AI and Machine Learning are changing the face of agriculture as we know it. Advancements in AI and ML are helping farmers take better care of their crops, livestock, and other farm assets. Artificial intelligence (AI) is being used for a wide range of use cases in agriculture. AI is also known as machine learning because it uses algorithms to learn from data and improve over time.
AI for Agricultural Research
Agricultural research is an important part of the development process for agricultural technology. AI has been used for this purpose in the form of image analysis. In this context, AI helps scientists to extract relevant information from images. Apart from producing insights on crops, AI can also be used for mapping, sensor integration, and model training.
With the help of AI, scientists can now conduct complex research tasks faster and more accurately. AI can also be used for analyzing large volumes of data. Using AI in agricultural research is supported by the increasing availability of large datasets. AI can also produce results that can be verified by human experts. This makes it easier to gain approval from regulatory bodies to start new agricultural technologies.
Let’s take an example – a research team is analyzing images and videos of crops to get an idea about their growth. They want to compare the images with reference images to understand how the crop changes over time. A conventional approach would be to manually compare the number of pixels in each image. With AI, researchers can automate this process and get results within minutes. This saves the researchers’ time and gives them an opportunity to study other crops as well.
Artificial Intelligence for Productivity Enhancement
AI can also be used for productivity enhancements in agricultural assets. For example, a drone can map the layout of a field and then use sensors to collect data about soil, weather, and other factors that affect crop growth. The data is then analyzed and used to design irrigation or water distribution systems or to make recommendations about pesticides to be sprayed.
The sensors can also be used for monitoring crops and animals. It is estimated that up to 80% of agricultural assets can be monitored using AI. This can be done using sensors that collect data about water, soil, and weather variables, or using satellite images to track assets.
The productivity of assets can also be enhanced using AI. For example, a tractor can be programmed to follow a certain route automatically. AI can also be used to analyze data about crops and so on. This can increase the productivity of assets as well as help with resource utilization.
Smart Farming using IoT and ML
AI can be used for smart farming practices. For example, IoT sensors can be used to track crops or animal assets. This helps to understand their health and make decisions about when to sprout seeds or spray water. AI can also be used for analyzing large volumes of data. With the help of ML algorithms, we can get insights about the health of assets or predict their behavior based on environmental factors.
The data collected by IoT sensors and ML algorithms can be used to design smart farming practices such as AI can be used for smart farming practices. For example, AI can be used to suggest a new irrigation system based on the sensors’ data. This can be useful in areas where water resources are scarce.
Robotic Assistance in Farming
AI-assisted robots are gaining popularity in agriculture. These are used for performing tasks that are dangerous or difficult for humans. Let’s take an example – an autonomous robot that is used for weeding crops. It can be programmed to identify plants and weeds. It can also be equipped with sensors that track the soil’s moisture, temperature, and other factors. The robot can use the data from these sensors to perform the weeding task.
Another example is an autonomous machine that digs a trench or water well to irrigate a field. The machine can be programmed to use a specific irrigation strategy using data collected from sensors. An AI-assisted robot can also be used to check the health of crops and animals. If a sick animal needs medication, an AI-assisted robot can be used to spray the required chemicals.
The Future of Farming: AI-Enabled Robotics
Robotic assistance is the next logical step after the use of AI-assisted machines. Let’s take an example – an autonomous drone. This drone can be programmed to collect data about crops and animals. With the help of ML algorithms and AI, we can get insights into their health and behavior.
These insights can be used to make decisions about what needs to be done, when, and where. Another example is an autonomous robot that is used for harvesting crops. The robot can be programmed to identify the crop and use sensors to understand the health of the asset.
An autonomous robot can also be used for automated inspections of assets like tractors, harvesters, irrigation systems, and others. This can help in detecting problems before they become big problems. Robotic assistance for agriculture has the potential to increase productivity and improve resource utilization. It can also help with dangerous tasks that are difficult or unsafe for humans.
Conclusion
AI and ML are helping farmers to take better care of their assets and perform complex tasks using data collection and analysis. These technologies have a wide range of applications in agriculture and are expected to grow further. With the advancement in technology, people can now perform tasks that were once performed by humans only. This can be helpful in society and in agriculture.
