Pest Classification with Deep Learning and ReactJS

Abstract:

Pests are the biggest problems faced by farmers. The main objective of Pest classification with deep learning and ReactJS paper is to create a website that takes in input in the form of images from the user and tries to classify them into various pests (mainly 9 different classes). For the classification of pests, three deep learning models were chosen and trained. Their performance is compared and the best performing model is deployed in a form of a website with a good and intuitive user interface. The user interface was created with React (javascript framework), Sass, and TensorFowJS (A deep learning library designed especially for javascript developers). The deep learning models that are selected, trained, and evaluated in this paper are VGGnet-16, ResNet-152, and MobileNet. MobileNet has provided the highest accuracy of 99.80%.

• Periodical: Advances in Science and Technology (Volume 124)
• Pages: 518-524
• DOI: https://doi.org/10.4028/p-f43wx1
• Online since: February 2023
• Authors: V. Durga Jaya*, N. Poornima
• Keywords: Deep Learning, ImageNet, Pest Classification, ReactJS, ResNet, TensorFlow, TFJS, VGGnet

References

[1] W. Liu, G. Wu, F. Ren, and X. Kang, DFF-ResNet: An insect pest recognition model based on residual networks,, in Big Data Mining and Analytics, vol. 3, no. 4, pp.300-310, Dec. 2020,.

DOI: 10.26599/bdma.2020.9020021

[2] M. Das and A. Bais, DeepVeg: Deep Learning Model for Segmentation of Weed, Canola, and Canola Flea Beetle Damage,, in IEEE Access, vol. 9, pp.119367-119380, 2021,.

DOI: 10.1109/access.2021.3108003

[3] L. Li, S. Zhang and B. Wang, Plant Disease Detection and Classification by Deep Learning—A Review,, in IEEE Access, vol. 9, pp.56683-56698, 2021,.

DOI: 10.1109/access.2021.3069646

[4] Y. -S. Chen, C. -S. Hsu and C. -L. Lo, An Entire-and-Partial Feature Transfer Learning Approach for Detecting the Frequency of Pest Occurrence,, in IEEE Access, vol. 8, pp.92490-92502, 2020,.

DOI: 10.1109/access.2020.2992520

[5] E. C. Tetila, B. Brandoli Machado, G. V. Menezes, N. A. de Souza Belete, G. Astolfi and H. Pistori, A Deep-Learning Approach for Automatic Counting of Soybean Insect Pests,, in IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 10, pp.1837-1841, Oct. 2020,.

DOI: 10.1109/lgrs.2019.2954735

[6] Q. Dai, X. Cheng, Y. Qiao and Y. Zhang, Agricultural Pest Super-Resolution and Identification With Attention Enhanced Residual and Dense Fusion Generative and Adversarial Network,, in IEEE Access, vol. 8, pp.81943-81959, 2020,.

DOI: 10.1109/access.2020.2991552

[7] L. Liu et al., PestNet: An End-to-End Deep Learning Approach for Large-Scale Multi-Class Pest Detection and Classification,, in IEEE Access, vol. 7, pp.45301-45312, 2019,.

DOI: 10.1109/access.2019.2909522

[8] H. Pan, Z. Pang, Y. Wang, Y. Wang and L. Chen, A New Image Recognition and Classification Method Combining Transfer Learning Algorithm and MobileNet Model for Welding Defects,, in IEEE Access, vol. 8, pp.119951-119960, 2020,.

DOI: 10.1109/access.2020.3005450

[9] F. Ren, W. Liu and G. Wu, Feature Reuse Residual Networks for Insect Pest Recognition,, in IEEE Access, vol. 7, pp.122758-122768, 2019,.

DOI: 10.1109/access.2019.2938194

[10] K. Kadam, S. Ahirrao, K. Kotecha and S. Sahu, Detection and Localization of Multiple Image Splicing Using MobileNet V1,, in IEEE Access, vol. 9, pp.162499-162519, 2021,.

DOI: 10.1109/access.2021.3130342