Internet of things‐based deeply proficient monitoring and protection system for crop field. Issue 5 (18th November 2021)

• Record Type: Journal Article
• Authors: Prabu, A. V.; Kumar, G. Sateesh; Rajasoundaran, Soundararajan; Malla, Prince Priya; Routray, Sidheswar; Mukherjee, Amrit
• Other Names: Sharma Rohit guestEditor.; Gupta Deepak guestEditor.; Maseleno Andino guestEditor.; Peng Sheng‐Lung guestEditor.; Menon Varun G. guestEditor.; Khosravi Reza guestEditor.; Jolfaei Alireza guestEditor.; Kumar Akshi guestEditor.; P Vinod guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-18
• Subjects: cognitive smart systems -- crop field -- deep learning and data analysis -- internet of things -- smart cities -- wireless sensor networks; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12876
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: The production rate of crops is significantly declining due to natural disasters, animal interventions and plant diseases. Internet of things (IoT) and wireless sensor networks are widely applied in crop field monitoring systems to observe the quality of each plant and the field. This work proposes IoT based crop field protection system (ICFPS) that monitors and protects the crop fields from animal intrusions. This proposed system uses ultrasonic sensors, hyperspectral cameras, voice recorded buzzers and other agriculture sensors to protect the entire crop field. This system uses numerous sensor nodes and cameras for gathering field objects (images and environmental objects). The proposed ICFPS creates deep learning techniques such as recurrent convolutional neural networks (RCNN) and recurrent generative adversarial neural networks (RGAN) for feature extraction, disease detection and field data monitoring practices. This proposed work develops a smart city‐based agriculture system using cognitive learning approaches. This proposed system analyses crop field data and provide automatic alerts regarding animal interferences and crop diseases. Moreover, the cognitive smart crop field system observes various field conditions which support for good production rate. In this system, sensors and camera‐enabled agriculture drones are coordinated with each other to collect the field data regularly. At the same time, the proposed work trains the RCNN and RGAN units using effective crop field datasets to attain realistic decisions within minimal time intervals. The experiment details and results show the proposed ICFPS works with 8%–10% of more classification accuracy than existing systems.