Abstract
In India, around 70% of the population is dependent on farming as it has a large arable land, while other countries are dependent on seafood. India not only does farming for food but also solves the purpose of employment as agriculture contributes around 20% of the GDP. Agriculture has the main concern of pests who harm the crops at an enormous rate. The average productivity of many crops in India is quite low. In past decades, farmers used many pesticides that harm the crop and land to solve these issues. So, a need for early detection and classification of pests may significantly decrease pest-related losses. Due to the sheer difference in the photo collecting direction, position, pest size, and challenging image backdrop, humongous pest recognition is among the most essential aspects of pest management in outdoor situations. To resolve these, a model is proposed based on the EfficientNetB4 deep CNN model. The suggested model is tested using the IP102 dataset (102 species) and approaches such as data preprocessing, data balancing, and feature extraction. The suggested EfficientNetB4 model achieved classification accuracies of 95%. All of the results indicate that the suggested approach provides a reliable option for recognizing insect pests in the field and enabling special plant treatment in agricultural production.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pavela R (2016) History, presence and perspective of using plant extracts as commercial botanical insecticides and farm products for protection against insects–a review. Plant Prot Sci52(4):229–241
Ebrahimi MA, Khoshtaghaza MH, Minaei S, Jamshidi B (2017) Vision-based pest detection based on SVM classification method. Comput Electron Agric137:52–58
Durgabai RPL, Bhargavi P (2018) Pest management using machine learning algorithms: a review. Int J Comput Sci Eng Inform Technol Res (IJCSEITR)8(1):13–22
Lima MCF, de Almeida Leandro MED, Valero C, Coronel LCP, Bazzo COG (2020) Automatic detection and monitoring of insect pests—a review. Agriculture10(5):161
Fuentes A, Yoon S, Kim SC, Park DS (2017) A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors17(9):2022
Ngugi LC, Abelwahab M, Abo-Zahhad M (2021) Recent advances in image processing techniques for automated leaf pest and disease recognition–a review. Inf Process Agric 8(1):27–51
Monis JB, Sarkar R, Nagavarun SN, Bhadra J (2022) Efficient Net: identification of crop insects using convolutional neural networks. In: 2022 International conference on advances in computing, communication and applied informatics (ACCAI). IEEE, pp 1–7
Xin M, Wang Y (2020) An image recognition algorithm of soybean diseases and insect pests based on migration learning and deep convolution network. In: 2020 International wireless communications and mobile computing (IWCMC). IEEE, pp 1977–1980
Souza WS, Alves AN, Borges DL (2019) A deep learning model for recognition of pest insects in maize plantations. In: 2019 IEEE international conference on systems, man and cybernetics (SMC). IEEE, pp 2285–2290
Deng L, Wang Y, Han Z, Yu R (2018) Research on insect pest image detection and recognition based on bio-inspired methods. Biosyst Eng169:139–148
Alves AN, Souza WS, Borges DL (2020) Cotton pests classification in field-based images using deep residual networks. Comput Electron Agric174:105488
. Yang X, Luo Y, Li M, Yang Z, Sun C, Li W (2021) Recognizing pests in field-based images by combining spatial and channel attention mechanism. IEEE Access9:162448–162458
Khan MK, Ullah MO (2022) Deep transfer learning inspired automatic insect pest recognition. In: Proceedings of the 3rd international conference on computational sciences and technologies. Mehran University of Engineering and Technology, Jamshoro, Pakistan pp 17–19
Wu X, Zhan C, Lai YK, Cheng MM, Yang J (2019) Ip102: a large-scale benchmark dataset for insect pest recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 8787–8796
Goodfellow I, Bengio Y, Courville A (2016). Deep learning. MIT press
Heravi EJ, Aghdam HH, Puig D (2016) Classification of foods using spatial pyramid convolutional neural network. In: CCIA, pp 163–168
Chung DTP, Van Tai D (2019) A fruits recognition system based on a modern deep learning technique. In J Phys Conf Ser 1327(1):012050)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dewari, S., Gupta, M., Kumar, R. (2023). Agricultural Insect Pest’s Recognition System Using Deep Learning Model. In: Kumar, S., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Third Congress on Intelligent Systems. CIS 2022. Lecture Notes in Networks and Systems, vol 608. Springer, Singapore. https://doi.org/10.1007/978-981-19-9225-4_22
Download citation
DOI: https://doi.org/10.1007/978-981-19-9225-4_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-9224-7
Online ISBN: 978-981-19-9225-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)