Leveraging machine learning for advancing insect pest control: A bibliometric analysis

Wang, Jiale; Chen, Yan; Huang, Jianxiang; Jiang, Xunyuan; Wan, Kai

doi:10.1111/jen.13223

Cited by 2 publications

References 58 publications

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Unveiling Trends in Forecasting Models for Crop Pest and Disease Outbreaks: A Systematic and Scientometric Analysis

Goyal,

Singh,

Raghuraman

et al. 2024

Preprint

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The exacerbation of global warming has significantly heightened the occurrence of crop pest and disease outbreaks, resulting in substantial agricultural losses worldwide. Weather-driven forecasting models play a pivotal role in predicting these outbreaks, facilitating timely and effective intervention strategies. This paper undertakes a comprehensive systematic review of the existing literature, to evaluate the comparative strengths, limitations, and relative efficacy of statistical versus machine learning forecasting models. Additionally, a bibliometric analysis encompassing 1,215 scientific studies sourced from the Web of Science Core Collection database (2000–2023), uncovering a sharp increase in research activity, particularly post-2019, across key disciplines like entomology, agronomy, and plant science. The continents leading in publication volume are Asia, Europe, and North America, with China, the United States, and India ranking as the top publishing countries. Chinese researchers rank highest among the top ten most productive authors in the field. Key journals with the highest citation rates include Plos One, Insects, and Computers and Electronics in Agriculture. The major research themes identified include deep learning, convolutional neural networks, artificial neural networks, and forecasting models, with keywords such as regression, prediction, insects, population dynamics etc. frequently occurring in the literature. Current research increasingly focuses on leveraging statistical as well as advanced machine learning methodologies, including hybrid and ensemble techniques, aimed at enhancing the accuracy and efficiency of forecasting pest and disease outbreaks. This study not only provides valuable insights into the current landscape of crop pest and disease forecasting but also offers a foundational framework for future research endeavors.

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Unveiling Trends in Forecasting Models for Crop Pest and Disease Outbreaks: A Systematic and Scientometric Analysis

Goyal,

Singh,

Raghuraman

et al. 2024

Preprint

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Monitoring Helicoverpa armigera Damage with PRISMA Hyperspectral Imagery: First Experience in Maize and Comparison with Sentinel-2 Imagery

Sári-Barnácz,

Zalai,

Milics

et al. 2024

Remote Sensing

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The cotton bollworm (CBW) poses a significant risk to maize crops worldwide. This study investigated whether hyperspectral satellites offer an accurate evaluation method for monitoring maize ear damage caused by CBW larvae. The study analyzed the records of maize ear damage for four maize fields in Southeast Hungary, Csongrád-Csanád County, in 2021. The performance of Sentinel-2 bands, PRISMA bands, and synthesized Sentinel-2 bands was compared using linear regression, partial least squares regression (PLSR), and two-band vegetation index (TBVI) methods. The best newly developed indices derived from the TBVI method were compared with existing vegetation indices. In mid-early grain maize fields, narrow bands of PRISMA generally performed better than wide bands, unlike in sweet maize fields, where the Sentinel-2 bands performed better. In grain maize fields, the best index was the normalized difference of λA = 571 and λB = 2276 (R2 = 0.33–0.54, RMSE 0.06–0.05), while in sweet maize fields, the best-performing index was the normalized difference of green (B03) and blue (B02) Sentinel-2 bands (R2 = 0.54–0.72, RMSE 0.02). The findings demonstrate the advantages and constraints of remote sensing for plant protection and pest monitoring.

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Leveraging machine learning for advancing insect pest control: A bibliometric analysis

Cited by 2 publications

References 58 publications

Unveiling Trends in Forecasting Models for Crop Pest and Disease Outbreaks: A Systematic and Scientometric Analysis

Unveiling Trends in Forecasting Models for Crop Pest and Disease Outbreaks: A Systematic and Scientometric Analysis

Monitoring Helicoverpa armigera Damage with PRISMA Hyperspectral Imagery: First Experience in Maize and Comparison with Sentinel-2 Imagery

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