AI can assist Farmers in the following ways:

Artificial Intelligence in Farming

• USING AI for intelligent spraying of chemicals- Every day, farms generate hundreds of data points about temperature, soil, water use, weather, and other factors. This data is used in real-time with the use of artificial intelligence and machine learning models to derive important insights such as when to sow seeds, which crops to plant, which hybrid seeds to plant for higher yields, and so on. Precision agriculture is a TERM used to describe how AI systems are helping to enhance overall harvest quality and accuracy. AI technology aids in the detection of plant disease, pests, and poor agricultural nutrition. AI sensors can detect and target weeds, then determine the best HERBICIDE to use in the area. These sophisticated AI sprayers can substantially reduce the amount of pesticides needed in the FIELDS, improving the quality of agricultural output while also reducing costs.
• Using AI-based robots for farm harvesting- Many businesses are attempting to increase agricultural efficiency. Autonomous strawberry-picking machine1 and a vacuum system that can harvest mature apples from trees are examples of products. Sensor fusion, machine vision, and artificial intelligence models are used by these devices to locate harvestable produce and assist in picking the proper crops.
• Predicting the best time to sow- The difference between a successful harvest and a failing one is just timely information on a single data point: seed sowing timing. To tackle this, AI can use a predictive analytics technique to determine the best time to sow the seeds for optimal yield. In addition to a 7-day weather forecast, it provides information on soil health and fertilizer recommendations.
• Crop yield prediction and price forecast- The major concern for many farmers is the unpredictability of crop prices. Farmers are never able to DESIGN a fixed production schedule due to fluctuating pricing. This issue is particularly widespread in crops with short shelf lives, such as tomatoes. Companies are assessing land and monitoring crop health in real-time using satellite imagery and weather data. Companies can detect pest and disease infestations, estimate tomato output and yield, and forecast prices using technologies like big data, AI, and machine learning. They can guide the farmers and governments on the future price patterns, demand level, type of crop to sow for maximum benefit, pesticide usage, etc.