The agricultural sector is continuously leveraging technology and tools. These technologies and tools perform various agricultural related tasks and do them very efficiently and accurately, ultimately saving a lot of time and painstaking effort. It is also true that the agricultural sector around the world is under intense pressure to produce more with fewer resources. At the same time, they also face limited land, labor shortages, climate change, natural resource degradation, low yields, and many other related challenges.
With the world's population on the rise and expected to reach 10 billion by 2050, food shortages can be addressed in two ways. The first is to use more land for large-scale agriculture, and the second is to use technology to increase the productivity of existing farmland. This led to various innovative developments in agriculture. Key technological interventions that have the potential to revolutionize the modern agricultural sector and increase productivity are artificial intelligence (AI) and machine learning (ML). Although we often hear people using AI and ML interchangeably, they are different. However, both are closely related.
AI vs. ML
Artificial intelligence (AI) is a branch of science that deals with the development of machines that mimic human intelligence. Machine learning (ML) is a subdomain of AI that allows machines to automatically learn from data without being explicitly programmed. AI and ML technologies have the ability to optimize resource utilization by analyzing agricultural data. It has changed the face of agriculture today by predicting various input parameters and predicting the post-harvest lifespan of crops. The easiest way to understand how AI and ML relate to each other is that AI is the broader concept of enabling machines or systems to sense, reason, act, or adapt like humans. , ML is the application of AI that allows machines to extract. Gain knowledge from data and autonomously learn from it. One way to help remember the difference between machine learning and artificial intelligence is to imagine them as an overarching category.
Artificial intelligence is an umbrella term that includes a variety of specific approaches and algorithms. Machine learning falls under that umbrella, but so do other major subfields such as deep learning, robotics, expert systems, and natural language processing. While artificial intelligence includes the concept of machines that can imitate human intelligence, machine learning does not. The purpose of machine learning is to teach machines how to perform specific tasks and provide accurate results by identifying patterns. AI allows machines to simulate human intelligence to solve problems. The goal is to develop intelligent systems that can perform complex tasks like humans.
AI has a wide range of applications and uses technology within systems to mimic human decision-making. Processing all types of data, including structured, semi-structured, and unstructured data, AI systems use logic and decision trees to learn, reason, and self-correct. Machine learning (ML), on the other hand, allows machines to learn autonomously from past data. The goal is to build machines that can learn from data and improve the accuracy of their output. Use data to train machines to perform specific tasks and provide accurate results. Machine learning has limited scope and uses self-learning algorithms to create predictive models. Only structured and semi-structured data can be used, and ML systems can rely on statistical models to learn and self-correct when provided with new data.
AI and ML in agriculture
Since 1950, when the term “artificial intelligence” was coined by John Mac Carthy, AI has come a long way, being leveraged in a variety of ways to best serve humanity. Agriculture is not only the main industry but also the basis of the economy. In the agricultural sector, AI and ML can play a pivotal role in various aspects of crop production and livestock. As mentioned earlier, artificial intelligence is a type of machine learning that seeks to induce perception, learning, reasoning, and understanding in machines and robots. Various companies are currently developing agricultural robots that can handle all important agricultural tasks, such as harvesting crops, in larger quantities and at a faster pace than human workers.
In this regard, crop and soil monitoring has taken the help of censors, leveraging computer vision and deep learning algorithms to process data captured by drones and software-based technologies to monitor the health of crops and soil. This is done by monitoring. Predictive agricultural analytics uses a variety of artificial intelligence and machine learning tools to predict the best time to sow seeds and get alerts about risks from pest attacks. Various machine learning models have been developed to track and predict various environmental impacts on crop yields, such as changes in weather. Many companies are currently working to streamline their supply chains. These companies use real-time data analytics on data streams from multiple sources to build efficient and smart supply chains.
Today, weed management has become an important factor in keeping crops healthy and resulting in higher yields. An estimated 250 weed species have become resistant to herbicides. A study conducted by the Weed Science Society of America on the impact of uncontrolled weeds on corn and soybean crops found that farmers are losing $43 billion annually. The ability to control weeds is a top priority for farmers and is an ongoing challenge due to the increasing resistance of weeds to herbicides. The companies have now devised automation and robotics to help farmers find more efficient ways to protect crops from weeds. Blue River Technology has reportedly developed a robot called See and Spray that uses computer vision to monitor and precisely spray weeds on cotton crops. This precise spraying helps prevent herbicide resistance. The company's website claims that its precision technology can reduce the amount of chemicals typically sprayed on crops by 80 percent and reduce herbicide spending by 90 percent.
In a country like the United States, where it is estimated that more than 1 billion pounds of pesticides are used annually, reducing herbicide spending through the use of robotics is critical. To alleviate workforce challenges, automation has also emerged as a key tool to address this issue. The industry also predicts that the number of farmers employed will decrease by 6% from 2014 to 2024. Harvest CROO Robotics has developed a robot to help strawberry farmers harvest and pack their crops. Major agricultural regions such as California and Arizona are reportedly facing millions of dollars in lost revenue due to worker shortages. In a typical season, he harvests 10,000 to 11,000 acres of strawberries in the Hillsborough County, Florida area, which has been described as the “winter strawberry capital of the nation.” Harvest CROO Robotics claims that its robot can harvest 8 acres in one day, replacing 30 human workers.
Sowing time is a very important factor in ensuring better yields. To this end, ICRISAT, the International Center for Research in the Semi-Arid Tropics, has collaborated with Microsoft to develop an artificial intelligence seeding app that leverages the Microsoft Cortana Intelligence Suite, including machine learning and Power BI. The app sends sowing recommendations to participating farmers on the best days to sow. It uses artificial intelligence to inform farmers in selected districts of Hyderabad about the correct sowing date. This is very important for farmers to harvest good crops. When farmers are informed about the appropriate dates for sowing, they can avoid losses that would otherwise occur due to seed costs and fertilizer applications. This information about the right time of sowing has already led to his 30% increase in yield for farmers who received the message. The most interesting thing is that it is very affordable. Farmers do not need to install sensors in their fields or invest in equipment. All you need is a smart feature phone that can receive text messages.
Similarly, machine learning (ML) is used in early warning systems to alert farmers to possible outbreaks. It can also be used to develop models to predict the spread of pests and diseases. Machine learning can help farmers identify areas of degradation and develop management plans to improve soil health. Drone and satellite images may be analyzed by deep learning algorithms to track crop health and identify problems. These models enable early detection and rapid response to diseases, pests, and nutrient deficiencies. Machine learning can also help farmers make informed business decisions about what to grow to match crops to existing market demands.
In conclusion, we can say that AI and ML bring powerful benefits to the agricultural sector. New possibilities are constantly emerging as data volumes grow in size and complexity. As a result, automated and intelligent systems emerge to help automate tasks, unlock value, and generate actionable insights to achieve better results. Both of these have great potential to disrupt every part of the agricultural industry over the next 100 years.
(The author writes about agriculture and social issues.He can be contacted at: [email protected])
