Jour of Adv Research in Dynamical & Control Systems, Vol. 10, 06-Special Issue, 2018 A Review on Advanced Crop Field monitoring system In Agriculture Field through Top Notch Sensors 1* Bollamreddi VVS Narayana, Department of E.C.E, K.L.University, Vijayawada, India Email Id: narayanabvv@outlook.com 2 Dr.K.S.Ravi, Department of E.C.E, NEC-Narasaraopeta, India Email Id: sreenivasaravik@gmail.com 3 Dr.N.V.K.Ramesh, Department of ECSE, K.L.University, Vijayawada, India Email Id: nv.krishnaramesh@gmail.com Abstract: In this paper, we explore the concept of the crop monitoring system .The various machine learning techniques are applied on data sensed from environment through sensors. The wireless sensor network (WSN) is now a day widely used to build decision support for overcome many problems in real world. One of the most interesting fields having an increasing need of decision support systems is precision agriculture (PA).The purpose of this paper is to design and develop an agricultural monitoring system using wireless sensor network. The sensor has to transmit the gathered information through the wireless communication network to the data server (cloud). The IOT gateway is in charge of the communication between the remote control serial devices and central control system. The farmers or the agriculture experts can observe the measurements from the web simultaneously. With the continuous monitoring of many environmental parameters, the grower can analyze the optimal environmental conditions to achieve maximum crop productiveness, for the better productivity and to achieve remarkable energy savings. Key Words: Internet of Things, Machine Learning, Artificial Neural Network, Sensors, Prediction Analytics 1. Introduction After successful survey, we proposed a system which handle crop growth. The Internet of Things is interconnection between computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to- human or human-to-computer interaction. Proposed system helps to take decision by prediction and its analysis on data sensed and collected from agriculture sector using machine learning algorithms .The data sensed from crop yield by sensor for various parameter humidity , temperature, wind-speed, sunlight etc are stored in storage through IoT platforms, which will further use for prediction of various factor which are directly impact on crop growth after prediction decision taken will be convey to the end user for further action which will gain profit of end user. Also proposed system is compare with existing system with respect to accuracy. Crop monitoring system aiming at optimizing profitability, productivity and sustainability, comprises a set of technologies including sensors, information systems, and informed management, etc. Expert systems are expected to aid farmers in plant management or environment control, but they are mostly based on the offline and static information, deviated from the actual situation. Parallel management, achieved by virtual/artificial agricultural system, computational experiment and parallel execution, provides a generic framework of solution for online decision support. We present the three steps toward the parallel management of plant 2.Prediction 3.prescription. After successful survey following sections are explained which are as follows Section 2 describes Literature survey, Section 3 describes proposed system, and Section 4 describes Acknowledgment. 2. Literature Survey The authors Thomas Truong; Anh Dinh; Khan Wahid ,explain in this paper that the device is explain which give real time environmental data to cloud storage and a machine learning algorithm to predict environmental condition for fungal detection and prevention. In machine learning algorithm using support vector machine regression (SVMr)was developed to process a raw data and predict result. SVM give result but it is less accurate than other algorithms[1]. The authors Mengzhen Kang; Fei-Yue Wang, explain in this paper that the concept of Knowledge Data Driven Model (KDDM)is used for new generation of smart agriculture which break the bottleneck of model application from laboratory environment to real world [2]. The authors Yun Shi, Zhen Wang, XianfengWang, ShanwenZhang,explain in this paper introduce the concept of Internet of things (IoT). plant diseases and insect pests causes significant reduction in quality as well as quantity of agricultural product so plant disease and insects pests forecasting is of great significance and quite necessary. By using machine learning algorithm the main objective is to achieve the disease and insect pests monitoring information and collection of IoT.[9]. The authors Carlos Cambra,SandraSendra,JaimeLloret, Laura Garcia, explain in this paper present the design of a smart IoT communication system manager used as a low cost irrigation controller. It shows how IoT, aerial images and SOA can be applied to large and smart farming system. Data is processed in *Corresponding Author: Bollamreddi VVS Narayana, Email Id: narayanabvv@outlook.com Article History: Received: May 15, 2018, Revised: June 10, 2018, Accepted: July 04, 2018 1572