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Related Experiment Video

Updated: Jan 16, 2026

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

Recent Trends in Machine Learning, Deep Learning, Ensemble Learning, and Explainable Artificial Intelligence

Ji Won Choi¹, Mohamad Soleh Hidayat¹, Soo Been Cho¹

¹Department of Biosystems Engineering, College of Agricultural and Life Sciences, Gyeongsang National University, 501, Jinju-daero, Jinju 52858, Republic of Korea.

Plants (Basel, Switzerland)

|September 27, 2025

Summary

Advanced Artificial Intelligence (AI) methods significantly improve crop yield prediction (CYP) by analyzing environmental factors and utilizing remote sensing. Feature selection enhances accuracy, paving the way for precision agriculture.

Keywords:

abnormal climate artificial intelligence crop yield deep learning machine learning

Related Experiment Videos

Last Updated: Jan 16, 2026

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:

Agricultural Science
Computer Science
Environmental Science

Background:

Crop yield prediction (CYP) is vital for agricultural productivity and mitigating climate change impacts.
Abnormal climate, pests, and soil degradation threaten global food security.

Purpose of the Study:

To review Artificial Intelligence (AI) applications in crop yield prediction.
To explore remote sensing, environmental factors, and yield reduction causes.

Main Methods:

Review of Machine Learning (ML), Deep Learning (DL), Ensemble Learning, and Explainable AI (XAI) in CYP.
Analysis of remote sensing (hyperspectral and multispectral imaging) and environmental data.
Evaluation of feature selection techniques and common algorithms (RF, SVM, ANNs, CNNs).

Main Results:

Stepwise feature selection improves model accuracy more than increasing feature volume.
Random Forest, SVM, ANNs, and CNNs are frequently used algorithms.
Hyperspectral and multispectral imaging via drones are common sensing techniques.

Conclusions:

AI, particularly ML and DL, offers powerful tools for accurate crop yield prediction.
Explainable AI shows promise for interpreting complex CYP models.
Understanding environmental factors and yield reduction causes is key for advancing precision agriculture and policy.