By James R. Ladlee and Adriana Murillo-Williams
Harnessing the power of AI could potentially offer farms options to become more sustainable while improving productivity. The following article introduces six key domains of AI that are already having an impact: Machine Learning (ML), Natural Language Processing (NLP), Computer Vision, Robotics, Expert Systems, and Reinforcement Learning.
Machine Learning
is a form of AI that allows computers to learn from data without being explicitly programmed. In agriculture, ML is used for tasks like yield prediction, where historical data on weather patterns, soil health, and crop performance can be analyzed to forecast future production. The predictive model may help farmers make informed decisions about planting, pest control, fertilizer application, or other resource allocations, ensuring minimal waste and more avenues to maximize yields. By leveraging ML, farmers may not only estimate future yields but also adapt more quickly to changing environmental conditions, improving overall efficiency and return on the investment of time and money.
Natural Language Processing
is another subset of AI that focuses on enabling computers to understand and interact with human language. This means using virtual assistants or chatbots to provide real-time advice on crops, pest control, weather forecasts, and more. These tools are especially useful in remote areas, where access to expert advice might be limited. Virtual assistants also allow you to ask questions in different languages. As a caution, a human expert should always review the output from these systems as the outputs differ wildly in their complexity, accuracy, and familiarity with agriculture. Since most of these systems are not specifically designed for agriculture, they must be appropriately trained to provide accurate advice.
Computer Vision
is another AI technology transforming agriculture by allowing machines to interpret visual data. Computer vision might be used to monitor crops and detect issues like plant diseases and weed growth. For instance, cameras equipped with AI can scan fields in real time, identifying diseases or pests of potential concern and enabling farmers to review the targeted information and possibly act before the problem spreads. A targeted approach saves time and reduces the need for widespread pesticide use, benefiting both the farmer's budget and the environment. Currently, cameras with computer vision combined with machine learning are being used to help guide equipment with the targeted broadcast of herbicides.
Robotics
is perhaps one of the most visible applications of AI in agriculture. Autonomous vehicles or robots can carry out labor-intensive tasks, such as harvesting crops, planting seeds, and even monitoring fields. These robots can work tirelessly and precisely, performing tasks that would require significant manual labor. For example, research is underway at Penn State to use robots equipped with AI to navigate orchards, carefully thinning fruit or pruning fruit trees. The autonomous robot offers the potential to reduce the need for human labor and can significantly increase farm efficiency during peak seasonal work. Furthermore, robots can now handle some delicate tasks with select crops, such as picking without damaging crops, ensuring higher-quality products reach the market. When combined with computer vision, these systems offer the opportunity to make real-time marketing decisions right in the field.
Expert Systems
are another form of AI designed to provide decision-making support. These systems mimic the expertise of human specialists by using rule-based algorithms to offer recommendations in specific domains. First introduced to agriculture in the 1980s, current expert systems are far more robust thanks to advances in computer processing, quality data availability, and substantially improved algorithms. Today, expert systems can help farmers with crop selection, considering factors like soil composition, weather conditions, and potential market demand. Additionally, expert systems can offer guidance on irrigation practices, pest control strategies, and nutrient management, ensuring that the best possible advice is tailored to local conditions.
Reinforcement Learning
is a more advanced subset of AI that focuses on teaching machines to make decisions by interacting with their environment and learning from the results. The approach can be beneficial for optimizing processes. AI systems equipped with reinforcement learning might know when and how much product or water to apply based on environmental feedback such as soil moisture levels, weather forecasts, and plant needs. Over time, these systems improve their decision-making by receiving rewards (successful outcomes, such as optimal crop growth) or penalties (over- or under-watering). The ability to fine-tune irrigation and application schedules conserves resources and can contribute to maximizing yields by ensuring crops get what they need at the right time.
By integrating AI technologies into agricultural operations, farmers may have the opportunity to access new levels of precision and efficiency. Cost, access, and connectivity remain significant barriers to the widespread adoption of AI technologies. However, as AI technology improves in automating labor-intensive tasks, providing expert decision-making support, and optimizing critical processes, it may help farms become more efficient, competitive, and sustainable.
Source : psu.edu