Enhancing Corn Pest and Disease Recognition through Deep Learning: A Comprehensive Analysis

Xu, Wenqing; Li, Weikai; Wang, Liwei; Pompelli, Marcelo F.

doi:10.3390/agronomy13092242

Cited by 10 publications

(2 citation statements)

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“…This study presents two primary innovations: (1) The utilization of the CycleGAN for generating compound disease images in maize leaves addresses the issue of limited availability of data for compound disease, which is insufficient to support the data requirements of large-scale deep learning. (2) The incorporation of attention mechanisms enhances the network model's focus on the lesion targets, mitigating the interference caused by compound diseases and improving the accuracy of disease recognition.…”

Section: Innovation and Limitationsmentioning

confidence: 99%

“…Maize, as a primary staple crop in China, serves as a crucial source of feed for the livestock and aquaculture industries, as well as an indispensable raw material for the medical, hygiene, and chemical sectors. Ensuring both the yield and quality of maize holds significant importance [1]. During the growth and development stages of maize, leaf diseases frequently occur, and the lack of timely prevention and control measures can lead to a reduction in the final yield and quality of maize [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Maize Leaf Compound Disease Recognition Based on Attention Mechanism

Dong,

Li,

Wang

et al. 2023

Agriculture

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In addition to the conventional situation of detecting a single disease on a single leaf in corn leaves, there is a complex phenomenon of multiple diseases overlapping on a single leaf (compound diseases). Current research on corn leaf disease detection predominantly focuses on single leaves with single diseases, with limited attention given to the detection of compound diseases on a single leaf. However, the occurrence of compound diseases complicates the accuracy of traditional deep learning algorithms for disease detection, necessitating the exploration of new models for the identification of compound diseases on corn leaves. To achieve rapid and accurate identification of compound diseases in corn fields, this study adopts the YOLOv5s model as the base network, chosen for its smaller size and faster detection speed. We propose a corn leaf compound disease recognition method, YOLOv5s-C3CBAM, based on an attention mechanism. To address the challenge of limited data for corn leaf compound diseases, a CycleGAN model is employed to generate synthetic images. The scarcity of real data is thereby mitigated, facilitating the training of deep learning models with sufficient data. The YOLOv5s model is selected as the base network, and an attention mechanism is introduced to enhance the network’s focus on disease lesions while mitigating interference from compound diseases. This augmentation results in improved recognition accuracy. The YOLOv5s-C3CBAM compound disease recognition model, incorporating the attention mechanism, achieves an average precision of 83%, an F1 score of 81.98%, and a model size of 12.6 Mb. Compared to the baseline model, the average precision is improved by 3.1 percentage points. Furthermore, it outperforms Faster R-CNN and YOLOv7-tiny models by 27.57 and 2.7 percentage points, respectively. This recognition method demonstrates the ability to rapidly and accurately identify compound diseases on corn leaves, offering valuable insights for future research on precise identification of compound agricultural crop diseases in field conditions.

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Section: Innovation and Limitationsmentioning

confidence: 99%