Rice pest dataset supports the construction of smart farming systems

Quach, Luyl-Da; Nguyen, Quoc Khang; Nguyen, Quynh Anh; Lan, Le Thi Thu

doi:10.1016/j.dib.2024.110046

Cited by 2 publications

(1 citation statement)

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“…In the realm of agricultural pest detection and classification, researchers have leveraged a variety of datasets to train and test machine learning models in the previous studies, such as the Rice Pest Image Dataset, the PlantVillage Dataset and the Agricultural Pest Dataset (APD) [7][8][9]. Despite advancements in previous plant pest identification research, existing limitations persist.…”

Section: Introductionmentioning

confidence: 99%

An Advancing GCT-Inception-ResNet-V3 Model for Arboreal Pest Identification

Li,

Tian,

Tian

et al. 2024

Agronomy

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The significance of environmental considerations has been highlighted by the substantial impact of plant pests on ecosystems. Addressing the urgent demand for sophisticated pest management solutions in arboreal environments, this study leverages advanced deep learning technologies to accurately detect and classify common tree pests, such as “mole cricket”, “aphids”, and “Therioaphis maculata (Buckton)”. Through comparative analysis with the baseline model ResNet-18 model, this research not only enhances the SE-RegNetY and SE-RegNet models but also introduces innovative frameworks, including GCT-Inception-ResNet-V3, SE-Inception-ResNet-V3, and SE-Inception-RegNetY-V3 models. Notably, the GCT-Inception-ResNet-V3 model demonstrates exceptional performance, achieving a remarkable average overall accuracy of 94.59%, average kappa coefficient of 91.90%, average mAcc of 94.60%, and average mIoU of 89.80%. These results signify substantial progress over conventional methods, outperforming the baseline model’s results by margins of 9.1%, nearly 13.7%, 9.1%, and almost 15% in overall accuracy, kappa coefficient, mAcc, and mIoU, respectively. This study signifies a considerable step forward in blending sustainable agricultural practices with environmental conservation, setting new benchmarks in agricultural pest management. By enhancing the accuracy of pest identification and classification in agriculture, it lays the groundwork for more sustainable and eco-friendly pest control approaches, offering valuable contributions to the future of agricultural protection.

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Section: Introductionmentioning

confidence: 99%