WEB BASED AGRICULTURE MONITORING SYSTEM USING MULTI-SENSOR DATA

Abstract

With the advancement and enhancement in technology, information about most of the things is now available to the user without actually moving there. The three most terms that make a revolution in a scenario to make world smart are satellite, cloud, and internet. With the increase in the advancement of technology, interest is to find the use of land from the available land in various aspects such as agriculture, urban planning, classification and change detection, monitoring etc. The drone is another air-borne sensor nowadays also known as Unmanned Aerial Vehicle (UAV), which is used in various applications like agriculture monitoring, crowd monitoring, and relief operation during disaster etc. Many physical devices like cameras, optical sensors, ray devices etc. can produce a variety of images including still and video which is used at several places for various purposes like in armed forces, entertainment, health care, science & research etc. The goal is to extract useful and meaningful information about the scene being imaged. The images recorded by devices are in raw form, thus needs some processing to extract information from those images which are a part of image enhancement. Image analysis is a technique done by an observer to extract information. Here, we focus on satellite and drone images which are provided by the number of sensors and having different characteristics. Agriculture monitoring made the biggest revolution as it is a part of our life and necessary so as to give a fruitful growth. For this, we need to monitor the requirement and other essential factors required for the crop. Land managers and planners show their keen interest towards the satellite-based sensing as a wide range of historically sensor data at various imaging scales is existing at a lower cost because of the radically change in processing methods in the last few years which makes them self-confident in planning and other land management activities. Researchers used satellite data in various applications especially for land monitoring, classification, change detection, information fusion, time series analysis etc. Monitoring agriculture at various levels is the major concern of many developed and developing countries from farmers to planners. Various space (i.e., Satellite) and airborne based (UAV) sensors are available which are providing end number of useful data but its challenge is to bring all data in a common scale to one platform. Because various sensors are having different spatial and temporal resolutions. In the advance era of computing, there is a need to explore the possibility of providing effective solutions based on the web, mobile etc., by which the end-user can retrieve direct information of the field as per his requirement. But the challenge with satellite images is to extract useful information as per the end-user requirement. The combination of the Internet of Things, Web Services and Cloud Computing which may help in providing smart solutions for agriculture to the farmers. Researches are using UAVs/drone in many of the real-time applications as they provide data in both images and recording form. The challenge with any UAV sensor image is a shadow that affects a lot to the quality of an image. A shadow can hinder in the identification of the target which also affects the vegetation parameters that highly depend on the band values (i.e., reflectance values at different Electromagnetic Spectrum) of the different sensors deployed on the satellite/UAV. Thus it is required to minimize the shadow effect without compromising the image quality. With the objective of monitoring and predicting the agriculture information, band values from the satellite data are required but they may lack in providing their accurate values. There are several parameters that affect the band values by which there is a possibility of error and needs to be calibrated. Therefore, a machine learning-based calibration approach may be useful for multispectral drone data.