Agricultural Informatics

5IoT and Machine Learning-Based Approaches for Real Time Environment Parameters Monitoring in Agriculture: An Empirical Review

Parijata Majumdar¹ and Sanjoy Mitra^2*

¹Department of Computer Science and Engineering, National Institute of Technology, Agartala, India²Department of Computer Science and Engineering, Tripura Institute of Technology, Agartala, India

Abstract

Agriculture monitoring is a promising domain for the economy as it is the primary contributor of job market and food production. Farmers are facing challenges in reducing water consumption and formulating the best irrigation schedules due to discontinuous monsoon, changing weather conditions for improvising crops yield and soil fertility. IoT-based decision making gives real time insight of weather parameters based on cost-effective sensor data acquisition and intelligent processing that reduces manual labor and saves time in Agriculture. Here in this chapter, we present an empirical review on real time visualization and on demand access of weather parameters even from remote locations and intelligent processing using IoT-based solutions like Machine Learning (ML). The ever-augmenting technologies like Machine Learning paved the way for identifying and adapting changes of crop design and irrigation patterns taking into account multi-dimensional large variety of weather data to accurately predict climate conditions suitable for crop irrigation. Hence, this chapter offers a detailed review of IoT-based Machine Learning ...