Frost prediction using machine learning and deep neural network models

Talsma, Carl; Solander, Kurt C.; Mudunuru, Maruti K.; Crawford, Brandon L.; Powell, Michelle R.

doi:10.3389/frai.2022.963781

Cited by 13 publications

(9 citation statements)

References 38 publications

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“…This explanation is consistent with findings of Talsma et al . (2023), who showed that a convolutional neural network predicted minimum temperature with much less warm bias that RF, especially for sparse data at the extrema. Forecasting of minimum temperature by RF and ANN was also studied by Eccel et al .…”

Section: Discussionmentioning

confidence: 99%

Machine learning based post‐processing of model‐derived near‐surface air temperature – A multimodel approach

Stachura,

Ustrnul,

Sekuła

et al. 2023

Quart J Royal Meteoro Soc

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In this article, a machine‐learning‐based tool for calibrating numerical forecasts of near‐surface air temperature is proposed. The study area covers Poland representing a temperate type of climate with transitional features and highly variable weather. The direct output of numerical weather prediction (NWP) models is often biased and needs to be adjusted to observed values. Forecasters have to reconcile forecasts from several NWP models during their operational work. As the proposed method is based on deterministic forecasts from three short‐range limited‐area models (ALARO, AROME and COSMO), it can support them in their decision‐making process. Predictors include forecasts of weather elements produced by the NWP models at synoptic weather stations across Poland and station‐embedded data on ambient orography. The Random Forests algorithm (RF) has been used to produce bias‐corrected forecasts on a test set spanning one year. Its performance was evaluated against the NWP models, a linear combination of all predictors (multiple linear regression, MLR) as well as a basic Artificial Neural Network (ANN). Detailed evaluation was done to identify potential strengths and weaknesses of the model at the temporal and spatial scale. The value of RMSE of a forecast obtained by the RF model was 11% and 27% lower compared to the MLR model and the best‐performing NWP model respectively. The ANN model turned out to be even superior, outperforming RF by around 2.5%. The greatest improvement occurred for warm bias during the nighttime from July to September. The largest difference in forecast accuracy between RF and ANN appeared for temperature drops inApril nights. Poor performance of RF for extreme temperature ranges may be suppressed by training the model on forecast error instead of observed values of the variable.

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Section: Discussionmentioning

confidence: 99%

Machine learning based post‐processing of model‐derived near‐surface air temperature – A multimodel approach

Stachura,

Ustrnul,

Sekuła

et al. 2023

Quart J Royal Meteoro Soc

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“…Additionally, to avoid unnecessary expenses, it is essential for farmers to accurately recognize when a frost episode poses an actual threat. In this context, access to precise meteorological data and frost risk forecasts assumes an invaluable role [23].…”

Section: A Frost Predictionmentioning

confidence: 99%

IoT-Driven Machine Learning for Precision Viticulture Optimization

Pero,

Bakshi,

Nappi

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Precision Agriculture (PA), also known as Smart Farming, has emerged as an innovative solution to address contemporary challenges in agricultural sustainability. A particular sector within PA, Precision Viticulture (PV), is specifically tailored for vineyards. The advent of the Internet of Things (IoT) has facilitated the acquisition of higher-resolution meteorological and soil data obtained through in situ sensing. The integration of Machine Learning (ML) with IoT-enabled farm machinery stands at the forefront of the forthcoming agricultural revolution. This data allows ML-based forecasting as an alternative to conventional approaches, providing agronomists with predictive tools essential for improved land productivity and crop quality. This study conducts a thorough examination of vineyards with a specific focus on three key aspects of PV: mitigating frost damage, analyzing soil moisture levels, and addressing grapevine diseases. In this context, several ML-based models are proposed in a realworld scenario involving a vineyard located in southern Italy. The test results affirm the feasibility and efficacy of the ML models, demonstrating their potential to revolutionize vineyard management and contribute to sustainable agricultural practices.

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“…It has the capability of identifying unseen patterns, and complex interconnections in the data series. Machine learning models are capable of predicting nonlinear relationships with high accuracy 35 , 36 . Therefore, several (8) state-of-the-art machine learning models were utilized to predict the surface and 10 cm depth soil temperature.…”

Section: Study Area and Datasetmentioning

confidence: 99%

Artificial intelligence to predict soil temperatures by development of novel model

Mampitiya,

Rozumbetov,

Rathnayake

et al. 2024

Sci Rep

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Soil temperatures at both surface and various depths are important in changing environments to understand the biological, chemical, and physical properties of soil. This is essential in reaching food sustainability. However, most of the developing regions across the globe face difficulty in establishing solid data measurements and records due to poor instrumentation and many other unavoidable reasons such as natural disasters like droughts, floods, and cyclones. Therefore, an accurate prediction model would fix these difficulties. Uzbekistan is one of the countries that is concerned about climate change due to its arid climate. Therefore, for the first time, this research presents an integrated model to predict soil temperature levels at the surface and 10 cm depth based on climatic factors in Nukus, Uzbekistan. Eight machine learning models were trained in order to understand the best-performing model based on widely used performance indicators. Long Short-Term Memory (LSTM) model performed in accurate predictions of soil temperature levels at 10 cm depth. More importantly, the models developed here can predict temperature levels at 10 cm depth with the measured climatic data and predicted surface soil temperature levels. The model can predict soil temperature at 10 cm depth without any ground soil temperature measurements. The developed model can be effectively used in planning applications in reaching sustainability in food production in arid areas like Nukus, Uzbekistan.

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Frost prediction using machine learning and deep neural network models

Cited by 13 publications

References 38 publications

Machine learning based post‐processing of model‐derived near‐surface air temperature – A multimodel approach

Machine learning based post‐processing of model‐derived near‐surface air temperature – A multimodel approach

IoT-Driven Machine Learning for Precision Viticulture Optimization

Artificial intelligence to predict soil temperatures by development of novel model

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