A Multi-class Hybrid Variational Autoencoder and Vision Transformer Model for Enhanced Plant Disease Identification

Intelligent Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 200490 - 200490

Published: Feb. 1, 2025

Language: Английский

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Past, present and future of deep plant leaf disease recognition: A survey

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 234, P. 110128 - 110128

Published: March 12, 2025

Language: Английский

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Classification of infection grade for anthracnose in mango leaves under complex background based on CBAM-DBIRNet

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 260, P. 125343 - 125343

Published: Sept. 11, 2024

Language: Английский

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Comparative Result Analysis of Cauliflower Disease Classification Based on Deep Learning Approach VGG16, Inception v3, ResNet, and a Custom CNN Model

Hybrid Advances, Journal Year: 2025, Volume and Issue: unknown, P. 100440 - 100440

Published: March 1, 2025

Language: Английский

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Reduction of flavonoid content in honeysuckle via Erysiphe lonicerae-mediated inhibition of three essential genes in flavonoid biosynthesis pathways

Frontiers in Plant Science, Journal Year: 2024, Volume and Issue: 15

Published: April 16, 2024

Honeysuckle, valued for its wide-ranging uses in medicine, cuisine, and aesthetics, faces a significant challenge cultivation due to powdery mildew, primarily caused by the Erysiphe lonicerae pathogen. The interaction between honeysuckle E. , especially concerning disease progression, remains insufficiently understood. Our study, conducted three different locations, found that naturally infected with showed notable decreases total flavonoid content, reductions of 34.7%, 53.5%, 53.8% observed each respective site. Controlled experiments supported these findings, indicating artificial inoculation led 20.9% reduction levels over 21 days, worsening 54.8% decrease day 42. Additionally, there was drop plant’s antioxidant capacity, reaching an 81.7% 56 days after inoculation. Metabolomic analysis also revealed substantial essential medicinal components such as chlorogenic acid, luteolin, quercetin, isoquercetin, rutin. Investigating gene expression marked relative LjPAL1 gene, starting early 7 post-inoculation falling minimal level (fold change = 0.29) 35. This trend mirrored consistent phenylalanine ammonia-lyase activity through entire process, which decreased 72.3% 56. Further sustained repression downstream genes LjFNHO1 LjFNGT1 closely linked . We identified mechanism inhibits this pathway suggest may strategically weaken honeysuckle’s resistance targeting key biosynthetic pathways, thereby facilitating further pathogen invasion. Based on our we recommend two primary strategies: first, monitoring constituent from -affected areas ensure therapeutic effectiveness; second, emphasizing prevention control measures against mildew persistent decline crucial active compounds.

Language: Английский

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Incorporation of Histogram Intersection and Semantic Information into Non-Negative Local Laplacian Sparse Coding for Image Classification

Mathematics, Journal Year: 2025, Volume and Issue: 13(2), P. 219 - 219

Published: Jan. 10, 2025

Traditional sparse coding has proven to be an effective method for image feature representation in recent years, yielding promising results classification. However, it faces several challenges, such as sensitivity variations, code instability, and inadequate distance measures. Additionally, classification often operate independently, potentially resulting the loss of semantic relationships. To address these issues, a new is proposed, called Histogram intersection Semantic information-based Non-negativity Local Laplacian Sparse Coding (HS-NLLSC) This integrates Locality into (NLLSC) optimisation, enhancing stability ensuring that similar features are encoded codewords. In addition, histogram introduced redefine between vectors codebooks, effectively preserving their similarity. By comprehensively considering both processes classification, more information retained, thereby leading representation. Finally, multi-class linear Support Vector Machine (SVM) employed Experimental on four standard three maritime datasets demonstrate superior performance compared previous six algorithms. Specifically, accuracy our approach improved by 5% 19% methods. research provides valuable insights various stakeholders selecting most suitable specific circumstances.

Language: Английский

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Evaluation of Statistical Models of NDVI and Agronomic Variables in a Protected Agriculture System

Horticulturae, Journal Year: 2025, Volume and Issue: 11(2), P. 131 - 131

Published: Jan. 26, 2025

Vegetable production in intensive protected agriculture systems has evolved due to its intensity and economic importance. Sensors are increasingly common for decision-making crop management control of environmental variables, obtaining optimal yields, such as estimating vegetation indices. Innovation technological advances unmanned vehicle platforms have improved spatial, spectral, temporal resolution. However, systems, the use is limited assumption having controlled conditions indeterminate vegetable production. Therefore, sequential monitoring NDVI proposed during 2022 2023 agricultural cycles using Green Seeker® sensor agronomic variables. This created a database generate predictive models development yield function nutrient status. The results obtained indicate high significance levels curves all phenological stages; contrast models, this maximum values (close one) recorded inside greenhouse comparison prediction from 18th week harvest. Evaluating between variables not an index that offers certainty predicting crops systems. constant response conditions, status, water supply greenhouse, without sustainability yield, which decreases final stages until becomes economically unprofitable.

Language: Английский

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Cf-Wiad: Consistency Fusion with Weighted Instance and Adaptive Distribution for Enhanced Semi-Supervised Skin Lesion Classification

Published: Jan. 1, 2025

Language: Английский

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Revolutionizing Agriculture With Automated Plant Disease Detection

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 267 - 296

Published: Feb. 7, 2025

Automated plant disease detection using computer vision has transformed agriculture by addressing challenges in health management, productivity, and sustainability. This chapter explores advancements from traditional methods to AI-enhanced deep learning multi-modal imaging, enabling early detection, real-time processing, precise interventions. Applications like precision agriculture, IoT integration, data-driven decision-making foster eco-friendly practices resource efficiency. Despite such as data quality, scalability, accessibility, future innovations collection, sustainable hardware, collaboration promise shape resilient agricultural systems. By aligning technology with sustainability, automated supports food security, environmental conservation, the evolution of modern farming practices.

Language: Английский

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3DVT: Hyperspectral Image Classification Using 3D Dilated Convolution and Mean Transformer

Photonics, Journal Year: 2025, Volume and Issue: 12(2), P. 146 - 146

Published: Feb. 11, 2025

Hyperspectral imaging and laser technology both rely on different wavelengths of light to analyze the characteristics materials, revealing their composition, state, or structure through precise spectral data. In hyperspectral image (HSI) classification tasks, limited number labeled samples lack feature extraction diversity often lead suboptimal performance. Furthermore, traditional convolutional neural networks (CNNs) primarily focus local features in data, neglecting long-range dependencies global context. To address these challenges, this paper proposes a novel model that combines CNNs with an average pooling Vision Transformer (ViT) for classification. The utilizes three-dimensional dilated convolution two-dimensional extract multi-scale spatial–spectral features, while ViT was employed capture Unlike encoder, which uses linear projection, our replaces it projection. This change enhances compensates encoder’s limitations extraction. hybrid approach effectively strengths dependency handling capabilities Transformers, significantly improving overall performance tasks. Additionally, proposed method holds promise fiber spectra, where high precision analysis are crucial distinguishing between characteristics. Experimental results demonstrate CNN-Transformer substantially improves accuracy three benchmark datasets. accuracies achieved public datasets—IP, PU, SV—were 99.35%, 99.31%, 99.66%, respectively. These advancements offer potential benefits wide range applications, including high-performance optical sensing, medicine, environmental monitoring, accurate is essential development advanced systems fields such as medicine technology.

Language: Английский

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