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

Agronomy, Год журнала: 2024, Номер 14(9), С. 1975 - 1975

Опубликована: Сен. 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

Язык: Английский

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Prediction of sugar beet yield and quality parameters using stacked-LSTM model with pre-harvest UAV time series data and meteorological factors

Artificial Intelligence in Agriculture, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

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Detecting Tasseling Rate of Breeding Maize Using UAV-based RGB Images and STB-YOLO Model

Smart Agricultural Technology, Год журнала: 2025, Номер unknown, С. 100893 - 100893

Опубликована: Март 1, 2025

Язык: Английский

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Precipitation nowcasting using transformer-based generative models and transfer learning for improved disaster preparedness

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 132, С. 103962 - 103962

Опубликована: Июль 1, 2024

Due to the rapidly changing climate conditions, precipitation nowcasting poses a daunting challenge because it is impossible make accurate short-term forecasts due rapid fluctuations in weather conditions. There are limitations traditional methods of forecasting precipitation, such as use numerical models and radar extrapolation, when comes providing highly detailed timely forecasts. With help contemporary machine learning (ML) models, including deep neural networks, transformers generative complex tasks can be performed an efficient way. To address this critical task enhance proactive emergency disaster management, we propose innovative method based on transformer-based for nowcasting. Our study area Soyang Dam basin South Korea, located upstream Han River, characterized by monsoon with approximately 1200 mm annual precipitation. develop model, composite data from 10 radars across Korea used. By utilizing reflective order train our able effectively predict future patterns, thus mitigating risk catastrophic conditions caused heavy rainfalls. This dataset covers reflectivity 2018 2022, spatial resolution 1km over 960 × grid. Normalization using min–max scaler applied data, which then transformed into grayscale images uniform comparison. We performance employing transfer pre-trained Transformer models. Initially, model comprehensive dataset. Subsequently, fine-tune data. adaptation improves accuracy rainfall capturing crucial features. Leveraging prior knowledge through not only enhances prediction but also increases overall efficiency. In terms predictive accuracy, extensive experimental results demonstrate that outperforms related approaches, conditional adversarial networks (cGANs), U-Net, convolutional long memory (ConvLSTM), pySTEP. As result research, preparedness response will greatly improved prediction.

Язык: Английский

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Estimating Leaf Chlorophyll Content of Winter Wheat from UAV Multispectral Images Using Machine Learning Algorithms under Different Species, Growth Stages, and Nitrogen Stress Conditions

Agriculture, Год журнала: 2024, Номер 14(7), С. 1064 - 1064

Опубликована: Июль 1, 2024

The rapid and accurate estimation of leaf chlorophyll content (LCC), an important indicator crop photosynthetic capacity nutritional status, is great significance for precise nitrogen fertilization management. To explore the existence a versatile regression model that can be successfully used to estimate LCC different varieties under growth stages stress conditions, study was conducted in 2023 across growing season winter wheat with five species application levels. Two machine learning algorithms, support vector (SVM) random forest (RF), were establish bridge between UAV-derived multispectral vegetation indices ground truth (relative content, SPAD), taking multivariate linear (MLR) algorithm as reference. results show visible atmospherically resistant index, vegetative normalized difference index had highest correlation LCC, Pearson’s coefficient 0.95. All three algorithms (MLR, RF, SVM) performed well on training dataset (R2: 0.932–0.944, RMSE: 3.96 4.37), but differently validation datasets stages, species, Compared levels, greatest influence generalization ability models, especially dough stage. At stage, compared MLR SVM best, R2 increasing by 0.27 0.10, respectively, RMSE decreasing 1.13 0.46, respectively. Overall, this demonstrated combination VIs could applied map conditions. Ultimately, research significant it shows successful UAV data mapping diverse offering valuable insights precision

Язык: Английский

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Random forest machine learning for maize yield and agronomic efficiency prediction in Ghana

Heliyon, Год журнала: 2024, Номер 10(17), С. e37065 - e37065

Опубликована: Авг. 28, 2024

Highlights•Random forest modelling of maize yield in Ghana was successful and explained 81 % the variance.•Random agronomic efficiency less accurate than for between 53 63 variance.•Soil variables were more important climate other environmental predicting yield.•The random model can guide development fertilizer recommendations sustainable production.AbstractMaize (Zea mays) is an staple crop food security Sub-Saharan Africa. However, there need to increase production feed a growing population. In Ghana, this mainly done by increasing acreage with adverse consequences, rather increment per unit area. Accurate prediction yields nutrient use critical making informed decisions toward economic ecological sustainability. We trained machine learning algorithm predict using soil, climate, environment, management factors, including application. calibrated evaluated performance 5 × 10-fold nested cross-validation approach. Data from 482 field trials consisting 3136 georeferenced treatment plots conducted 1991 2020 used train algorithm, identify predictor variables, quantify uncertainties associated predictions. The mean error, root squared coefficient 90 interval coverage probability calculated. results obtained on test data demonstrate good (MEC = 0.81) moderate 0.63, 0.55 0.54 AE-N, AE-P AE-K, respectively). found that climatic predictors soil prediction, but temperature key importance rainfall efficiency. developed models provided better understanding drivers tropical insight towards improving

