A Systematic Review on Automatic Insect Detection Using Deep Learning

Agriculture, Journal Year: 2023, Volume and Issue: 13(3), P. 713 - 713

Published: March 19, 2023

Globally, insect pests are the primary reason for reduced crop yield and quality. Although pesticides commonly used to control eliminate these pests, they can have adverse effects on environment, human health, natural resources. As an alternative, integrated pest management has been devised enhance control, decrease excessive use of pesticides, output quality crops. With improvements in artificial intelligence technologies, several applications emerged agricultural context, including automatic detection, monitoring, identification insects. The purpose this article is outline leading techniques automated detection insects, highlighting most successful approaches methodologies while also drawing attention remaining challenges gaps area. aim furnish reader with overview major developments field. This study analysed 92 studies published between 2016 2022 insects traps using deep learning techniques. search was conducted six electronic databases, 36 articles met inclusion criteria. criteria were that applied classification, counting, written English. selection process involved analysing title, keywords, abstract each study, resulting exclusion 33 articles. included 12 classification task 24 task. Two main approaches—standard adaptable—for identified, various architectures detectors. accuracy found be influenced by dataset size, significantly affected number classes size. highlights two recommendations, namely, characteristics (such as unbalanced incomplete annotation) limitations algorithms small objects lack information about insects). To overcome challenges, further research recommended improve practices. should focus addressing identified ensure more effective management.

Language: Английский

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Human-Centered AI in smart farming: Towards Agriculture 5.0

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 62199 - 62214

Published: Jan. 1, 2024

Language: Английский

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Harnessing quantum computing for smart agriculture: Empowering sustainable crop management and yield optimization

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 218, P. 108680 - 108680

Published: Feb. 10, 2024

Language: Английский

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Plant disease recognition in a low data scenario using few-shot learning

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 219, P. 108812 - 108812

Published: March 4, 2024

Plant disease is one of the major problems in agriculture. Diseases damage plants, reduce yields and lower quality produce. Traditional approaches to detecting plant diseases are usually based on visual inspection laboratory testing, which can be expensive time-consuming. They require trained pathologists as well specialised equipment. Several studies demonstrate that artificial intelligence (AI) methods produce promising results. However, AI generally data-hungry large annotated datasets, collection annotation such datasets a limiting factor. It often appears only small amount data available for certain types. Whereas performance typical drops significantly when they with inadequate data. This paper proposes novel few-shot learning (FSL) method detect alleviate scarcity problem. The proposed uses few five images per class machine process. Our state-of-the-art FSL pipeline called pre-training, meta-learning, fine-tuning (PMF), integrated feature attention (FA) module; we call overall PMF+FA. FA module emphasises discriminative parts image reduces impact complicated backgrounds undesired objects. We used ResNet50 Vision Transformers (ViT) learner. Two publicly were repurposed meet requirements. thoroughly evaluated PlantDoc dataset, contains samples field environments complex unwanted PMF+FA ViT achieved an average accuracy 90.12% recognition. results consistently outperforms baseline PMF. also highlight using generates better than diagnosing implementations computationally efficient, taking 1.11 0.57 ms evaluate test set respectively. high throughput high-quality training dataset indicate technique real-time detection digital farming systems.

Language: Английский

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PestLite: A Novel YOLO-Based Deep Learning Technique for Crop Pest Detection

Agriculture, Journal Year: 2024, Volume and Issue: 14(2), P. 228 - 228

Published: Jan. 31, 2024

Timely and effective pest detection is essential for agricultural production, facing challenges such as complex backgrounds a vast number of parameters. Seeking solutions has become pressing matter. This paper, based on the YOLOv5 algorithm, developed PestLite model. The model surpasses previous spatial pooling methods with our uniquely designed Multi-Level Spatial Pyramid Pooling (MTSPPF). Using lightweight unit, it integrates convolution, normalization, activation operations. It excels in capturing multi-scale features, ensuring rich extraction key information at various scales. Notably, MTSPPF not only enhances accuracy but also reduces parameter size, making ideal models. Additionally, we introduced Involution Efficient Channel Attention (ECA) attention mechanisms to enhance contextual understanding. We replaced traditional upsampling Content-Aware ReAssembly FEatures (CARAFE), which enable achieve higher mean average precision detection. Testing dataset showed improved while reducing size. mAP50 increased from 87.9% 90.7%, count decreased 7.03 M 6.09 M. further validated using IP102 dataset, other hand, conducted comparisons mainstream Furthermore, visualized targets. results indicate that provides an solution real-time target pests.

Language: Английский

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Research on Small-Target Detection of Flax Pests and Diseases in Natural Environment by Integrating Similarity-Aware Activation Module and Bidirectional Feature Pyramid Network Module Features

Agronomy, Journal Year: 2025, Volume and Issue: 15(1), P. 187 - 187

Published: Jan. 14, 2025

In the detection of pests and diseases flax, early wilt disease is elusive, yellow leaf symptoms are easily confusing, pest hampered by issues such as diversity in species, difficulty detection, technological bottlenecks, posing significant challenges to efforts. To address these issues, this paper proposes a flax method based on an improved YOLOv8n model. enhance accuracy generalization capability model, first employs Albumentations library for data augmentation, which strengthens model’s adaptability complex environments enriching training samples. Secondly, terms model architecture, Bidirectional Feature Pyramid Network (BiFPN) module introduced replace original feature extraction network. Through bidirectional multi-scale fusion, ability distinguish with similar features large scale differences effectively improved. Meanwhile, integration SimAM attention mechanism enables learn information from three-dimensional channels, enhancing its perception features. Additionally, adopts EIOU loss function further optimize bounding box regression, reducing distortion boxes caused high sample variability. The experimental results demonstrate that achieves performance dataset, notable improvements mean average precision compared Finally, four-headed design, significantly enhances small targets size 4 × pixels or larger introducing new heads optimizing extraction. This not only improves but also maintains computational efficiency, providing effective technical support rapid precise possessing important practical application value.

Language: Английский

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Detection of citrus black spot disease and ripeness level in orange fruit using learning-to-augment incorporated deep networks

Ecological Informatics, Journal Year: 2022, Volume and Issue: 71, P. 101829 - 101829

Published: Sept. 24, 2022

Language: Английский

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Research on CBF-YOLO detection model for common soybean pests in complex environment

Computers and Electronics in Agriculture, Journal Year: 2023, Volume and Issue: 216, P. 108515 - 108515

Published: Dec. 19, 2023

Language: Английский

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Improved Pest-YOLO: Real-time pest detection based on efficient channel attention mechanism and transformer encoder

Ecological Informatics, Journal Year: 2023, Volume and Issue: 78, P. 102340 - 102340

Published: Oct. 20, 2023

Language: Английский

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Field detection of small pests through stochastic gradient descent with genetic algorithm

Computers and Electronics in Agriculture, Journal Year: 2023, Volume and Issue: 206, P. 107694 - 107694

Published: Feb. 14, 2023

Language: Английский

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