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

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Drought stress detection technique for wheat crop using machine learning.

Ankita Gupta¹, Lakhwinder Kaur¹, Gurmeet Kaur²

¹Computer Science and Engineering, Punjabi University, Patiala, Punjab, India.

Peerj. Computer Science

|June 22, 2023

Summary

This summary is machine-generated.

This study developed an automated model for detecting wheat crop water stress using advanced image processing and machine learning. The Random Forest algorithm achieved 91.164% accuracy, offering a reliable solution for precision agriculture.

Keywords:

Chlorophyll fluoroscence Demonising Drought Image enhancement Image processing Machine learning PSII efficiency analysis PlantCV Texture analysis Wheat

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Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Wheat crop water stress significantly impacts yield.
Accurate and timely detection of water stress is crucial for effective irrigation management.
Existing methods for water stress detection can be labor-intensive and lack automation.

Purpose of the Study:

To develop an automated model for detecting wheat crop water stress.
To evaluate the efficacy of various pre-processing and segmentation techniques for water stress detection.
To identify the most suitable machine learning algorithm for classifying wheat crop water stress.

Main Methods:

Image pre-processing using Total Variation with L1 data fidelity term (TV-L1) denoising and min-max contrast stretching.
Wheat canopy segmentation utilizing a curve fit based K-means algorithm (Cfit-kmeans).
Machine learning model development through rapid prototyping, hyper-parameter tuning, and 10-fold cross-validation, testing nine different algorithms.

Main Results:

TV-L1 denoising and min-max contrast stretching were identified as optimal pre-processing methods.
The Cfit-kmeans algorithm demonstrated high accuracy in wheat canopy segmentation.
The Random Forest algorithm achieved the highest global diagnostic accuracy of 91.164% among the tested models.

Conclusions:

The developed automated model, particularly using the Random Forest algorithm, is highly suitable for detecting wheat crop water stress.
The integration of advanced image processing and machine learning techniques offers a robust solution for precision agriculture.
This research provides a foundation for improved irrigation strategies and enhanced crop management.