Using Artificial Intelligence (AI) in agriculture improves the quality of farm produce, lowers production costs and reduces the need for labour and inputs. Work becomes more precise, takes less time, carries less risk and yields more. AI can be applied in almost every area of farming, and pesticide use can be cut by 60 percent.

• It can use temperature, humidity and rainfall forecasts to set the right time for sowing seed, raising seedlings or transplanting, and sow or plant at the correct spacing and depth.
• It can identify crops and weeds to control weeds, and remove off-type plants from seed crops.
• It can detect crop diseases and pests and suggest control measures so that action is taken in time.
• It can analyse soil data to recommend and apply the right fertiliser, and calculate how much water the soil needs so that suitable irrigation is applied at the right time, reducing cost and environmental impact.
• It can predict when flowers will open and fruit ripen and how much will be harvested, and can carry out harvesting, threshing, winnowing, drying and bagging.
• It can report market prices so that farmers get a fair price for their produce.

AI-powered sensors can track the health, temperature, environment and behaviour of livestock and poultry, and monitor water quality, temperature, pH, turbidity and fish health, density and growth in fish farms, recommending action quickly. AI also helps scientists set up and supervise experiments and collect and analyse data. In extension, it can spread information on newly developed varieties, technologies, machinery and products, and forecast floods, droughts, storms, rainfall and tidal surges. A drone fitted with AI can report the overall condition of an area after a single flight over the field.

The Ministry of Agriculture has taken steps to apply AI in farming. The Department of Agricultural Extension (DAE) can use it to monitor fields, estimate output, check soil nutrients, moisture, temperature, pH and salinity, identify nutrient deficiencies, diseases and pests, and spray pesticides. Image processing, big data and AI could also assess crop damage after natural disasters to arrange incentives. The Department of Agricultural Marketing has scope to use IoT, big data and AI to verify market prices, supply and demand, imports and exports.

The National Agricultural Training Academy could provide training on IoT, big data and AI, and the Agriculture Information Service could use AI in its agricultural call centre and information services.

The Bangladesh Rice Research Institute (BRRI) has launched a mobile app called Rice Solution, a sensor-based rice pest management tool that can identify disease from a photo of an affected plant taken in the field. Gazipur Agricultural University is implementing an "e-Village" project in which sensors report on soil health in crop fields.

• AgroCares of the Netherlands makes AI-powered hardware and software for farming. Its most popular product is a nutrient scanner that tests soil samples and shows which nutrients are lacking, so farmers can apply the right fertiliser before cultivation.
• An application made by Germany's PEAT detects soil nutrient deficiencies and likely crop diseases and advises on suitable fertiliser and pesticides; the farmer only needs to upload a few photos of the crop.
• Intello Labs, an Indian startup, uses computer vision and AI to give detailed information on the condition of fruit and vegetables, reducing spoilage during marketing.
• Microsoft's FarmVibes.Bot is a farming chatbot that gives farmers personalised advice based on their crops, land and weather. About 500,000 farmers in Africa are already using it.

Many countries are already benefiting from wide use of AI in agriculture, and Bangladesh's farmers have also begun to enjoy its benefits.

Source: Jagonews24. First published in Bengali on The Agro News.