Integration of Remote Sensing and Machine Learning for Precision Agriculture: A Comprehensive Perspective on Applications

Agronomy, Journal Year: 2024, Volume and Issue: 14(9), P. 1975 - 1975

Published: Sept. 1, 2024

Due to current global population growth, resource shortages, and climate change, traditional agricultural models face major challenges. Precision agriculture (PA), as a way realize the accurate management decision support of production processes using modern information technology, is becoming an effective method solving these In particular, combination remote sensing technology machine learning algorithms brings new possibilities for PA. However, there are relatively few comprehensive systematic reviews on integrated application two technologies. For this reason, study conducts literature search Web Science, Scopus, Google Scholar, PubMed databases analyzes in PA over last 10 years. The found that: (1) because their varied characteristics, different types data exhibit significant differences meeting needs PA, which hyperspectral most widely used method, accounting more than 30% results. UAV offers greatest potential, about 24% data, showing upward trend. (2) Machine displays obvious advantages promoting development vector algorithm 20%, followed by random forest algorithm, 18% methods used. addition, also discusses main challenges faced currently, such difficult problems regarding acquisition processing high-quality model interpretation, generalization ability, considers future trends, intelligence automation, strengthening international cooperation sharing, sustainable transformation achievements. summary, can provide ideas references combined with promote

Language: Английский

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Generalist Pests Cause High Tree Infestation, but Specialist Pests Cause High Mortality

Forests, Journal Year: 2025, Volume and Issue: 16(1), P. 127 - 127

Published: Jan. 11, 2025

Whether specialist pests can cause more damage to their host plants than generalist is a critical issue in both basic biology and nonnative species management. To date, there no consensus on how we define “specialist vs. generalist” should assess forest or impacts (volume loss mortality). Here, comparatively investigate whether may US forests using two frameworks: (1) the “binary dichotomous approach” through largely arbitrary classification of pests, (2) “specialist-generalist continuum”. We measure impact ways, one by total volume infested other mortality. In binary comparison, generalists tree per pest specialists, but latter (mostly pathogens) caused higher mortality trees. The continuum” concept could reveal different pattern regarding invasions when clear separation between specialists community region. Therefore, suggest “continuum” approach address related questions future studies, thus offering new insights into that have deeper implications for monitoring

Language: Английский

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Mapping the research landscape on forest insects: bibliometric approach from 2010 to 2024

Published: April 8, 2025

Abstract This study presents a bibliometric analysis of forest insect research from 2010 to 2024, utilizing dataset 12,822 publications extracted 2319 journals. The annual growth rate was 4.43%, with an average citation impact 19.39 per article. highest output recorded in 2021 (1144 articles), followed by slight decline subsequent years. Key contributing authors included Jactel H (78 publications, 14.56 fractionalized score), JR (75, 12.70), and Liebhold AM (58, 13.59). Institutional revealed that the USDA Forest Service (385 publications), Beijing Forestry University (351), Swedish Agricultural Sciences (341) were leading institutions. Keyword co-occurrence identified Climate change as most frequently occurring term, indicating its central role entomology research. Network strong collaborative linkages, Raffa KF emerging key influencers. Geographic distribution indicated China, United States, Germany, Brazil significant contributors, States serving primary hub for international collaborations. Thematic evolution showed transition ecological taxonomic studies (2010–2015) integration advanced methodologies, including remote sensing machine learning pest management (2021–2024). These findings provide insights into trends, knowledge distribution, frontiers studies. Graphical

Language: Английский

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Microbial control of forest insect pests over 60 years (1964–2024): Network analysis and bibliometric mapping

Journal of Natural Pesticide Research, Journal Year: 2025, Volume and Issue: 12, P. 100132 - 100132

Published: April 29, 2025

Language: Английский

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Research progress in surface water quality monitoring based on remote sensing technology

International Journal of Remote Sensing, Journal Year: 2024, Volume and Issue: 45(7), P. 2337 - 2373

