What is artificial intelligence in agriculture. What are some uses of AI in the field of agriculture.
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What is artificial intelligence in agriculture. What are some uses of AI in the field of agriculture.
AI systems help improve the overall harvesting quality and accuracy, known as precision agriculture. AI technology detects diseases in plants, pests, and poor nutrition in farms. AI sensors can detect and target weeds and decide which herbicide to apply within the region. Here are some uses of AI in agriculture:
Crop and soil monitoring: Micro and macro nutrients in the soil are critical factors for a crop’s health and yield quantity and quality. Traditionally, human observation and judgment determined soil quality and crop health. But this method is not accurate. Instead, we now use drones to capture aerial image data and train computer vision models for intelligent monitoring of crop and soil conditions.
Insect and plant disease detection: We can now automate the detection of plant diseases and pests using image recognition technology. This works using detection, image classification, and image segmentation methods to build models that can “keep an eye” on plant health.
Livestock health monitoring: Animals are another significant component of our agriculture systems, and they tend to need a bit more tracking than plants. CattleEye is an excellent example of an AI. Use of overhead cameras and computer vision algorithms to monitor cattle health and behavior.
Intelligent spraying: UAVs equipped with computer vision AI make it possible to automate the spraying of pesticides or fertilizer evenly across a field. With real-time recognition of targeted spraying areas, UAV sprayers can operate with high precision in terms of the area and amount to be spread. This significantly reduces the risk of contaminate crops, animals, and water resources.
Automatic weeding: Intelligent sprayers are not the only AI getting into weeding. Other computer-vision robots are taking an even more direct approach to eliminate unwanted plants.
Aerial survey and imaging: AI can analyze imagery through drones and satellites to monitor crops and herds that helps farmers. In this way, if something looks wrong they can be notified immediately without having to observe the fields themselves constantly.
Produce grading and sorting: By inspecting fruit and vegetables for it’s size, color, shape and volume computer vision can automate the grading and sorting process with accuracy rates and speed much higher than a trained professional.
The future of AI in Agriculture: Farmers are as AI engineers
The growing and affordable availability of computer vision is another significant step forward here.
With considerable changes in our climate, environment, and global food needs, AI can transform 21st-century agriculture by:
Increasing efficiency of time, work, and resources.
Improving environmental sustainability.
Making resource allocation “smarter.”
Providing real-time monitoring to promote excellent health and produce quality.
Engineers evaluated whether artificial intelligence and machine learning could be used productively in agriculture as a result of the most recent advancements in these fields, and the results have been encouraging. These technologies, like other innovations, are making baby steps toward garnering consumer trust and investor interest while maintaining a focus on financial viability.
Technologies exist that can accurately determine how a field is laid out and direct a tractor (with or without driver) to prepare the field in a way that will maximize production. These technologies can function without a driver and properly determine how to best use the available space. Farmers may now take pictures of diseased crops and instantly receive a probability score for the disease’s infection thanks to modern AI-based apps. The farmer must take pictures of the crops in order to receive a probability score of nutrient deficit in real time, just like when diagnosing diseases.
However, a physical examination is advised for accurate diagnosis. Users can now map the terrain based on the nutrients in the soil that are currently present. This is highly helpful for applying urea, DAP, MOP, SSP, and separate dosages of N, P, and K to the ground in the proper amounts without endangering the health of the soil. Based on historical data of soil health and soil nutrients, technologies to monitor water release timing, flow, quantum, & speed ensure that water is used most productively.
Technologies exist that analyze environmental variables and soil characteristics to forecast yields for upcoming seasons. Additionally, there are image-based technologies that estimate production by looking at photos of plantations for things like palm, rubber, sugarcane, tea, coffee, coconuts, apples, mangoes, and more.
Thanks to AI, machines can now harvest crops more precisely and thoroughly. Once more, this is accomplished by examining photos and pruning crops to maximise yield.