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Kumar, N.; Nagarathna, N.; Flammini, F. Insect Detection System with Field Adaption. Encyclopedia. Available online: https://encyclopedia.pub/entry/56623 (accessed on 30 June 2024).
Kumar N, Nagarathna N, Flammini F. Insect Detection System with Field Adaption. Encyclopedia. Available at: https://encyclopedia.pub/entry/56623. Accessed June 30, 2024.
Kumar, Nithin, Nagarathna Nagarathna, Francesco Flammini. "Insect Detection System with Field Adaption" Encyclopedia, https://encyclopedia.pub/entry/56623 (accessed June 30, 2024).
Kumar, N., Nagarathna, N., & Flammini, F. (2024, May 08). Insect Detection System with Field Adaption. In Encyclopedia. https://encyclopedia.pub/entry/56623
Kumar, Nithin, et al. "Insect Detection System with Field Adaption." Encyclopedia. Web. 08 May, 2024.
Insect Detection System with Field Adaption
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This entry is adapted from the peer-reviewed paper 10.3390/agriculture13030741

The most incredible diversity, abundance, spread, and adaptability in biology are found in insects. The foundation of insect study and pest management is insect recognition.

YOLOv5 insect pest classification

1. Introduction

A zoology subfield called entomology covers insect-related research [1]. Researchers discovered that a more thorough investigation is required to identify the species level of insects due to the vast number of harmful insect populations. The aim to stop insects from damaging people, animals, plants, and farms has led to a surge in entomology research in recent years [2][3]. Entomology research is crucial because it opens new avenues and benefits for chemistry, medicine, engineering, and pharmaceuticals inspired by insects and nature [4]. Insects rob and obliterate a third of the world’s harvests, resulting in the loss of numerous products, and businesses suffer losses. Quick and accurate identification of insects is essential to avoid financial losses and progress the study of entomology. Scientists are also inspired by insects while developing robotics, sensors, mechanical structures, aerodynamics, and intelligent systems [5]. Although an estimated 1.5 million insect species are on the planet, only about 750 thousand are identified and classified species [6]. However, it is uncommon for scientists to keep finding and naming new species. Some bug species are wiped out covertly due to destruction and forest fires [7]. These factors make scholarly research on insect detection crucial for demonstrating biodiversity. Identifying the order level an insect belongs to is vital in determining insects [8]. Knowing the order level is necessary to differentiate the type. Scientific research has led to the identification of 31 insect orders in the natural world [9]. Insect orders dating back to 2002 were discovered. About 21 factors have to be considered, such as the number of wings, body type, legs, and head shape [8][10]. Traditional insect testing methods are time-consuming as many criteria must be met to avoid diagnostic errors. No decision support system was found to classify at this level when examining the insect recognition literature at the order level. It was also pointed out that no thorough deep-learning comparative study identifies and classifies insects at eye level. Research shows that several classification approaches based on deep learning, artificial intelligence, or machine learning are required to consistently and accurately classify and recognize insects.
Several studies on pest taxonomy and identification have recently been published, but this area of research has yet to be fully explored [10][11]. The most popular technique for classifying and identifying insect pests in current research is transfer learning using support vector machines and DL frameworks that have already been trained [12][13][14][15][16][17]. However, the SVM machine learning technique needs more time to prepare with more enormous datasets [18]. The constraints in transfer learning that cause the most worry are overfitting and negative transfer. Ding et al. [19] conducted an experiment to classify moths in 24 classes of insects from the internet using VGG16 and achieved a classification accuracy of 89.22%. Shi et al. [20] conducted an experiment to detect and classify stored grain insects in eight classes of stored grain insects using DenseNet-121 and achieved a classification and detection accuracy of 88.06%. Mamdouh et al. [21] conducted an experiment to classify olive fruit flies using MobileNet and achieved a classification accuracy of 96.89%.

2. Opto-Acoustic Techniques

Potamitis et al. [22] developed an optoacoustic spectrum analysis-based method for identifying olive fruit flies. The optoacoustic spectrum analysis can determine the species by examining the patterns of the insects’ wingbeats. The authors looked at its temporal and frequency domains to better understand the recorded signal. The qualities that were discovered in the time and frequency parts are given as input into the random forest classifier. The recall, F1-score, and precision of the random forest classifier were all 0.93. Optoacoustic detection is ineffective in determining the difference between peaches and figs. Additionally, sunshine can also affect sensor readings. The trap is also more susceptible to shocks or other unexpected impacts that could cause false alarms on windy days.

