Research Trends in Artificial Intelligence: Internet of Things

The Role of Machine Intelligence in Agriculture: A Case Study

Author(s): Prabhakar Laxmanrao Ramteke* and Ujwala Kshirsagar * .

Pp: 54-79 (26)

DOI: 10.2174/9789815136449123010007

* (Excluding Mailing and Handling)


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.

Keywords: Agricultural robotics, Agricultural applications, Computer vision, Crop and soil management, Machine intelligence, Satellite drone.

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