An Algorithm for Detection and Identification of Infestation Density of Pest-Fall Armyworm in Maize Plants using Deep Learning based on IoT

An Algorithm for Detection and Identification of Infestation Density of Pest-Fall Armyworm in Maize Plants using Deep Learning based on IoT
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© 2022 by IJETT Journal
Volume-70 Issue-9
Year of Publication : 2022
Authors : D. Sheema, K. Ramesh, R. Surendiran, S. Gokila, S. Aiswarya
DOI : 10.14445/22315381/IJETT-V70I9P224

How to Cite?

D. Sheema, K. Ramesh, R. Surendiran, S. Gokila, S. Aiswarya, "An Algorithm for Detection and Identification of Infestation Density of Pest-Fall Armyworm in Maize Plants using Deep Learning based on IoT" International Journal of Engineering Trends and Technology, vol. 70, no. 9, pp. 240-251, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I9P224

Abstract
Information Technology plays a vital role in human lives by dealing with everyday challenges. Technological innovation in agriculture provides farmers with the possibility to increase productivity along with the management of natural resources. Pests have plagued agriculture, destroying a part of the crops or even the entire field. Farmers face immense challenges in controlling pests at the early stage of their crop development. Eventually, it adversely affects the economy. A novel way of analysing pests by examining their odor substance is presented in this study. Every pest has a unique smell. Compared with other detection methodologies, Odor makes the work easier because it enables the identification of concealed pests, such as deeply buried pests, swirled pests, etc. Consequently, the proposed system takes note of Odor as a key consideration and evaluates five different smells, including pungent, misty, sweet, musty, and so forth. Here, gas sensors are utilized to combine these analyses with Faster R-CNN-based algorithm to extract features. It is also used to identify the density of infestation. Pseudocode can be used for further development to achieve accurate and timely processes. Compared with Faster R CNN-based pest detection, accuracy increased to 6%. Performance metrics of the proposed progression have been tested on some samples.

Keywords
Pest Detection, Object Detection, Detection of Odor, Faster R-CNN.