3. Image Processing Techniques

Image processing is the foundation of the detection technique [23][24][25]. Although image-processing methods are simpler to employ than machine learning or deep learning algorithms, their performance is lighting-dependent, and they only have a moderate accuracy (between 70 and 80 percent). An image processing technique was created by Doitsidis et al. [23] to find olive fruit flies; the algorithm uses auto-brightness adjustment to remove the impact of varying lighting and weather conditions. The borders are then made sharper using a coordinate logic filter to emphasize the contrast between the dark insect and the bright background. The program then uses a noise reduction filter after a circular Hough transforms to establish the trap’s boundaries and achieve a 75% accuracy. Tirelli et al. [24] developed a Wireless Sensor Network (WSN) to find pests in greenhouses; the algorithm used to analyze photographs first takes the effects of variations in lighting out of the picture before denoising it and then looking for blobs. Insect image processing, insect segmentation, and sorting were all part of the Sun et al. [25] proposal for “soup” photographs of insects. The insects appear floating on the liquid surface in images of bug “soup”. The algorithm was tested with 19 pictures of soup, and it worked well for most of the pictures. Philimis et al. [26] were able to detect olive fruit flies and medflies in the field with the help of McPhail traps and WSNs. The creation of WSNs, which are networks of sensors that gather data, allows for the processing and transmission of that data to humans. Actuators that respond to certain events may also be present in WSNs.

4. Machine Learning Techniques

To classify 14 species of butterflies, Kaya et al. [27] developed a classifier using machine learning. The authors extracted the properties for texture and color features; the recovered features were fed into a three-layer neural network as inputs and achieved a classification accuracy of 92.85%.

5. Deep Learning Techniques

Using a multi-class classifier based on deep learning, Zhong et al. [28] categorize and count six different kinds of flying insects. The detection and coarse counting processes are built on the YOLO approach [29]; the authors modify the photographs by scaling, flipping, rotating, translating, changing contrast, and adding noise to expand the dataset size. Additionally, they used a YOLO network that had already been trained and then modified the network parameters using the dataset for insect classification. Kalamatianos et al. [30] used the DIRT dataset; the authors examined various iterations of a deep learning detection algorithm called Faster Region Convolutional Neural Networks (Faster-RCNN) [30]. Before classification, convolutional neural networks with region suggestions, or RCNNs, indicate the regions of objects. Faster-RCNN was successful since it attained an mAP of 91.52%, the highest average precision for various recall settings. Although the detection accuracy of grayscale and RGB images is roughly the same, the authors demonstrated that the size of the image strongly influences detection. Due to the computationally demanding nature of Faster RCNN, each e-trap sends its periodically collected image to a server for processing, devising a method for spotting codling moths [31]. Translation, rotation, and reversal all enhance the visuals. An algorithm for color correction is used to equalize the average brightness of the red, green, and blue channels during the pre-processing of the images. The moths in the photos are then found using a sliding window [31]. CNNs are supervised learning systems that apply filters with predetermined weights on picture pixels utilizing an extensive deep-learning neural network [19] for detecting 24 different insect species in agricultural fields; a trained VGG-19 network is used to extract the necessary characteristics. Then, the bug’s location is determined via the “Region Proposal Network (RPN) [32]”, and an mAP of 89.22% was attained.