Published: March 21, 2024

Urban surface water is an important freshwater resource, and the environment increasingly being destroyed. Dynamic monitoring of great significance for protecting ecological environment. Remote sensing technology provides technical support monitoring, which overcomes drawbacks traditional manual sampling. It has been widely applied in monitoring. The paper systematically reviews research progress remote from aspects data, inversion models quality parameters. Advantages disadvantages (analytical methods, empirical semi-empirical machine learning methods comprehensive methods) are compared analysed. Furthermore, we summarize chlorophyll a (Chl-a), total suspended matter (TSM), coloured dissolved organic (CDOM), transparency non-photosensitive Although new ideas there still some problems that need to be solved, such as signals affected by atmosphere, poor portability models, low resolution satellite sensors, susceptibility external factors. Therefore, future should combine multi-source conduct in-depth on optical characteristics bodies, optimize construct transferable break through temporal spatial limitations, promote rapid development pollution warning.

Language: Английский

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Identification of High-Photosynthetic-Efficiency Wheat Varieties Based on Multi-Source Remote Sensing from UAVs

Agronomy, Journal Year: 2024, Volume and Issue: 14(10), P. 2389 - 2389

Published: Oct. 16, 2024

Breeding high-photosynthetic-efficiency wheat varieties is a crucial link in safeguarding national food security. Traditional identification methods necessitate laborious on-site observation and measurement, consuming time effort. Leveraging unmanned aerial vehicle (UAV) remote sensing technology to forecast photosynthetic indices opens up the potential for swiftly discerning varieties. The objective of this research develop multi-stage predictive model encompassing nine indicators at field scale breeding. These include soil plant analyzer development (SPAD), leaf area index (LAI), net rate (Pn), transpiration (Tr), intercellular CO2 concentration (Ci), stomatal conductance (Gsw), photochemical quantum efficiency (PhiPS2), PSII reaction center excitation energy capture (Fv’/Fm’), quenching coefficient (qP). ultimate goal differentiate through model-based predictions. This gathered red, green, blue spectrum (RGB) multispectral (MS) images eleven stages jointing, heading, flowering, filling. Vegetation (VIs) texture features (TFs) were extracted as input variables. Three machine learning regression models (Support Vector Machine Regression (SVR), Random Forest (RF), BP Neural Network (BPNN)) employed construct across multiple growth stages. Furthermore, conducted principal component analysis (PCA) membership function on predicted values optimal each indicator, established comprehensive evaluation high efficiency, cluster screen test materials. categorized into three groups, with SH06144 Yannong 188 demonstrating higher efficiency. moderately efficient group comprises Liangxing 19, SH05604, SH06085, Chaomai 777, SH05292, Jimai 22, Guigu 820, totaling seven Xinmai 916 Jinong 114 fall category lower aligning closely results clustering based actual measurements. findings suggest that employing UAV-based multi-source identify feasible. study provide theoretical basis winter phenotypic monitoring breeding using sensing, offering valuable insights advancement smart practices

Language: Английский

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Charting the evolution: bibliometric perspectives on anomaly detection within hyperspectral domains

Published: April 19, 2024

Language: Английский

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Sustainable Plant Protection Measures in Regenerative Farming

Published: Jan. 1, 2024

Language: Английский

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Sentinel-2A Image Reflectance Simulation Method for Estimating the Chlorophyll Content of Larch Needles with Pest Damage

Forests, Journal Year: 2024, Volume and Issue: 15(11), P. 1901 - 1901

Published: Oct. 28, 2024

With the development of remote sensing technology, estimation chlorophyll content (CHLC) vegetation via satellite data has become an important means monitoring health, and high-precision been focus research in this field. In study, we used larch affected by Yarl’s looper (Erannis jacobsoni Djak) boundary region Mongolia as object, simulated multispectral reflectance, downscaled Sentinel-2A data, performed mixed-pixel decomposition, analyzed potential for estimating calculating spectral indices (SIs) derivatives (SDFs) images, then extracted sensitive features model training set. Spectral to were establish set, and, finally, was constructed on basis partial least squares algorithm (PLSR). The results revealed that SI SDF based highly under influence pests, with SAVI EVI2 well D_B2 D_B5 being most content. models significantly better than without terms accuracy, especially those SDF-PLSR. reflectance reflected characteristics canopy damaged larch, green light, red edge, near-infrared bands. proposed approach improves accuracy enhances ability monitor changes complex forest conditions through simulations, providing new technical a theoretical forestry pest health management.

Language: Английский

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