Machine Learning Approaches for Agro IoT Systems

In agriculture, the technological advancement is essential for better growth and sustainability in the long run. Conventional way of farming is less efficient and time consumable because of more labor cost and high energy consumption. Hence, forefront technology like the Internet of Things (IoT) would be an affordable and more precise solution for the betterment in agriculture. By deploying intelligent systems, agricultural process can be automated and human intervention can be reduced. To increase the agriculture yield, most of the industries are adopting the automation methodologies in which agricultural data are collected and processed in an efficient manner. To analyze the sensed data, machine learning approaches are used in Agro IoT. Some of the machines learning algorithms are available for predicting the solution to the agriculture problems. ML algorithms learn from the given data and make the predictions precisely. A combination of optimized CNN model with deep learning neural network model provides promising results for IoT-based smart farming system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.