References

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  2. Martineau, M.; Conte, D.; Raveaux, R.; Arnault, I.; Munier, D.; Venturini, G. A survey on image-based insect classification. Pattern Recognit. 2017, 65, 273–284.
  3. Thenmozhi, K.; Reddy, U.S. Crop pest classifcation based on deep convolutional neural network and transfer learning. Comput. Electron. Agric. 2009, 164, 104906.
  4. Ngô-Muller, V.; Garrouste, R.; Nel, A. Small but important: A piece of mid-cretaceous burmese amber with a new genus and two new insect species (odonata: Burmaphlebiidae & ‘psocoptera’: Compsocidae). Cretac. Res. 2020, 110, 104405.
  5. Serres, J.R.; Viollet, S. Insect-inspired vision for autonomous vehicles. Curr. Opin. Insect Sci. 2018, 30, 46–51.
  6. Fox, R.; Harrower, C.A.; Bell, J.R.; Shortall, C.R.; Middlebrook, I.; Wilson, R.J. Insect population trends and the IUCN red list process. J. Insect Conserv. 2019, 23, 269–278.
  7. Krawchuk, M.A.; Meigs, G.W.; Cartwright, J.M.; Coop, J.D.; Davis, R.; Holz, A.; Kolden, C.; Meddens, A.J. Disturbance refugia within mosaics of forest fire, drought, and insect outbreaks. Front. Ecol. Environ. 2020, 18, 235–244.
  8. Gullan, P.J.; Cranston, P.S. The Insects: An Outline of Entomology; Wiley: Hoboken, NJ, USA, 2014.
  9. Wheeler, W.C.; Whiting, M.; Wheeler, Q.D.; Carpenter, J.M. The Phylogeny of the Extant Hexapod Orders. Cladistics 2001, 17, 113–169.
  10. Kumar, N.; Nagarathna. Survey on Computational Entomology: Sensors based Approaches to Detect and Classify the Fruit Flies. In Proceedings of the 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India, 1–3 July 2020; pp. 1–6.
  11. Amarathunga, D.; Grundy, J.; Parry, H.; Dorin, A. Methods of insect image capture and classification: A Systematic literature review. Smart Agric. Technol. 2021, 1, 100023.
  12. Yang, Z.; He, W.; Fan, X.; Tjahjadi, T. PlantNet: Transfer learning based fine-grained network for high-throughput plants recognition. Soft Comput. 2022, 26, 10581–10590.
  13. Rehman, M.Z.U.; Ahmed, F.; Khan, M.A.; Tariq, U.; Jamal, S.S.; Ahmad, J.; Hussain, L. Classification of citrus plant diseases using deep transfer learning. Comput. Mater. Continua 2022, 70, 1401–1417.
  14. Xiao, Z.; Yin, K.; Geng, L.; Wu, J.; Zhang, F.; Liu, Y. Pest identification via hyperspectral image and deep learning. Signal Image Video Process. 2022, 16, 873–880.
  15. Amrani, A.; Sohel, F.; Diepeveen, D.; Murray, D.; Jones, M. Deep learning-based detection of aphid colonies on plants from a reconstructed Brassica image dataset. Comput. Electron. Agric. 2023, 205, 107587.
  16. Wang, Q.J.; Zhang, S.Y.; Dong, S.F.; Zhang, G.C.; Yang, J.; Li, R.; Wang, H.Q. Pest24: A large-scale very small object data set of agricultural pests for multi-target detection. Comput. Electron. Agric. 2020, 175, 105585.
  17. Amrani, A.; Sohel, F.; Diepeveen, D.; Murray, D.; Jones, M. Insect detection from imagery using YOLOv3-based adaptive feature fusion convolution network. Crop Pasture Sci. 2023.
  18. Zhang, J.P.; Li, Z.W.; Yang, J. A parallel SVM training algorithm on large-scale classification problems. In Proceedings of the 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China, 18–21 August 2005.
  19. Xia, D.; Chen, P.; Wang, B.; Zhang, J.; Xie, C. Insect detection and classification based on an improved convolutional neural network. Sensors 2018, 18, 4169.
  20. Shi, Z.; Dang, H.; Liu, Z.; Zhou, X. Detection and Identification of Stored-Grain Insects Using Deep Learning: A More Effective Neural Network. IEEE Access 2020, 8, 163703–163714.
  21. Mamdouh, N.; Khattab, A. YOLO-Based Deep Learning Framework for Olive Fruit Fly Detection and Counting. IEEE Access 2021, 9, 84252–84262.
  22. Potamitis, I.; Rigakis, I.; Tatlas, N.A. Automated surveillance of fruitflies. Sensors 2017, 17, 110.
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  26. Philimis, P.; Psimolophitis, E.; Hadjiyiannis, S.; Giusti, A.; Perelló, J.; Serrat, A.; Avila, P. A centralised remote data collection system using automated traps for managing and controlling the population of the Mediterranean (Ceratitis capitata) and olive (Dacus oleae) fruit flies. In Proceedings of the First International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2013), Paphos, Cyprus, 8 April 2013.
  27. Kaya, Y.; Kayci, L. Application of artificial neural network for automatic detection of butterfly species using color and texture features. Vis. Comput. 2014, 30, 71–79.
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  29. Redmon, J.; Divvala, S.; Girshick, R.; Farhadi, A. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016; pp. 779–788.
  30. Kalamatianos, R.; Karydis, I.; Doukakis, D.; Avlonitis, M. DIRT: The dacus image recognition toolkit. J. Imaging 2018, 4, 129.
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  32. Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv 2014, arXiv:1409.1556.
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Subjects: Computer Science, Interdisciplinary Applications
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Nithin Kumar
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Nagarathna Nagarathna
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Francesco Flammini
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Update Date: 09 May 2024
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