Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Responses to Drought and Flooding

Responses to Drought and Flooding

Water plays a significant role in the life cycle of plants. However, insufficient or excess of water can be detrimental and pose a serious threat to plants.

Precipitation Processes

Precipitation Processes

The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...

Precipitation and Co-precipitation

Precipitation and Co-precipitation

Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...

Adaptations that Reduce Water Loss

Adaptations that Reduce Water Loss

Though evaporation from plant leaves drives transpiration, it also results in loss of water. Because water is critical for photosynthetic reactions and other cellular processes, evolutionary pressures on plants in different environments have driven the acquisition of adaptations that reduce water loss.

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Data analysis tool for identifying multidimensional health profiles associated with frailty in older adults.

BMC medical informatics and decision making·2026

Same author

Missing data imputation of climate time series: A review.

MethodsX·2025

Same author

Predicting Drug Resistance in <i>Mycobacterium tuberculosis</i>: A Machine Learning Approach to Genomic Mutation Analysis.

Diagnostics (Basel, Switzerland)·2025

Same author

Use and Comparison of Machine Learning Techniques to Discern the Protein Patterns of Autoantibodies Present in Women with and without Breast Pathology.

Journal of proteome research·2024

Same author

asMODiTS: An application of surrogate models to optimize Time Series symbolic discretization through archive-based training set update strategy.

MethodsX·2024

Same author

Efficiently handling constraints in mixed-integer nonlinear programming problems using gradient-based repair differential evolution.

PeerJ. Computer science·2024

Search research articles

Related Experiment Video

Updated: Jun 21, 2025

Semi-High Throughput Screening for Potential Drought-tolerance in Lettuce Lactuca sativa Germplasm Collections

Semi-High Throughput Screening for Potential Drought-tolerance in Lettuce Lactuca sativa Germplasm Collections

Published on: April 17, 2015

Characterizing drought prediction with deep learning: A literature review.

Aldo Márquez-Grajales¹, Ramiro Villegas-Vega², Fernando Salas-Martínez³

¹INFOTEC Center for Research and Innovation in Information and Communication Technologies, Circuito Tecnopolo Sur, No 112, Fracc. Tecnopolo Pocitos, Aguascalientes, 20326, Aguascalientes, México.

|July 11, 2024

Summary

Deep learning enhances drought prediction using climate and vegetation indices like SPI, SPEI, and NDVI. Long Short-Term Memory networks (LSTM) are most common, but research is lacking in America and Africa.

Keywords:

Characterizing drought prediction with deep learning: A literature review Deep learning Drought Prediction Remote sensing and climate index

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

Related Experiment Videos

Last Updated: Jun 21, 2025

Semi-High Throughput Screening for Potential Drought-tolerance in Lettuce Lactuca sativa Germplasm Collections

Semi-High Throughput Screening for Potential Drought-tolerance in Lettuce Lactuca sativa Germplasm Collections

Published on: April 17, 2015

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

Area of Science:

Environmental Science
Computer Science
Climate Science

Background:

Drought prediction is vital for mitigating environmental and human impacts.
Deep learning offers advanced capabilities for complex prediction tasks.
Understanding current methodologies is key to advancing drought forecasting.

Purpose of the Study:

To review and characterize state-of-the-art deep learning techniques for drought prediction.
To identify commonly used climate and vegetation indices in drought forecasting.
To map the global distribution of research on deep learning for drought prediction.

Main Methods:

Systematic literature review of deep learning applications in drought prediction.
Analysis of frequently used climate indices: Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI).
Identification of prevalent multispectral indices, notably the Normalized Difference Vegetation Index (NDVI).
Examination of deep learning algorithms, with a focus on Long Short-Term Memory (LSTM) networks and hybrid approaches.

Main Results:

SPI and SPEI are the most frequently employed climate indices.
NDVI is the most utilized multispectral index for drought prediction.
Asia and Oceania lead in research output, while America and Africa show limited publications.
LSTM networks, both standalone and in hybrid models, are the dominant deep learning methods.

Conclusions:

A significant research gap exists in applying deep learning and multispectral indices for drought prediction in America and Africa.
Developing countries present an opportunity for advancing drought prediction research.
Further investigation is needed to enhance drought forecasting capabilities in underrepresented regions.