The Application of Deep Learning in the Whole Potato Production Chain: A Comprehensive Review

Agriculture, Journal Year: 2024, Volume and Issue: 14(8), P. 1225 - 1225

Published: July 25, 2024

The potato is a key crop in addressing global hunger, and deep learning at the core of smart agriculture. Applying (e.g., YOLO series, ResNet, CNN, LSTM, etc.) production can enhance both yield economic efficiency. Therefore, researching efficient models for great importance. Common application areas chain, aimed improving yield, include pest disease detection diagnosis, plant health status monitoring, prediction product quality detection, irrigation strategies, fertilization management, price forecasting. main objective this review to compile research progress various processes provide direction future research. Specifically, paper categorizes applications into four types, thereby discussing introducing advantages disadvantages aforementioned fields, it discusses directions. This provides an overview describes its current stages chain.

Language: Английский

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Time series forecasting of bed occupancy in mental health facilities in India using machine learning

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 21, 2025

Machine learning models are vital for forecasting and optimizing healthcare parameters, especially in the context of rising mental health issues India globally. With increasing demand services, effective resource management, like bed occupancy forecasting, is crucial to ensure proper patient care reduce burden on facilities. This study applies six machine models, namely Support Vector Regression, eXtreme Gradient Boosting, Random Forest, K-Nearest Neighbors, Decision Tree, forecast weekly second largest hospital India, using data from 2008 2024. Accuracy were evaluated Mean Absolute Percentage Error, Diebold–Mariano test assessing differences predictive performance. Further, we occupancy, providing insights administrators capacity planning allocation, supporting data-driven decisions enhancing quality services India.

Language: Английский

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Potato Harvesting Prediction Using an Improved ResNet-59 Model

Potato Research, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 1, 2024

Language: Английский

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Exogenous variable driven deep learning models for improved price forecasting of TOP crops in India

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: July 26, 2024

Accurately predicting agricultural commodity prices is crucial for India's economy. Traditional parametric models struggle with stringent assumptions, while machine learning (ML) approaches, though data-driven, lack automatic feature extraction. Deep (DL) models, advanced extraction and predictive abilities, offer a promising solution. However, their application to price data ignored the exogenous factors. Hence, study explored versions of well-known univariate NBEATSX TransformerX. The research employed essential crops like Tomato, Onion, Potato (TOP) from major Indian markets complemented it corresponding weather (precipitation temperature). To provide comprehensive analysis, also evaluated traditional statistical methods (ARIMAX MLR) suite ML algorithms (ANN, SVR, RFR, XGBoost). performance these was rigorously using error metrics RMSE, MAE, sMAPE, MASE QL. findings were significant indicating DL particularly when augmented variables, consistently outshone other TransformerX showing an average RMSE 110.33 135.33, MAE 60.08 74.92, sMAPE 22.14 24.00, 1.02 1.32 QL 30.04 34.07, respectively. They exhibited lower metrics, as compare underscoring effectiveness potential in crop forecasting. This not only bridged gap but highlighted robust enhancing accuracy predictions India.

Language: Английский

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N-BEATS Deep Learning Architecture for Agricultural Commodity Price Forecasting

Potato Research, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 12, 2024

Language: Английский

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Transformer-based deep learning architecture for time series forecasting

Software Impacts, Journal Year: 2024, Volume and Issue: 22, P. 100716 - 100716

Published: Nov. 1, 2024

Language: Английский

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Decomposition combining averaging seasonal-trend with singular spectrum analysis and a marine predator algorithm embedding Adam for time series forecasting with strong volatility

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126864 - 126864

Published: Feb. 1, 2025

Language: Английский

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A Novel BiGRU-Attention Model for Predicting Corn Market Prices Based on Multi-Feature Fusion and Grey Wolf Optimization

