Decision support systems (DSS) were built using the support of wireless sensors network (WSN) for resolving many real-world issues. Precision agriculture (PA) is the most popular area which requires DSS. Numerous agricultural cropping schemes in arid and semiarid areas practice irrigation process which is a crucial one and also here the main concern is water applications and management. An automatic Smart data mining based Irrigation Support Scheme is projected in our work in order to manage the irrigation in agriculture. Then for irrigation management, the author introduced the work Convolutional Neural Support Vector Machines Hybrid Classifier (CNSVMHC). This, in turn, avoids the weekly irrigations which is required for plantation. In this proposed research work, real time soil moisture content (MC) data collection were performed with the assistance of WSN and then irrigation will be controlled according to those collected data through CNSVMHC for an efficient irrigation management. The CNSVMHC is a heterogeneous combination of the convolutional neural network (CNN) and support vector machines (SVM), where the output layer of the CNN is substituted by an SVM. A control system with closed loop scheme was enabled through this process, which adjust the decision support scheme to approximation faults and local perturbations. As of the intricate and varied information dependent systems, the effectiveness and consistency of irrigation can be preserved through the soil, weather, and water and crop data. In order to do this process, we need help from the sensor network and other agricultural techniques for storing and using the rain water, maximizing their crop productivity, minimize the cost for cultivation and utilize the real time values rather than depending on prediction.

决策支持系统（DSS）是使用无线传感器网络（WSN）的支持构建的，用于解决许多现实问题。精准农业（PA）是最需要DSS的领域。干旱和半干旱地区的许多农业种植计划都采用灌溉过程，这是至关重要的过程，这里的主要关注点是水的施用和管理。我们计划设计一个基于智能数据挖掘的自动灌溉支持计划，以管理农业灌溉。然后，对于灌溉管理，作者介绍了卷积神经支持向量机混合分类器（CNSVMHC）的工作。反过来，这避免了种植所需的每周灌溉。在这项拟议的研究工作中，在WSN的帮助下进行了实时土壤水分（MC）数据收集，然后通过CNSVMHC根据收集的数据控制灌溉，以进行有效的灌溉管理。CNSVMHC是卷积神经网络（CNN）和支持向量机（SVM）的异构组合，其中CNN的输出层被SVM替代。通过该过程启用了具有闭环方案的控制系统，该系统将决策支持方案调整为近似故障和局部扰动。作为复杂而多样的信息依赖系统，灌溉的有效性和一致性可以通过土壤，天气，水和作物数据来保持。为了执行此过程，