Fall Armyworm Detection on Maize Plants Using Gas Sensors, Image Classification, and Neural Network Based on IoT
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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.
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Murdia, L. K., et al. "Maize utilization in India: an overview." American Journal of Food and Nutrition 4.6 (2016): 169-176.
Midega, Charles AO, et al. "A climate-adapted push-pull system effectively controls fall armyworm, Spodoptera frugiperda (JE Smith), in maize in East Africa." Crop protection 105 (2018): 10-15. Kumar, Kasi Indra, Kanchhi Maya Waiba, and Mohinder Singh. "First report of natural infestation of Ovomermis sinensis (Nematoda: Mermithidae) parasitizing fall armyworm Spodoptera sp.(Lepidoptera: Noctuidae) in Himachal Pradesh, India." (2021).
Li, Ming, et al. "Odor recognition with a spiking neural network for bioelectronic nose." Sensors 19.5 (2019): 993.
Wu, Danli, et al. "POP-CNN: Predicting odor pleasantness with convolutional neural network." IEEE Sensors Journal 19.23 (2019): 11337-11345.
Sharma, Anju, et al. "SMILES to smell: decoding the structure–odor relationship of chemical compounds using the deep neural network approach." Journal of Chemical Information and Modeling 61.2 (2021): 676-688.
Mo, Zhuofeng, et al. "FPGA implementation for odor identification with depthwise separable convolutional neural network." Sensors 21.3 (2021): 832.
Guo, Juan, et al. "ODRP: A Deep Learning Framework for Odor Descriptor Rating Prediction Using Electronic Nose." IEEE Sensors Journal 21.13 (2021): 15012-15021.
Wen, Tengteng, et al. "An Odor Labeling Convolutional Encoder–Decoder for Odor Sensing in Machine Olfaction." Sensors 21.2 (2021): 388.
Withington, Lucy, et al. "Artificial neural networks for classifying the time series sensor data generated by medical detection dogs." Expert Systems with Applications 184 (2021): 115564.
Jana, Susovan, Ranjan Parekh, and Bijan Sarkar. "Detection of Rotten Fruits and Vegetables Using Deep Learning." Computer Vision and Machine Learning in Agriculture. Springer, Singapore, 2021. 31-49.
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 168.7 (2021): 077519.
Liu, Yang, et al. "A survey and performance evaluation of deep learning methods for small object detection." Expert Systems with Applications 172 (2021): 114602.
Bhatt, Prakruti, et al. "Identification of Diseases in Corn Leaves using Convolutional Neural Networks and Boosting." ICPRAM. 2019.
Ahila Priyadharshini, Ramar, et al. "Maize leaf disease classification using deep convolutional neural networks." Neural Computing and Applications 31.12 (2019): 8887-8895.
Uddin, Md Palash, Md Al Mamun, and Md Ali Hossain. "PCA-based feature reduction for hyperspectral remote sensing image classification." IETE Technical Review 38.4 (2021): 377-396.
Fan, Qinglan, et al. "Genetic programming for feature extraction and construction in image classification." Applied Soft Computing (2022): 108509.
Chen, Bolin, et al. "Multipath feature recalibration DenseNet for image classification." International Journal of Machine Learning and Cybernetics 12.3 (2021): 651-660.
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.
Ye, Zhenyi, Yuan Liu, and Qiliang Li. "Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods." Sensors 21.22 (2021): 7620.
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 168.7 (2021): 077519.
Jing, Tao, Qing‐Hao Meng, and Hiroshi Ishida. "Recent progress and trend of robot odor source localization." IEEJ Transactions on Electrical and Electronic Engineering 16.7 (2021): 938-953.
Hirata, Yusuke, et al. "Biohybrid sensor for odor detection." Lab on a Chip 21.14 (2021): 2643-2657.
Liu, Kewei, and Chao Zhang. "Volatile organic compounds gas sensor based on quartz crystal microbalance for fruit freshness detection: A review." Food Chemistry 334 (2021): 127615.
Kumar, Brajesh, et al. "Feature extraction for hyperspectral image classification: A review." International Journal of Remote Sensing 41.16 (2020): 6248-6287.
Wu, Xiongwei, Doyen Sahoo, and Steven CH Hoi. "Recent advances in deep learning for object detection." Neurocomputing 396 (2020): 39-64.
Pal, Sankar K., et al. "Deep learning in multi-object detection and tracking: state of the art." Applied Intelligence 51.9 (2021): 6400-6429.
Xiao, Bo, and Shih-Chung Kang. "Development of an image data set of construction machines for deep learning object detection." Journal of Computing in Civil Engineering 35.2 (2021): 05020005.
Wu, Bizhi, et al. "Application of conventional UAV-based high-throughput object detection to the early diagnosis of pine wilt disease by deep learning." Forest Ecology and Management 486 (2021): 118986.
Shivappriya, S. N., et al. "Cascade object detection and remote sensing object detection method based on trainable activation function." Remote Sensing 13.2 (2021): 200.
Lu, Xiaocong, et al. "Attention and feature fusion SSD for remote sensing object detection." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1-9.
Cheng, Gong, et al. "Prototype-CNN for few-shot object detection in remote sensing images." IEEE Transactions on Geoscience and Remote Sensing 60 (2021): 1-10.
Zhang, Ning, et al. "FPGA implementation for CNN-based optical remote sensing object detection." Electronics 10.3 (2021): 282.
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 168.7 (2021): 077519.
Jing, Tao, Qing‐Hao Meng, and Hiroshi Ishida. "Recent progress and trend of robot odor source localization." IEEJ Transactions on Electrical and Electronic Engineering 16.7 (2021): 938-953.
Hirata, Yusuke, et al. "Biohybrid sensor for odor detection." Lab on a Chip 21.14 (2021): 2643-2657.
Liu, Kewei, and Chao Zhang. "Volatile organic compounds gas sensor based on quartz crystal microbalance for fruit freshness detection: A review." Food Chemistry 334 (2021): 127615.
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