Abstract
Agriculture 4.0, as the future of farming technology, comprises numerous key enabling technologies towards sustainable agriculture. The use of state-of-the-art technologies, such as the Internet of Things, transform traditional cultivation practices, like irrigation, to modern solutions of precision agriculture. To achieve effective water resource usage and automated irrigation in precision agriculture, recent technologies like machine learning (ML) can be employed. With this motivation, this paper design an IoT and ML enabled smart irrigation system (IoTML-SIS) for precision agriculture. The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation. The proposed IoTML-SIS model involves different IoT based sensors for soil moisture, humidity, temperature sensor, and light. Besides, the sensed data are transmitted to the cloud server for processing and decision making. Moreover, artificial algae algorithm (AAA) with least squares-support vector machine (LS-SVM) model is employed for the classification process to determine the need for irrigation. Furthermore, the AAA is applied to optimally tune the parameters involved in the LS-SVM model, and thereby the classification efficiency is significantly increased. The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.
Highlights
The effects of global warming and growing droughts lead to the scarcity on the continuous availability of water resource
5 Conclusion This paper has introduced a new IoTML-SIS for precision agriculture in order to effectively use the water resources in farmland
The least squares-support vector machine (LS-SVM) model is applied as a classifier to determine the required level of the water
Summary
The effects of global warming and growing droughts lead to the scarcity on the continuous availability of water resource. Other sensors like infrared radiometers (IR), satellites, multispectral & thermal cameras, are utilized for estimating water crop needs For handling this difficult problem, several sensor based smart irrigation systems using mobile applications are implemented at distinct times. With growing computer processors, many artificial intelligences based approaches like LSSVM, ANN, NF-SC, and NF-FCM are established as beneficial methods for modelling difficult problems These modules have been extended for simulating and solving the challenges in distinct regions because of a huge amount of efficient variables, interaction among the higher uncertainty, parameters, and difficulty of the solution. For ensuring the improved classification efficiency of the presented IoTML-SIS technique, extensive experimental analysis is carried out and the results are examined interms of different aspects
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