Advancing Agricultural Predictions: A Deep Learning Approach to Estimating Bulb Weight Using Neural Prophet Model

Kim, Wonseong; Soon, Byung Min

doi:10.3390/agronomy13051362

Cited by 4 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the analysis process, LASSO regression analysis was used for variable selection and coefficient estimation of garlic bulb weight. Kim and Soon [16] learned the Neural Prophet (NP) lagged time-series model using onion and garlic data from the Korea Rural Economic Institute. And they predicted the average fresh bulb weight of onion and garlic using the learned NP model.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Live Bulb Weight for Field Vegetables Using Functional Regression Models and Machine Learning Methods

Kim,

Cho,

et al. 2024

Agriculture

View full text Add to dashboard Cite

(1) Background: This challenge is exacerbated by the aging of the rural population, leading to a scarcity of available manpower. To address this issue, the automation and mechanization of outdoor vegetable cultivation are imperative. Therefore, developing an automated cultivation platform that reduces labor requirements and improves yield by efficiently performing all the cultivation activities related to field vegetables, particularly onions and garlic, is essential. In this study, we propose methods to identify onion and garlic plants with the best growth status and accurately predict their live bulb weight by regularly photographing their growth status using a multispectral camera mounted on a drone. (2) Methods: This study was conducted in four stages. First, two pilot blocks with a total of 16 experimental units, four horizontals, and four verticals were installed for both onions and garlic. Overall, a total of 32 experimental units were prepared for both onion and garlic. Second, multispectral image data were collected using a multispectral camera repeating a total of seven times for each area in 32 experimental units prepared for both onions and garlic. Simultaneously, growth data and live bulb weight at the corresponding points were recorded manually. Third, correlation analysis was conducted to determine the relationship between various vegetation indexes extracted from multispectral images and the manually measured growth data and live bulb weights. Fourth, based on the vegetation indexes extracted from multispectral images and previously collected growth data, a method to predict the live bulb weight of onions and garlic in real time during the cultivation period, using functional regression models and machine learning methods, was examined. (3) Results: The experimental results revealed that the Functional Concurrence Regression (FCR) model exhibited the most robust prediction performance both when using growth factors and when using vegetation indexes. Following closely, with a slight distinction, Gaussian Process Functional Data Analysis (GPFDA), Random Forest Regression (RFR), and AdaBoost demonstrated the next-best predictive power. However, a Support Vector Machine (SVM) and Deep Neural Network (DNN) displayed comparatively poorer predictive power. Notably, when employing growth factors as explanatory variables, all prediction models exhibited a slightly improved performance compared to that when using vegetation indexes. (4) Discussion: This study explores predicting onion and garlic bulb weights in real-time using multispectral imaging and machine learning, filling a gap in research where previous studies primarily focused on utilizing artificial intelligence and machine learning for productivity enhancement, disease management, and crop monitoring. (5) Conclusions: In this study, we developed an automated method to predict the growth trajectory of onion and garlic bulb weights throughout the growing season by utilizing multispectral images, growth factors, and live bulb weight data, revealing that the FCR model demonstrated the most robust predictive performance among six artificial intelligence models tested.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of Live Bulb Weight for Field Vegetables Using Functional Regression Models and Machine Learning Methods

Kim,

Cho,

et al. 2024

Agriculture

View full text Add to dashboard Cite

show abstract

“…Neural Prophet is built on PyTorch [23] and incorporates innovative techniques such as automatic model selection, feature engineering, and uncertainty estimation, making it a powerful tool for accurate and reliable predictions [24]. With its flexibility and ability to handle complex temporal patterns, Neural Prophet has gained attention as a promising framework for forecasting time series data [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Bitcoin Price Forecasting Using Neural Prophet

Noviandy,

Maulana,

Idroes

et al. 2023

Ekon. J. Econ.

View full text Add to dashboard Cite

This study focuses on using the Neural Prophet framework to forecast Bitcoin prices accurately. By analyzing historical Bitcoin price data, the study aims to capture patterns and dependencies to provide valuable insights and predictive models for investors, traders, and analysts in the volatile cryptocurrency market. The Neural Prophet framework, based on neural network principles, incorporates features such as automatic differencing, trend, seasonality considerations, and external variables to enhance forecasting accuracy. The model was trained and evaluated using performance metrics such as RMSE, MAE, and MAPE. The results demonstrate the model's effectiveness in capturing trends and predicting Bitcoin prices while acknowledging the challenges posed by the inherent volatility of the cryptocurrency market.

show abstract