for food and bioenergy is to be met.

It is estimated that the efficiency of agricultural productivity must increase by 25% to meet that goal, while limiting the growing pressure agriculture places on the environment. Striking increases in agricultural production are needed. Today’s agriculture in industrialized and developing countries is quickly becoming an exciting high-tech industry, attracting new professionals, creating billions in job opportunities for new startups and new investors. An interesting example is an underwater robot designed in Scotland to maintain coral reefs in the Gulf of Mexico, a soft robotic fish that can independently swim alongside real fish in the ocean to protect coral reefs and open new avenues to uncover the mysteries of marine life. These robots are programmed to detect damage, reattach broken coral fragments and carry out other maintenance. Robotics technology is not only increasing the production capabilities of farmers, but also advancing the branch of robotics and artificial intelligence. Now farmers are using Artificial Intelligence and robots to increase their farm yield and other practical purposes like exploring sea beds, defusing bombs and mines. The robotics market has been booming for quite some time now, with an estimated value of USD 23.67 billion in 2020.

It is expected to reach 74 billion WASD by 2026, Robotics and Artificial Intelligence finally started changing the face of global agriculture. Robotics and automation can play a vital role in developing countries like India in meeting the needs of agricultural production. In India, the agriculture sector accounts for 18% of GDP and provides employment to 50% of the country’s workforce. The use of AI and robotics in the agriculture sector will boost rural development, further leading to rural transformation and eventually structural change and for the past so many years we have seen that robots have played a fundamental role in increasing the efficiency and reducing the cost of industrial production and products. A similar trend has begun in agriculture, with GPS- and vision-based self-guided tractors and harvesters already commercially available. With a global population of 7.8 billion by 2022, the number of smartphone subscriptions now exceeds the number of people on the planet.

The Global System for Mobile Association (GMSA) predicted that global smartphone subscriptions will reach 8 billion by 2025 [3]. We believe that cutting-edge technologies such as AI, IoT (Internet of Things), robotics, satellites, cloud computing, and machine learning are transforming agriculture and helping farmers predict their near future. Artificial Intelligence (AI) with deep learning models that help identify plant diseases by plant appearance and visual symptoms that mimic human behavior [4] prevent pest and disease outbreaks [5]. Agricultural robots are increasing the yield of productions for farmers in a variety of ways, from drones to autonomous tractors to robotic arms, technology is being deployed in creative and innovative applications. Recently, farmers have begun experimenting with autonomous systems that automate various types of operations such as pruning, thinning, and harvesting, as well as mowing, spraying, and weed removal. In the fruit tree industry, for example, workers using robotic platforms seem to be more efficient than workers using ladders.

Major Areas of Artificial Intelligence

Artificial Intelligence and Robotics in Agriculture Artificial intelligence deals with large amounts of data that are first combined with fast, interactive processing and smart algorithms that allow the system to learn from patterns within the data, and it is a field of study that includes many theories, methods, and technologies. The major subfields under AI are explained below:

1. Machine Learning

Machine learning is the subfield of computer science in which a machine can learn on its own from examples and earlier experiences. The machine can change its algorithm or correct when required. Machine learning explores the study and creation of algorithms that can surpass such algorithms following strictly static program instructions by making data-driven predictions or decisions through building a model from sample inputs and making predictions on data.

2. Neural Networks

Artificial Neural Networks (ANNs) have been developed after being inspired by biological neural networks such as the brain. ANNs are one of the most important tools in machine learning for finding patterns within data that are too complex for a human to detect and teaching a machine to recognize them.

3. Deep Learning

Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is the study of artificial neural networks and related machine learning algorithms that have more than one hidden layer. In deep learning, large amounts of data are analyzed, and here, the algorithm performs the task repeatedly, each time slightly tweaking/editing to improve the result.

4. Cognitive Computing

The main goal of cognitive computing is to copy the human thought process in a computer model. This can be achieved by using self-learning algorithms, pattern recognition by neural networks, and natural language processing, a computer can mimic the human way of thinking. Here, computerized models are used to simulate the human cognition process.

5. Computer Vision

It works by allowing computers to see, recognize, and process images, the same way human vision does, and then it provides an appropriate output. Computer Vision is closely related to AI. Here, the computer must understand what it sees, and then analyze it, as it is.

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