Design of a lightweight recognition network for adult locusts and grasshoppers based on deep learning.

Youchen Zhen, Haibin Han, Hongru Yue, Yanmin Shan, Wei Wu, Ning Wang, Yanyan Li

Abstract read

In one paragraph

Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Youchen ZhenResearch Center for Grassland Entomology, Inner Mongolia Agricultural University, Hohhot 010020, China.

Haibin HanResearch Center for Grassland Entomology, Inner Mongolia Agricultural University, Hohhot 010020, China.

Hongru YueKey Laboratory of Biohazard Monitoring, Green Prevention and Control for Artificial Grassland, Ministry of Agriculture and Rural Affairs, Institute of Grassland Research of Chinese Academy of Agricultural Sciences, Hohhot 010010, China.

Yanmin ShanInner Mongolia Forestry and Grassland Pest Control and Quarantine Station, Hohhot 010020, China.

Wei WuKey Laboratory of Agricultural Blockchain Application, Ministry of Agriculture and Rural Affairs & Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.

Ning WangKey Laboratory of Biohazard Monitoring, Green Prevention and Control for Artificial Grassland, Ministry of Agriculture and Rural Affairs, Institute of Grassland Research of Chinese Academy of Agricultural Sciences, Hohhot 010010, China.

Yanyan LiResearch Center for Grassland Entomology, Inner Mongolia Agricultural University, Hohhot 010020, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Grassland locusts and grasshoppers play a vital role in driving the dynamic changes of grassland ecosystems. In this study, we propose a lightweight deep learning-based network model for accurate identification of locust and grasshopper genera. Two image datasets were constructed, each containing 60 genera of locusts and grasshoppers. To improve recognition accuracy and computational efficiency while reducing floating-point operations (FLOPs) and the number of parameters, we introduced the channel-wise principal-component attention (CPCA) attention mechanism module and replaced part of the EfficientNet convolution modules with GhostConv, which incorporates the efficient channel attention (ECA) attention mechanism, thereby developing the CGENet model. During training, transfer learning and the Adam optimization algorithm were employed, significantly enhancing accuracy. This study makes precise control of locusts and grasshoppers feasible, thereby helping to reduce the damage they cause to agricultural production.

Indexed as

agricultural scienceartificial intelligenceenvironmental science

Identifiers

PMID40836923

DOI10.1016/j.isci.2025.113096

PMCPMC12361779

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.