Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, India.
Environ Sci Pollut Res Int. 2022 Mar;29(14):19955-19974. doi: 10.1007/s11356-021-13248-3. Epub 2021 Mar 31.
Internet of Things (IoT) in the field of agriculture promises to continuously provide global access to the farming information. The smart agriculture system either gives alert regarding the farm or it recommends for best agriculture field. This paper addresses both irrigation and alert, i.e., Agricultural Irrigation Recommendation and Alert (AIRA) system that operates individually without any correlation. At first, the IoT users of each farm field registers in HDFS, i.e., Hadoop Distributed File System. All the registered farm field holders will receive alerts for water level status and others. The collected data will be processed in a hybrid classifier that combines k-nearest neighbor with a neural network (k-N). The classifier classifies into five classes of irrigation alerts: low water level, high water level, maintained water level, low pressure, and cyclonic storm. For faster classification, firstly, the neural network is used. Secondly, the recommendation for agronomists is optimal. The collected data is clustered by modified fuzzy clustering, and then optimal weather conditions are recommended from attractiveness-based particle swarm optimization (APSO) algorithm. The main measurements taken into account from the farms are soil moisture, temperature, humidity, wind speed, and intensity. Also, the access for IoT users is authenticated with identity, password, and biometric. Here, biometric iris is used, which is more secure than the fingerprint. Furthermore, data security is assured based on M-RSA cryptography.
物联网（IoT）在农业领域承诺为全球提供持续的农业信息访问。智能农业系统可以提供农场的警报，也可以推荐最佳的农业领域。本文涉及灌溉和警报，即农业灌溉推荐和警报（AIRA）系统，它可以独立运行而没有任何关联。首先，每个农田的物联网用户在 HDFS（即 Hadoop 分布式文件系统）中注册。所有注册的农田持有者将收到水位状态等的警报。收集的数据将在混合分类器中进行处理，该分类器结合了 k-最近邻和神经网络（k-N）。该分类器将灌溉警报分为五类：低水位、高水位、保持水位、低压力和旋风风暴。为了更快地分类，首先使用神经网络。其次，为农学家推荐最佳方案。通过改进的模糊聚类对收集的数据进行聚类，然后从基于吸引力的粒子群优化（APSO）算法中推荐最佳天气条件。从农场收集的主要测量值包括土壤湿度、温度、湿度、风速和强度。此外，物联网用户的访问通过身份、密码和生物识别进行认证。这里使用的是生物识别虹膜，比指纹更安全。此外，基于 M-RSA 加密技术确保数据安全。