Agriculture, Journal Year: 2025, Volume and Issue: 15(5), P. 469 - 469

Published: Feb. 21, 2025

Accurately predicting corn market prices is crucial for ensuring production, enhancing farmers’ income, and maintaining the stability of grain market. However, price fluctuations are influenced by various factors, exhibiting non-stationarity, nonlinearity, high volatility, making prediction challenging. Therefore, this paper proposes a comprehensive, efficient, accurate method prices. First, in data processing phase, seasonal trend decomposition using LOESS (STL) algorithm was used to extract trend, seasonality, residual components prices, combined with GARCH-in-mean (GARCH-M) model delve into volatility clustering characteristics. Next, kernel principal component analysis (KPCA) employed nonlinear dimensionality reduction key information accelerate convergence. Finally, BiGRU-Attention model, optimized grey wolf optimizer (GWO), constructed predict accurately. The effectiveness proposed assessed through cross-sectional longitudinal validation experiments. empirical results indicated that STLG-KPCA-GWO-BiGRU-Attention (SGKGBA) exhibited significant advantages terms MAE (0.0159), RMSE (0.0215), MAPE (0.5544%), R2 (0.9815). This effectively captures fluctuation features, significantly enhances accuracy, offers reliable forecasts decision makers regarding

Language: Английский

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A Novel Framework for Agricultural Futures Price Prediction With BERT‐Based Topic Identification and Sentiment Analysis

Journal of Forecasting, Journal Year: 2025, Volume and Issue: unknown

Published: April 11, 2025

ABSTRACT In China's financial and economic system, the agricultural futures market plays an important role in guiding to self regulate providing efficient information transmission for regulators. The effective prediction of prices can assist production, monitoring operational risks arising from significant price fluctuations, enhancing predictability pertinence country's macroeconomic regulation policies. This study investigates main variety grain futures—soybean futures, taking into account complex non‐market influencing factors. Using historical data related news headlines soybean as source integrating topic identification sentiment analysis techniques, a novel framework predicting that integrates is constructed. model uses BERTopic extract texts, then FinBERT construct topic‐based features, fuses them with structured constructs LSTM multi‐feature inputs. order better short‐term features state transfer patterns time series, hidden Markov (HMM) further used states, which are deeply fused model. empirical results show fusing significantly improves forecasting accuracy all lags, works best forecasting, combination HMM exhibits performance advantages medium‐ long‐term forecasting. Compared baseline relies only on provide incremental contribution each feature calculated based PI metric close 50%. addition, deep learning–based performs than machine learning models dealing extreme external shocks such climate disasters, COVID‐19 pandemic, Russia–Ukraine conflict.

Language: Английский

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A Hybrid Model Integrating Variational Mode Decomposition and Intelligent Optimization for Vegetable Price Prediction

Agriculture, Journal Year: 2025, Volume and Issue: 15(9), P. 919 - 919

Published: April 23, 2025

In recent years, China’s vegetable market has faced frequent and drastic price fluctuations due to factors such as supply–demand relationships climate change, which significantly affect government bodies, farmers, consumers, other participants in the industry supply chain. Traditional forecasting methods demonstrate evident limitations capturing nonlinear characteristics complex volatility patterns of series, underscoring necessity developing high-precision prediction models. This study proposes a hybrid model integrating variational mode decomposition (VMD), Fruit Fly Optimization Algorithm (FOA), gated recurrent unit (GRU). The employs VMD for multi-scale original series utilizes FOA adaptive optimization GRU’s critical parameters, effectively addressing challenges high nonlinearity agricultural forecasting. Empirical analysis conducted on daily data six major vegetables, specifically, Chinese cabbage, cucumber, beans, tomato, chili, radish, from 2014 2024 reveals that proposed outperforms traditional methods, single deep learning models, models predictive performance. Experimental results indicate substantial improvements key metrics including Mean Absolute Error (MAE), Root Square (RMSE), Coefficient Determination (R2), with R2 values consistently exceeding 99.4% achieving over 5% enhancement compared baseline GRU model. research establishes novel methodological framework analyzing while providing reliable technical support monitoring policy regulation.

Language: Английский

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