?? AI for Soil: Prediction of Soil Water Content
Predicting soil water content is important for effective agricultural management, irrigation planning, and environmental monitoring.
The application of artificial intelligence in this domain offers significant advantages due to its ability to handle large datasets, identify complex patterns, and provide accurate predictions. Today we'll focus on predicting soil water content based on images captured with a digital camera and drone.
We'll examine which machine learning models for soil analysis best utilize the dependency between the color of the soil and soil water content.
How practically useful are the findings of these soil science studies?
Rapid Prediction of Soil Water Content Using Colour Information and Machine Learning
Country: China ????
Published: 2 March 2023
This study aims to develop a non-invasive, efficient method for predicting soil water content (SWC) by analyzing soil images using Gaussian-fitting of grey histograms and machine learning models.
The methods involve capturing soil images under controlled illumination, extracting characteristic colour parameters from multiple colour spaces, and using machine learning models (PLSR, RF, SVMR, GPR) to predict SWC. The process includes image segmentation, optimal area sampling, and statistical analysis to identify the most relevant predictors.
Key findings show that the Gaussian-fitting method effectively reduces interference from complex soil surfaces. The GPR model achieved the highest prediction accuracy (R2 = 0.95, RMSE = 2.01%, RPD = 4.95, RPIQ = 6.37).
Colour Parameters for Soil Water Content Prediction
Ten key colour parameters were identified as optimal predictors without significant loss of accuracy compared to 32 parameters.
The ten key color parameters identified as optimal predictors for soil water content (SWC) in the study are:
These parameters were selected based on their significance and contribution to the SWC prediction models
The results can be practically applied by agricultural engineers, geotechnical professionals, and environmental scientists for real-time soil moisture monitoring.
Main tools/technologies
- Digital Image Analysis
- Gaussian-fitting Gray Histogram
- Partial Least Squares Regression (PLSR)
- Random Forest (RF)
- Support Vector Machines Regression (SVMR)
- Gaussian Process Regression (GPR)
- MATLAB
Soil Colour Analysis Based on a RGB Camera and an Artificial Neural Network Towards Smart Irrigation
Country: Iraq ????, Australia ????
Published: 20 January 2021
The focus of this study is targeted on a non-contact vision system using an RGB camera and a feed-forward back propagation neural network to predict irrigation requirements for loam soils by analyzing soil colour variations.
The methods involved collecting soil images under different conditions (distance, time, and illumination), using an RGB camera mounted on a tripod, and processing these images to extract RGB colour values. These values were used as input to an artificial neural network (ANN) to determine soil hydration status. The ANN was configured with two hidden layers, each with ten neurons, to achieve optimal performance.
Key findings include the system's high accuracy, with mean square errors (MSE) of 1.616 × 10?? for training, 1.004 × 10?? for testing, and 1.809 × 10?? for validation.
The ANN effectively classified soil types under various conditions, achieving a high coefficient of determination (R2 = 1 for training and testing).
Agricultural engineers, farmers, and irrigation managers can practically apply the results to improve irrigation efficiency and water management.
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Main tools/technologies
Smart Soil Moisture Prediction with UAV Multispectral and Thermal Imaging
Country: Hungary ????
Published: 1 August 2022
The final study for today aims to evaluate the efficacy of UAV-based thermal and multispectral cameras combined with machine learning algorithms in predicting soil water content (SWC) in agricultural fields.
The methods involved UAV flights equipped with thermal and multispectral cameras to capture soil data. Soil samples were collected and analyzed in the laboratory for SWC, which served as reference data. Machine learning algorithms (Random Forest [RF], Elastic Net Regression [ENR], General Linear Model [GLM], Robust Linear Model [RLM]) were tested using three pixel value extraction methods (single pixel, mean of 20 cm radius buffer, mean of 30 cm radius buffer).
Key findings showed that multispectral cameras provided better data for SWC prediction than thermal cameras, with the RF model achieving the highest accuracy (R2 = 0.97, nRMSE = 10%).
Thermal data were less accurate but still useful, especially with the RF model (nRMSE = 24.4%). Single pixel extraction proved to be the most effective method for data input.
Agricultural engineers and farmers can practically apply the results to enhance precision irrigation and water management.
Main tools/technologies
What's next in "AI for Soil"?
Next time we hope to find more interesting resources about applying machine learning for soil analysis. Particularly, we will take a look at estimating soil organic carbon and visual assessment of other important soil characteristics.
Would you like to share your opinion about this choice of the topic?
We'll be grateful for a few words in the comments below ??
Stay in touch! ??
Wishes of accurate predictions of soil water content,
Maryna Kuzmenko, Ph.D ????, Chief Inspiration Officer at Petiole Pro Community
#soilanalysis
Photo credit for the cover image: Al-Naji, A., Fakhri, A. B., Gharghan, S. K., & Chahl, J. (2021). Soil color analysis based on a RGB camera and an artificial neural network towards smart irrigation: A pilot study. Heliyon, 7(1), e06078. ISSN 2405-8440. https://doi.org/10.1016/j.heliyon.2021.e06078.
References for today's "AI for Soil" edition
Engineer || AgTech || Precision Crop Protection Researcher || UAV's
8 个月Interesting!
Optimizing logistics and transportation with a passion for excellence | Building Ecosystem for Logistics Industry | Analytics-driven Logistics
8 个月Innovative How can we use AI and color parameters to accurately predict soil water content in agriculture?
Crop Improvement Researcher
8 个月Maryna Kuzmenko, Ph.D ???? Already there are submergence tolerance genes (sub1,2,3) identified and successfully introgressed in many paddy mega varieties for submergence tolerance that can help plants even up to 30 days under the water. On the other hand, there is a huge problem in wheat and maize when late monsoon or pre-winter rainfall increases moisture content that either affects on seed germination (by creating anaerobic conditions in soil) or heavy moisture in the soil leads to seedling death. Contrarily in the same field due to drought during panicle emergence or grain filling leads the yield loss to farmers. In those cases up to the discovery of tolerant genes. Maryna Kuzmenko, Ph.D ???? work can significantly help in research work and even local farmers to reduce the cost of cultivation. No words besides "excellent work Maryna Kuzmenko, Ph.D ????"