The Detection and Identification of Pest-FAW Infestation in Maize Crops Using Iot-Based Deep-Learning Algorithm

International Journal of Electrical and Electronics Engineering

© 2022 by SSRG - IJEEE Journal

Volume 9 Issue 12

Year of Publication : 2022

Authors : D. Sheema, K. Ramesh, V. K. Reshma, R. Surendiran

10.14445/23488379/IJEEE-V9I12P116

How to Cite?

D. Sheema, K. Ramesh, V. K. Reshma, R. Surendiran, "The Detection and Identification of Pest-FAW Infestation in Maize Crops Using Iot-Based Deep-Learning Algorithm," SSRG International Journal of Electrical and Electronics Engineering, vol. 9,  no. 12, pp. 180-188, 2022. Crossref, https://doi.org/10.14445/23488379/IJEEE-V9I12P116

Abstract:

In today's world, technology plays an integral role in addressing everyday challenges. Using technology in agriculture allows farmers to increase productivity while managing natural resources. Agriculture has always been plagued by pests, which destroy parts of the crops or even the whole field. Pest control at an early stage of crop development is a challenge for farmers. It eventually has a negative impact on the economy. By examining the odour substance of pests, a novel way to analyze pests is presented in this study. There is a distinct smell associated with every pest. Since odour can identify concealed pests, including deeply buried pests, swirled pests, etc., it is easier to detect than other detection methodologies. In light of this, based on odour as a key consideration, the proposed system evaluates five different odours, such as pungent, musty, misty, and sweet. In this case, gas sensors are analyzed, and then features are extracted using a Faster R-CNN algorithm. Additionally, it can be used to determine the density of infestations and counting. It is possible to develop process accuracy and timeliness using pseudocode. Comparatively, accuracy increased to 6% from Faster R CNN-based pest detection. Samples of the proposed development have been tested for performance metrics.

Keywords:

Pest detection, Odour detection, Deep learning detection, IoT.

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