What Applications Does Artificial Intelligence Have in Agriculture?

Use of artificial intelligence in agriculture

There are many ways to leverage AI to improve efficiency and productivity in agriculture. We have collected some examples to illustrate some of them.

• Market demand analysis

Analysing market demand is a key aspect of modern agriculture. AI helps farmers choose the best crops to grow or sell. Descartes Labs is a New Mexico-based company that provides farmers with an AI-based platform to predict market demand. The company develops machine learning algorithms that analyse satellite images and weather data, providing valuable insights into optimal planting times and best crops. Descartes Labs can analyse data patterns to predict market demand for specific crops and help farmers maximise profits.

• Risk management

Forecasting and predictive analytics can help farmers reduce the risk of crop loss. For example, Intello Labs is an Indian startup company that uses artificial intelligence (AI) to help farmers analyse produce quality and reduce food loss.

The company develops software application products that use AI and computer vision algorithms to analyse fruits and vegetables and provide insights into quality, ripeness and quantity. These AI tools can also detect defects and diseases in crops, allowing farmers to take preventive measures before crops are damaged.

• Fertility seeds

By collecting data on plant growth, AI can help produce crops that are less susceptible to disease and better adapted to climate conditions. With the help of AI, scientists can identify the best performing plant varieties and cross them to create better hybrids.

That's right. The process of creating hybrids has been used in the agricultural industry for many years. However, collecting seed genetic information through AI technology such as Seed-X can help speed up the process and increase the likelihood of success.

• Soil health monitoring

AI systems can perform chemical soil analysis and accurately predict missing nutrients. An example is the AI-based hardware and software built by Dutch agricultural technology company Agroceres.

One of these products, the Nutrient Scanner, collects data from soil samples and provides farmers with accurate estimates of missing nutrients and overall soil health. This allows farmers to adjust fertiliser application and irrigation practices to ensure optimal crop growth and minimise environmental impact.

AgroCeres also provides farmers with customised recommendations on soil management, helping them maintain long-term soil health.

• Crop protection

AI is capable of keeping an eye on plant health in order to recognize and forecast illness, locate and eradicate weeds, and suggest efficient pest control measures. For example, precision agriculture startup Taranis uses computer vision and machine learning to provide plant insights by analysing high-resolution images of crops to detect signs of stress or disease. AI-based technology can detect and classify diseases and pests with high accuracy. This could represent a highly effective treatment against pests and reduce the need for broad-spectrum pesticides that harm beneficial insects and cause pesticide resistance.

• Observation of crop maturity

Predicting crop growth and maturity is a difficult and difficult task for farmers, but AI can perform this task quickly and accurately. AI-based hardware, such as sensors and image recognition tools, can help farmers detect and track crop changes to accurately predict when crops will reach optimal maturity. According to studies, crop maturity predictions made by AI are more accurate than those made by human observers.This increased accuracy can result in significant cost savings and higher profits for farmers.

• Soil monitoring

Farmers can precisely monitor the amount of water and nutrients in the soil by integrating sensors and AI systems.Using sensors in soil monitoring allows devices to measure various parameters such such as temperature, pH levels, nutritional content, and soil moisture.These sensors send information back to the AI system, which analyses it and provides farmers with suggestions on how to manage their crops based on what it discovers about soil conditions.

For example, AI systems can identify areas of cropland where the soil is too dry or too wet and provide recommendations on when and how much to water to optimise crop growth. Likewise, the system detects nutrient deficiencies in the soil and provides advice on the appropriate type and amount of fertiliser to improve yields.

• Identification of insect and plant diseases

Farmers can use AI-based systems to identify insects and plant diseases faster than humans. For example, an AI-based system could detect an aphid infestation in a strawberry crop, send data back to the farmer's mobile phone and then suggest further action. If pesticide application is required, the system can also automate this via a connected sprayer.

• Intelligent spraying

AI technology can automate weed and pest control. With the help of computer vision, robotic weeding is said to be so accurate that it reduces pesticide use by 90%. These data-analytics-based technologies can determine the appropriate dose of pesticide for each field by analysing historical, soil, and crop data.

Blue River Technology has revolutionised traditional weed control methods with its flagship “see and spray” machine. Using computer vision and machine learning, we can distinguish between crops and weeds and spray herbicides only where necessary. This can be expensive.

• Chatbot for farmers

Chatbots can serve as a mediator between farmers and wholesalers or consumers.Farmers can use these conversational agents to answer questions about products or services being offered, order supplies, and check inventory levels.

Chatbots can also be used to maintain a database of information about crop and soil conditions. They act as virtual agricultural assistants to perform agricultural tasks. Farmers can receive tailored advice and recommendations from chatbots, like Microsoft's FarmVibes.Bot, based on data.In order to comprehend farmers' inquiries and deliver real-time insights into the weather, market pricing, and other agricultural information, the platform makes use of machine learning algorithms and natural language processing. Currently, half a million farmers in sub-Saharan Africa are using this system.

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