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Recently, artificial intelligence (AI) has been widely used in farming.The agriculture sector is turning to AI technology to develop healthier crops, manage pests, monitor soil and growth conditions, analyse data for farmers, and improve other food supply chain management tasks.It makes it difficult for farmers to figure out the best time to plant seeds.AI assists farmers in selecting the best seed for a given weather circumstance.It also supplies weather forecast data.AI-powered solutions will assist farmers in producing more with fewer resources, increasing crop quality, and shortening product time to market.AI assists in understanding soil properties.AI assists farmers by recommending which fertilizers to apply to improve soil quality.AI can assist farmers in determining the best time to plant their seeds.Intelligent equipment can compute seed spacing and maximum planting depth.A health monitoring system is an AI-powered system that supplies farmers with information on the health of their crops as well as the nutrients that must be applied to improve yield quality and quantity.This research collects and analyses significant papers on artificial intelligence for agriculture.Farmers may now use AI to have access to advanced data and analytics tools that will promote better farming, increase efficiency, and minimize waste in biofuel and food production while minimizing negative environmental impacts.AI and Machine Learning (ML) has altered several industries, and AI wave has now reached agriculture.Several technologies are being developed by companies to aid farmers in monitoring crop and soil health.These AI-powered solutions collect more precise data on crop health in greater volume for analysis.This article investigated AI and its application in agriculture.The process of AI in agriculture is described, as well as various agricultural metrics tracked by AI.Finally, we highlighted and analysed the important uses of artificial intelligence in agriculture.This paper provides an overview of the uses of AI in soil management, crop management, weed control, and disease management.A significant emphasis is placed on the application's strengths and limitations and the methods for employing expert systems for increased productivity
Kasyap et al. (Fri,) studied this question.
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