Early detection of rice blast using UAV hyperspectral imagery and multi-scale integrator selection attention transformer network (MS-STNet)

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 231, С. 110007 - 110007

Опубликована: Фев. 7, 2025

Язык: Английский

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Recognition of Multi-Symptomatic Rice Leaf Blast in Dual Scenarios by Using Convolutional Neural Networks

Smart Agricultural Technology, Год журнала: 2025, Номер unknown, С. 100867 - 100867

Опубликована: Фев. 1, 2025

Язык: Английский

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Integrating Stride Attention and Cross-Modality Fusion for UAV-Based Detection of Drought, Pest, and Disease Stress in Croplands

Agronomy, Год журнала: 2025, Номер 15(5), С. 1199 - 1199

Опубликована: Май 15, 2025

Timely and accurate detection of agricultural disasters is crucial for ensuring food security enhancing post-disaster response efficiency. This paper proposes a deployable UAV-based multimodal disaster framework that integrates multispectral RGB imagery to simultaneously capture the spectral responses spatial structural features affected crop regions. To this end, we design an innovative stride–cross-attention mechanism, in which stride attention utilized efficient feature extraction, while cross-attention facilitates semantic fusion between heterogeneous modalities. The experimental data were collected from representative wheat maize fields Inner Mongolia, using UAVs equipped with synchronized (red, green, blue, red edge, near-infrared) high-resolution sensors. Through combination image preprocessing, geometric correction, various augmentation strategies (e.g., MixUp, CutMix, GridMask, RandAugment), quality diversity training samples significantly enhanced. model trained on constructed dataset achieved accuracy 93.2%, F1 score 92.7%, precision 93.5%, recall 92.4%, substantially outperforming mainstream models such as ResNet50, EfficientNet-B0, ViT across multiple evaluation metrics. Ablation studies further validated critical role modules performance improvement. study demonstrates integration lightweight mechanisms UAV remote sensing enables efficient, accurate, scalable under complex field conditions.

Язык: Английский

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Fusion of UAV-Acquired Visible Images and Multispectral Data by Applying Machine-Learning Methods in Crop Classification

Agronomy, Год журнала: 2024, Номер 14(11), С. 2670 - 2670

Опубликована: Ноя. 13, 2024

The sustainable development of agriculture is closely related to the adoption precision techniques, and accurate crop classification a fundamental aspect this approach. This study explores application machine learning techniques by integrating RGB images multispectral data acquired UAVs. focused on five crops: rice, soybean, red bean, wheat, corn. To improve accuracy, researchers extracted three key feature sets: band values vegetation indices, texture features from grey-scale co-occurrence matrix, shape features. These were combined with models: random forest (RF), support vector (SVM), k-nearest neighbour (KNN) based, regression tree (CART) artificial neural network (ANN). results show that Random Forest model consistently outperforms other models, an overall accuracy (OA) over 97% significantly higher Kappa coefficient. Fusion improved 1–4% compared using single source. Our importance analysis showed indices had greatest impact results. provides comprehensive extraction evaluation, identifying optimal combination providing valuable insights for advancing through fusion techniques.

Язык: Английский

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Algorithms for Plant Monitoring Applications: A Comprehensive Review

Algorithms, Год журнала: 2025, Номер 18(2), С. 84 - 84

Опубликована: Фев. 5, 2025

Many sciences exploit algorithms in a large variety of applications. In agronomy, amounts agricultural data are handled by adopting procedures for optimization, clustering, or automatic learning. this particular field, the number scientific papers has significantly increased recent years, triggered scientists using artificial intelligence, comprising deep learning and machine methods bots, to process crop, plant, leaf images. Moreover, many other examples can be found, with different applied plant diseases phenology. This paper reviews publications which have appeared past three analyzing used classifying agronomic aims crops applied. Starting from broad selection 6060 papers, we subsequently refined search, reducing 358 research articles 30 comprehensive reviews. By summarizing advantages applying analyses, propose guide farming practitioners, agronomists, researchers, policymakers regarding best practices, challenges, visions counteract effects climate change, promoting transition towards more sustainable, productive, cost-effective encouraging introduction smart technologies.

Язык: Английский

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Enhancing domain adaptation for plant diseases detection through Masked Image Consistency in Multi-Granularity Alignment

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127101 - 127101

Опубликована: Март 1, 2025

Язык: Английский

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Estimation of Chlorophyll Content in Apple Leaves Infected with Mosaic Disease by Combining Spectral and Textural Information Using Hyperspectral Images

Remote Sensing, Год журнала: 2024, Номер 16(12), С. 2190 - 2190

Опубликована: Июнь 17, 2024

Leaf chlorophyll content (LCC) is an important indicator of plant nutritional status and can be a guide for disease diagnosis. In this study, we took apple leaves infected with mosaic as research object extracted two types information on spectral textural features from hyperspectral images, view to realizing non-destructive detection LCC. First, the collected images were preprocessed reflectance was in region interest. Subsequently, used successive projections algorithm (SPA) select optimal wavelengths (OWs) eight basic using gray-level co-occurrence matrix (GLCM). addition, composite metrics, including vegetation indices (VIs), normalized difference texture (NDTIs), (DTIs), ratio (RTIs) calculated. Third, applied maximal coefficient (MIC) significant VIs textures, well tandem method fuse features. Finally, employ support vector regression (SVR), backpropagation neural network (BPNN), K-nearest neighbors (KNNR) methods explore efficacy single combined feature models estimating The results showed that model (R2 = 0.8532, RMSE 2.1444, RPD 2.6179) NDTIs 0.7927, 2.7453, 2.2032) achieved best among spectra texture, respectively. However, generally exhibit inferior performance compared are unsuitable standalone applications. Combining potentially improve models. Specifically, when combining input parameters, three machine learning outperform model. Ultimately, SVR achieves highest LCC 0.8665, 1.8871, 2.7454). This study reveals improves quantitative disease, leading higher estimation accuracy.

Язык: Английский

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Revolutionizing UAV: Experimental Evaluation of IoT-Enabled Unmanned Aerial Vehicle-Based Agricultural Field Monitoring Using Remote Sensing Strategy

Remote Sensing in Earth Systems Sciences, Год журнала: 2024, Номер unknown

Опубликована: Сен. 13, 2024

Язык: Английский

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