Automated Soil Residue Levels Detecting Device With IoT Interface

Introduction

The historical backdrop of Horticulture in India goes back to Hindu Valley in development time and even before that in certain pieces of south-India. India reaches 2^nd worldwide in farm outputs. In 2008, Agriculture employed 50% of India work force and contributed 17%-18% of national’s GDP. However, in India still farmers are facing scrupulous problems by not getting minimal price for their crop. Agriculture Sector in India is diminishing day after day resulting in not only lesser farm outputs but also fast disappearance of the traditional knowledge base of thousands of years of practical experience of farming. There is need to resolve this problem and guide the farmers in effective way by proposing smart agricultural system. The smart agriculture system should include making use of latest technology for better productivity in the agriculture field for different activities. These activities such as loosening the soil, seeding, special watering, moving plants when they grow bigger and lead to abundant growth of a crop. Pesticide buildup alludes to the pesticides that may stay on or in the food after they are applied to the food crops or to the dirt. The most extreme permitted levels of these buildups in food or soil are frequently endorsed by administrative bodies in numerous nations. By their tendency, pesticides are destructive to certain types of life. It is in this way to be expected that, at a specific degree of presentation, they might be unsafe to people. The amount of poisons in wheat can be incredibly high, and may represent a danger to human wellbeing. To the extent medical issues because of pesticide deposits in staples are concerned, a few nations have begun food tainting checking programs which assess information of pesticide buildups as per models and rules suggested by the Codex Committee on Pesticide Residues, just as by the European Union. The main steps for agricultural practices include preparation of soil, sowing, adding manure and fertilizers, irrigation, harvesting and storage. In village, farmers need information throughout the entire farming cycle for taking effective decision in time. The proposed Automated Soil Residue Levels Detecting Device (ASRLDD) with IoT interface need modern tools and technologies that can improve production efficiency, product quality, schedule and monitoring the crops, fertilizer, spraying, planting…etc and monitoring geospace time which helps the farmers choosing their best suitable crop. The ASRLDD with IoT interface model uses an IoT technology and a few kinds of sensors which are deployed in the crop field area, helps us in collecting the data from agriculture sector. The smart Agriculture System receives data from IoT devices which are configured with the system and same data will be uploaded into a cloud server. Cloud computing and Internet of Things (IoT) are two different advancement that are already part of our life. Their adoption and use are expected to be more and more pervasive, making them important components of the Future Internet. The smart Agriculture System uses different devices or smart things and these are interconnected for communication purpose. These devices collect the data and stores in cloud for analysis. The main objective of this proposal is to develop an automated embedded device for finding the pesticides residue levels in the soil or food in a given agriculture land. The proposed system integrates, analyzes, and correlates different data sets of information collected from agriculture sector and generates analysis report to the farmer using machine learning techniques. The purpose of this chapter is to introduce the various technologies as a frame of reference for effective way of analyzing agriculture data.

The main objective of this chapter is to describe crop and soil management for agriculture using low cost automated detecting device with IoT interface. This System is also useful to the farmers to know which type of crop should be cultivated and to estimate the productivity of that crop for particular season. The cropping details and data generated from the system are stored in cloud and proper machine learning technique is used for analyzing this data. It is also useful to the farmers to know which type of crop should be cultivated and to estimate the productivity of that crop for particular season. It makes a multidisciplinary research circumstance for exploring and tentatively approving profoundly inventive and progressive thoughts in the Agriculture area.

ASRLDD Objectives: