Algorithms for Plant Monitoring Applications: A Comprehensive Review

Algorithms, Journal Year: 2025, Volume and Issue: 18(2), P. 84 - 84

Published: Feb. 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.

Language: Английский

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

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 231, P. 110007 - 110007

Published: Feb. 7, 2025

Language: Английский

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

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100867 - 100867

Published: Feb. 1, 2025

Language: Английский

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

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127101 - 127101

Published: March 1, 2025

Language: Английский

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

Agronomy, Journal Year: 2024, Volume and Issue: 14(11), P. 2670 - 2670

Published: Nov. 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.

Language: 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, Journal Year: 2024, Volume and Issue: 16(12), P. 2190 - 2190

Published: June 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.

Language: 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, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 13, 2024

Language: Английский

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