Affiliation:
1. H.V.P.M’s College of Engineering & Technology, Amravati (Maharashtra State) Affiliated to
Sant Gadge Baba Amravati University, Amravati, Maharashtra, India
2. Department of Electronics and Telecommunication Engineering, Symbiosis Institute of
Technology Symbiosis International (Deemed University), Pune – 412115, India
Abstract
India's GDP is heavily reliant on agricultural products and business
management. Therefore, it is crucial for the agriculture industry to comprehend the
most common uses of artificial intelligence (AI) through case studies. To increase its
production, this industry must overcome a number of obstacles, such as soil treatment,
plant disease and pest effects, crop management, farmers' innovative methods, and the
use of technology. The major ideas behind AI in agriculture are its adaptability,
excellence, accuracy, and economy. It is critical to examine AI applications for
managing soil, crops, and the environment, and plant or leaf diseases. Food security
continues to be seriously threatened by deforestation and poor soil conditions, both of
which harm the economy. The application's advantages, constraints, and methods for
employing expert systems to increase productivity are all given particular attention.
Businesses are utilizing robots and automation to assist farmers in developing more
effective weed control strategies for their crops. See & Spray, a robot created by Blue
River Technology, is said to use computer vision to monitor and accurately spray
weeds on cotton plants. Crop and Soil Monitoring - Businesses are using deep learning
and computer vision algorithms to interpret data taken by drones and/or software-based
technologies to monitor the health of crops and soil. Crop sustainability and weather
forecasting are accomplished via satellite systems. A Colorado-based startup employs
satellites and machine learning algorithms to examine agricultural sustainability,
forecast weather, and assess farms for the presence of diseases and pests. Utilizing
predictive analytics, machine learning models are being created to monitor and forecast
various environmental factors, such as weather variations. Drones and computer vision
are used for crop analysis, while machine learning is used for identifying soil flaws.
Publisher
BENTHAM SCIENCE PUBLISHERS
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