Reference
[1] Nagaratna, W., et al,, "Effect of Silicon and Plant Growth Regulators on the Biology and Fitness of Fall Armyworm, Spodoptera Frugiperda, A Recently Invaded Pest of Maize in India," Silicon, vol. 14, no. 3, pp. 783-793, 2022.
[2] Fukuyama, K., Matsui, K., Omatsu, S., Rivas, A., & Corchado, J. M, “ Feature Extraction and Classification of Odor Using Attention Based Neural Network,” International Symposium on Distributed Computing and Artificial Intelligence, Springer, Cham, pp. 142-149, 2019.
[3] Chacko, R., Jain, D., Patwardhan, M., Puri, A., Karande, S., & Rai, B,” Data Based Predictive Models for Odor Perception,” Scientific Reports, vol. 10, no. 1, pp. 1-13, 2020.
[4] Sharma, A., Kumar, R., Ranjta, S., & Varadwaj, P. K, “SMILES to Smell: Decoding the Structure–Odor Relationship of Chemical Compounds Using the Deep Neural Network Approach,” Journal of Chemical Information and Modeling, vol. 61, no. 2, pp. 676-688, 2021.
[5] Full, J., Delbrück, L., Sauer, A., & Miehe, R, “Market Perspectives and Future Fields of Application of Odor Detection Biosensors— A Systematic Analysis,” In Multidisciplinary Digital Publishing Institute Proceedings , vol. 60, no. 1, pp. 40, 2020.
[6] S. Poorani and P. G. Brindha, “Automatic Detection of Pomegranate Fruits Using K-Mean Clustering,” Int. J. Adv. Res.Sci. Eng., vol. 3, no. 8, pp. 198–202, 2014.
[7] V. Pham and B. Lee, “An Image Segmentation Approach for Fruit Defect Detection Using K-Means Clustering and Graph-Based Algorithm,” Vietnam J. Comput. Sci., vol. 2, no. 1, pp. 25–33,2015.
[8] Xiong, Yizhou, et al,, "An Odor Recognition Algorithm of Electronic Noses Based on Convolutional Spiking Neural Network for Spoiled Food Identification," Journal of the Electrochemical Society, vol. 168, no. 7, pp. 077519, 2021.
[9] Borowik, Piotr, et al,,"Odor Detection Using an E-Nose With aReduced Sensor Array," Sensors, vol. 20, no. 12, pp. 3542, 2020.
[10] Wu, Danli, et al,, "POP-CNN: Predicting Odor Pleasantness With Convolutional Neural Network," IEEE Sensors Journal , vol. 19, no. 23, pp. 11337-11345, 2019.
[11] Sheema, D., et al,, "Detection of Pest Using Odor Substance Based on Deep Learning Algorithms," 2021 5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT), IEEE, 2021.
[12] Withington, Lucy, et al,, "Artificial Neural Networks for Classifying the Time Series Sensor Data Generated By Medical Detection Dogs," Expert Systems With Applications , vol. 184 , pp. 115564, 2021.
[13] Yang, Ruixin, and Yingyan Yu,"Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis," Frontiers in Oncology, vol. 11, pp. 573, 2021.
[14] Vasconez, Juan Pablo, et al,, "Comparison of Convolutional Neural Networks in Fruit Detection and Counting: A Comprehensive Evaluation," Computers and Electronics in Agriculture, vol. 173, pp. 105348, 2020.
[15] Li, Dengshan, et al,, “A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network,” Sensors, vol. 20, no. 3 , pp. 578, 2020.
[16] Sheema, D., et al,, “Comparative Study of Major Algorithms for Pest Detection in Maize Crop,” 2021 International Conference on Intelligent Technologies (CONIT),IEEE, 2021.
[17] Sheema, D., et al,, “The Pest FAW Infestation Detection in Maize Fields Using Deep Learning,” 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI),IEEE, 2022.
[18] Tetila, Everton Castelao, et al,, “A Deep-Learning Approach for Automatic Counting of Soybean Insect Pests,” IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 10, pp. 1837-1841, 2019.
[19] Priya, CN Naga, et al,, “Deep Learning Based Thermal Image Processing Approach for Detection of Buried Objects and Mines,” Engineering Journal, vol. 25., no. 3, pp. 61-67, 2021.
[20] M.Muruga Lakshmi, Dr.S.Thayammal, "Ship Detection in Medium-Resolution SAR Images Using Deep Learning," SSRG International Journal of Electronics and Communication Engineering, vol. 8, no. 5, pp. 1-5, 2021. Crossref, https://doi.org/10.14445/23488549/IJECE-V8I5P101.
[21] Dijkstra, Klaas, et al,, “Centroidnetv2: A Hybrid Deep Neural Network for Small-Object Segmentation and Counting,” Neurocomputing, vol. 423, pp. 490-505, 2021.
[22] Anjali S and Nithin Joe, "Faster RCNN for Concurrent Pedestrian and Cyclist Detection," SSRG International Journal of Electronics and Communication Engineering, vol. 5, no. 5, pp. 21-24, 2018. Crossref, Https://Doi.Org/10.14445/23488549/IJECE-V5I5P105.
[23] Khaki, Saeed, et al,, “Deepcorn: A Semi-Supervised Deep Learning Method for High-Throughput Image-Based Corn Kernel Counting and Yield Estimation,” Knowledge-Based Systems, vol. 218, pp. 106874, 2021.
[24] Sheikh Haroonsafdar, Malashree G , “A Literature Survey on Internet of Things Based Flood Detection and Monitoring System Using Raspberry Pi,” International Journal of Engineering Trends and Technology, vol. 67, no. 11, pp. 27-30, 2019. Crossref, https://doi.org/10.14445/22315381/IJETT-V67I11P205.
[25] Kalletla Sunitha, "Automatically Identifying Wild Animals in Camera-Trap Images With Deep Learning," SSRG International Journal of Computer Science and Engineering, vol. 8, no. 5, pp. 12-16, 2021. Crossref, https://doi.org/10.14445/23488387/IJCSE-V8I5P102.
[26] Cheng, Jian, et al,, “Decoupled Two-Stage Crowd Counting and Beyond,” IEEE Transactions on Image Processing, vol. 30 , pp. 2862-2875, 2021.
[27] Sujata Chaudhari, Nisha Malkan, Ayesha Momin, Mohan Bonde, “Yolo Real-Time Object Detection,” International Journal of Computer Trends and Technology, vol. 68, no. 6, pp. 70-76, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I6P112.
[28] Rahimzadeh, Mohammad, and Abolfazl Attar, “Detecting and Counting Pistachios Based on Deep Learning,” Iran Journal of Computer Science , vol. 5, no. 1, pp. 69-81, 2022.
[29] Suresh Arunachalam T, Shahana R, Kavitha T, "Advanced Convolutional Neural Network Architecture: A Detailed Review," International Journal of Engineering Trends and Technology, vol. 67, no. 5, pp. 183-187, 2019. Crossref, https://doi.org/10.14445/22315381/IJETT-V67I5P231.
[30] Liu, Tao, et al,, “Detection of Aphids in Wheat Fields Using a Computer Vision Technique,” Biosystems Engineering, vol. 141, pp. 82- 93, 2016.
[31] Sahu, Dameshwari, and Ravindra Manohar Potdar, “Defect Identification and Maturity Detection of Mango Fruits Using Image Analysis,” American Journal of Artificial Intelligence, vol. 1, no. 1, pp. 5-14, 2017.
[32] Rong, Minxi, et al,, “Pest Identification and Counting of Yellow Plate in Field Based on Improved Mask R-CNN,” Discrete Dynamics in Nature and Society , vol. 2022, 2022.
[33] M. Rajeshwari, K. Rathika, "Palm Print Recognition Using Texture and Shape Features," SSRG International Journal of Computer Science and Engineering, vol. 9, no. 2, pp. 1-5, 2022. Crossref, https://doi.org/10.14445/23488387/IJCSEV9I2P101
[34] Srinivasan Suresh, "Prediction of Roadway Crashes Using Logistic Regression in SAS," SSRG International Journal of Computer Science and Engineering, vol. 7, no. 10, pp. 13-17, 2020. Crossref, https://doi.org/10.14445/23488387/IJCSEV7I10P103.
[35] Hong Khai, Teh, et al,, “Underwater Fish Detection and Counting Using Mask Regional Convolutional Neural Network,” Water, vol. 14, no. 2, pp. 222, 2022.
[36] Kitano, Bruno T., et al, “Corn Plant Counting Using Deep Learning and UAV Images,” IEEE Geoscience and Remote Sensing Letters, 2019.
[37] Abitha A and Lincy K, “A Faster RCNN Based Image Text Detection and Text to Speech Conversion,” SSRG International Journal of Electronics and Communication Engineering, vol. 5, no. 5, pp. 11-14, 2018. Crossref, https://doi.org/10.14445/23488549/IJECE-V5I5P103
[38] Gupta, Pooja, Varsha Sharma, and Sunita Varma, “People Detection and Counting Using Yolov3 and SSD Models,” Materials Today: Proceedings, 2021.
[39] Chen, Peng, et al,, “Recognition and Counting of Wheat Mites in Wheat Fields by a Three-Step Deep Learning Method,” Neurocomputing, vol. 437, pp. 21-30, 2021.