Fall Armyworm Detection on Maize Plants Using Gas Sensors, Image Classification, and Neural Network Based on IoT - D. Sheema; K. Ramesh; P. N. Renjith; Aiswarya S. (International Journal of Intelligent Systems and Applications in Engineering, 2022)
Agriculture is pivotal to human health and for the growth of the nation, but to reap quality food, the maintenance of crops by evading pests is the vantage point. So, precision farming technology is very essential to solve this problem, and thereby it eases to yield more harvest. Crop health can be determined automatically from images which significantly paves the way to increase yields and profits for farmers along with reducing costs and time. In this study, two techniques have been proposed to solve the problem, (i) identifying the pest based on odor using gas sensors, and (ii) finding the pest infestation based on infected crops. Gas sensors are used as a substitute for human olfaction to detect gases emitting from pests. The FAW detection algorithm uses the Faster R-convolutional neural network (CNN) models VGG16, VGG19, MobileNetV2, and InceptionV3 to determine whether or not maize leaves have been infected. . Internet of Things (IoT) and Machine Learning are being used by the next generation of farmers to automate agricultural production and thereby eliminating the need for physical labour on the land while keeping on their crops. Models were developed to analyze pest and infected leaves which are captured by a Camera via remote sensing. Processes and actions are automatically triggered by the data and in specific environmental conditions to safeguard crops. Simulations were carried out using Shi-Thomas corner detection techniques. Compared to earlier proposed models, this proposed model is found to be relatively more accurate as well as more efficient. Also, the result for the object detection using odor increased to 8% compared with the previous detection and as a result of the modified image training, the models were found to be more accurate, having the accuracy range increasing from 93.35%, 93.32%, 98.01%, and 98.35% to 96.17%, 97.15%, 99.23%, and 99.13% respectively.