Язык: Английский

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Evaluation of Machine Learning Regression Techniques for Estimating Winter Wheat Biomass Using Biophysical, Biochemical, and UAV Multispectral Data

Drones, Год журнала: 2024, Номер 8(7), С. 287 - 287

Опубликована: Июнь 26, 2024

Crop above-ground biomass (AGB) estimation is a critical practice in precision agriculture (PA) and vital for monitoring crop health predicting yields. Accurate AGB allows farmers to take timely actions maximize yields within given growth season. The objective of this study use unmanned aerial vehicle (UAV) multispectral imagery, along with derived vegetation indices (VI), plant height, leaf area index (LAI), nutrient content ratios, predict the dry (g/m2) winter wheat field southwestern Ontario, Canada. This assessed effectiveness Random Forest (RF) Support Vector Regression (SVR) models ABG from 42 variables. RF consistently outperformed SVR models, top-performing model utilizing 20 selected variables based on their contribution increasing node purity decision trees. achieved an R2 0.81 root mean square error (RMSE) 149.95 g/m2. Notably, included combination MicaSense bands, VIs, levels, height. significantly all other that relied solely UAV data or content. insights gained can enhance management AGB, leading more effective yield predictions management.

Язык: Английский

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Unmanned Aerial Vehicle-Measured Multispectral Vegetation Indices for Predicting LAI, SPAD Chlorophyll, and Yield of Maize

Agriculture, Год журнала: 2024, Номер 14(7), С. 1110 - 1110

Опубликована: Июль 9, 2024

Predicting crop yield at preharvest is pivotal for agricultural policy and strategic decision making. Despite global targets, labour-intensive surveys estimation pose challenges. Using unmanned aerial vehicle (UAV)-based multispectral sensors, this study assessed phenology biotic stress conditions using various spectral vegetation indices. The goal was to enhance the accuracy of predicting key parameters, such as leaf area index (LAI), soil plant analyser development (SPAD) chlorophyll, grain maize. study’s findings demonstrate that during kharif season, wide dynamic range (WDRVI) showcased superior correlation coefficients (R), determination (R2), lowest root mean square errors (RMSEs) 0.92, 0.86, 0.14, respectively. However, rabi atmospherically resistant (ARVI) achieved highest R R2 RMSEs 0.83, 0.79, 0.15, respectively, indicating better in LAI. Conversely, normalised difference red-edge (NDRE) season modified chlorophyll absorption ratio (MCARI) were identified predictors with SPAD prediction. Specifically, values 0.91 0.94, 0.83 0.82, RMSE 2.07 3.10 obtained, most effective indices LAI prediction (WDRVI NDRE) (ARVI MCARI) further utilised construct a model stepwise regression analysis. Integrating predicted into resulted higher compared individual predictions. More exactly, 0.51 0.74, while 9.25 6.72, seasons, These underscore utility UAV-based imaging yields, thereby aiding sustainable management practices benefiting farmers policymakers alike.

Язык: Английский

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Ground-Based Hyperspectral Estimation of Maize Leaf Chlorophyll Content Considering Phenological Characteristics

Remote Sensing, Год журнала: 2024, Номер 16(12), С. 2133 - 2133

Опубликована: Июнь 13, 2024

Accurately measuring leaf chlorophyll content (LCC) is crucial for monitoring maize growth. This study aims to rapidly and non-destructively estimate the LCC during four critical growth stages investigate ability of phenological parameters (PPs) LCC. First, spectra were obtained by spectral denoising followed transformation. Next, sensitive bands (Rλ), indices (SIs), PPs extracted from all at each stage. Then, univariate models constructed determine their potential independent estimation. The multivariate regression (LCC-MR) built based on SIs, SIs + Rλ, Rλ after feature variable selection. results indicate that our machine-learning-based LCC-MR demonstrated high overall accuracy. Notably, 83.33% 58.33% these showed improved accuracy when successively introduced SIs. Additionally, model accuracies milk-ripe tasseling outperformed those flare–opening jointing under identical conditions. optimal was created using XGBoost, incorporating SI, PP variables R3 These findings will provide guidance support management.

Язык: Английский

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An improved deep learning approach for detection of maize tassels using UAV-based RGB images

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 130, С. 103922 - 103922

Опубликована: Май 22, 2024

The emergence of maize tassels is the turning vegetative stage to reproductive (Zea mays L.), which critical for estimating grain yields. Recent advances in unmanned aerial vehicles (UAVs) remote sensing and deep learning-based object detection technique have provided a new approach detecting tassels. Meanwhile, there still exists challenges accurate due uncertainties complex field environment. existing networks fall accurately overlapping or small-scale tassels, as well exhibiting insufficient capability strong lighting conditions. Furthermore, current dataset exhibits limited temporal scope, unable encompass whole tasseling progress. In this study, we proposed FMTS dataset, designed novel called RESAM-YOLOv8n (Residual Spatial Attention Module-You Only Look Once v8n), introducing RESAM module training network with larger input image sizes. These enabled focus on important tassel features neglect irrelevant information, thereby enhancing its capability. was trained evaluated using mAP0.5, mAP0.75, Recall, Precision, F1 were 95.74 %, 66.70 89.28 95.59 92.00 respectively. counting number R2 value between network's ground truth reached 0.976, low RMSE 1.56 results showed better performance network, providing an effective method identifying

Язык: Английский

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