IoT-Enabled Pest Identification and Classification with New Meta-Heuristic-Based Deep Learning Framework

Abstract

The pest and insect affected crop is an important concern to cause damage to the agricultural sector. While identifying the pest in the crop, the camera placement is not supported in an inconsistent manner to capture the pest images. Hence, certain Internet of Things (IoT) devices are used to catch the pest images with its corresponding agriculture based sensor, yet it also faces some limitations to provide the accurate results. In order to alleviate the problem, an IoT-assisted pest identification and classification method is proposed. Initially, the IoT sensors are used to collect the required images. Subsequently, the input images are used to perform the object detection phase that is accomplished by the Yolov3, where the pest is detected significantly. Further, the detected images are fed into the model of “Convolutional Neural Network (CNN),” in which the deep features are fetched and finally given as input to the classifier model of “Convolution Neural Long Short-Term Memory (CNLSTM),” in turn some hyper parameters are optimally tuned by “Adaptive Honey Badger Algorithm (AHBA).” Hence, the experimental results prove that the recommended method achieves the better performance in terms of diverse metrics.

Keywords:

• Adaptive honey badger algorithm
• agriculture
• convolution neural long short-term memory
• internet of things
• pest identification and classification
• smart agriculture system

Data Availability Statement

The data underlying this article are available in database, at https://github.com/xpwu95/IP102.