Design of an IoT-Based Quantity Controlled Pesticide Sprayer Using Plant Identification

Design of an IoT-Based Quantity Controlled Pesticide Sprayer Using Plant Identification

Jerin Geo Jacob, Siji A. Thomas, Bivin Biju, Richarld John, Abhilash P. R.
DOI: 10.4018/978-1-7998-6463-9.ch009
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This chapter discusses the design of a quantitative controlled pesticide sprayer and the development of an efficient algorithm for plant identification. The whole system is controlled using the raspberry pi and convolutional neural networks (CNN) algorithm for training the proposed model. Once the algorithm identifies the plant by processing the image, it is captured by using a pi camera, and it determines the pesticide and its dosage. The sensors will collect the information related to the plant condition such as humidity and surrounding temperature, which is simultaneously sent to the farmers/agriculture officers through the internet of things (IoT), for the purpose of live analysis, and they are stored using cloud services, making the system suitable for remote farming. The proposed algorithm is trained mainly for three types of plant leaves, which include tomato, brinjal, and chilly. The CNN algorithm scores accuracy of 97.2% with sensitivity and specificity of 0.94 and 0.95, respectively. The robot is intended to encourage the agriculturists for next-level farming to facilitate their work.
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Logeswari et al. (2015) designed a robotic system to sense surrounding temperature, humidity and air quality of remote agricultural fields. The system uses Global System for Mobile Communication (GSM) and it communicates with the cloud through Raspberry Pi, thereby helping the data to be used in future to create awareness about the environmental changes. The work of Gowrishankar et al. (2018) aim at designing multipurpose autonomous agricultural robotic vehicle which can be controlled through IoT for seeding and spraying of pesticides which in turn helps to reduce the human intervention, ensuring high yield and efficient utilization of resources. The authors also developed an android mobile application to control the robot and uses Wi-Fi module for communication.

Key Terms in this Chapter

Internet of Things (IoT): Internet of things is the interconnection of computing devices embedded in any objects, enabling them to send and receive data.

Convolutional Neural Networks (CNN): It is a class of deep learning technology mainly used for accurate classification of images.

Raspberry Pi: It is a credit card sized computer especially used for exploring the applications of internet of things (IoT).

Pesticide: The chemicals used to control or kill the pests.

Next-Level Farming: It is defined as providing over-all solutions in other words complete modernization from the start till the end of the agricultural applications.

Remote Farming: It is defined as monitoring and managing of farm remotely through the application of Internet, with less/ without direct human intervention.

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