Developing an automated monitoring system for fast and accurate prediction of soil texture using an image-based deep learning network and machine vision system

Azadnia, Rahim; Jahanbakhshi, Ahmad; Rashidi, Shima; Khajehzadeh, Mohammad

doi:10.1016/j.measurement.2021.110669

Cited by 27 publications

(4 citation statements)

References 42 publications

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“…CNNs are therefore very well suited for this soil texture prediction. CNN models were developed to classify soil texture using soil images [14]. This model utilized a Convolutional Neural Network (CNN) architecture, a pre-trained model capable of classifying images into 1100 different object classes.…”

Section: Cnn Methodologymentioning

confidence: 99%

Soil Texture Prediction Using Machine Learning Approach for Sustainable Soil Health Management

Abishek,

Kannan,

Devi

et al. 2023

IJPSS

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Soil in the earth acts as a foothold for all crops. Soil texture is the most important soil health indicator being used for the selection of crops, mechanical manipulation, irrigation management, and fertilizer management. The texture of the soil influences the storage and flow of air and water within the soil, as well as root development, the accessibility of plant nutrients, and the activities of different microorganisms. These factors collectively impact the soil's fertility, quality, and soil health. A conventional method of soil texture analysis is cumbersome, time-consuming, and labor-intensive. Machine learning (ML) is a newly emerging technique being used to assess the soil's physical, chemical, and biological properties quickly in real-time. This is an eco-friendly approach since it does not involve any hazardous chemicals. Machine learning can learn complex features and predict nonlinear properties. Convolutional Neural Networks (CNN) employs convolutional layers to automatically learn features from the input data and is widely used in image classification, object detection, and image generation tasks in a short time. Soil texture images are given as input dataset after the completion of image subsetting, data preprocessing, and Image augmentation. This gives a CNN-based soil texture predictive model with a reliable accuracy of 87.50% at a lower cost.

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Section: Cnn Methodologymentioning

confidence: 99%

Soil Texture Prediction Using Machine Learning Approach for Sustainable Soil Health Management

Abishek,

Kannan,

Devi

et al. 2023

IJPSS

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“…Computer vision collects the necessary visual data about crops, livestock or farms, enabling us to identify, detect and track specific objects using visual elements, and to understand complex visual data for automated tasks. Over the past decades, expert and intelligent systems based on computer vision technology have been well used in agricultural operations, such as seed quality analysis [1] ， soil analysis [2] ， plant health analysis [3] and yield estimation [4]. In smart agriculture, distance measurement also has important research value in areas such as automatic driving of agricultural vehicles and terrain mapping of farmland.…”

Section: Introductionmentioning

confidence: 99%

Height-Variable Monocular Vision Ranging Technology for Smart Agriculture

Gao,

Li,

Xue

et al. 2023

IEEE Access

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Smart agriculture utilizes a variety of advanced technologies to promote sustainable agriculture and provide solutions for intelligent, automated and unmanned agriculture. Agricultural robots and related technologies are an important part of smart agriculture, while autonomous navigation is a core function of autonomous agricultural robots, which rely on information about the distance of obstacles in a scene to support decision making. In this paper, we propose a ground point geometric ranging model, which can be used in camera height dynamic change scenarios, and the method is validated by model derivation and hypothesis testing. The model combines ranging and camera calibration, choosing to compensate for distortion and defocus phenomena caused by nonlinear imaging of the camera to the focal length, and completes the parameter calibration using a small amount of ground point real distance data. In this paper, the YOLOv8 model is used to identify and range outdoor cattle, and the experimental results show that the lowest range accuracy of this method reaches 95%, this method eliminates the dependence on camera height for focal calibration in ranging models, and in practice requires only once focal calibration for permanent use, achieving a significant reduction in the complexity of focal calibration, and the migrability of the model in scenarios where the camera height changes.

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“…Overall, DL addresses the shortcomings of ML by providing enhanced performance, automatic feature extraction, and improved scalability. Various researchers have utilized DL models to address soil science problems such as predicting soil texture [24,25] and soil salinity [26] using the CNN algorithm and predicting soil moisture using the LSTM algorithm [27]. While DL models have several advantages, they are also associated with drawbacks such as computational complexity [28] and overfitting [29].…”

Section: Introductionmentioning

confidence: 99%

Geospatial Artificial Intelligence (GeoAI) and Satellite Imagery Fusion for Soil Physical Property Predicting

Hosseini,

Seo,

Razavi-Termeh

et al. 2023

Sustainability

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This study aims to predict vital soil physical properties, including clay, sand, and silt, which are essential for agricultural management and environmental protection. Precision distribution of soil texture is crucial for effective land resource management and precision agriculture. To achieve this, we propose an innovative approach that combines Geospatial Artificial Intelligence (GeoAI) with the fusion of satellite imagery to predict soil physical properties. We collected 317 soil samples from Iran’s Golestan province for dependent data. The independent dataset encompasses 14 parameters from Landsat-8 satellite images, seven topographic parameters from the Shuttle Radar Topography Mission (SRTM) DEM, and two meteorological parameters. Using the Random Forest (RF) algorithm, we conducted feature importance analysis. We employed a Convolutional Neural Network (CNN), RF, and our hybrid CNN-RF model to predict soil properties, comparing their performance with various metrics. This hybrid CNN-RF network combines the strengths of CNN networks and the RF algorithm for improved soil texture prediction. The hybrid CNN-RF model demonstrated superior performance across metrics, excelling in predicting sand (MSE: 0.00003%, RMSE: 0.006%), silt (MSE: 0.00004%, RMSE: 0.006%), and clay (MSE: 0.00005%, RMSE: 0.007%). Moreover, the hybrid model exhibited improved precision in predicting clay (R2: 0.995), sand (R2: 0.992), and silt (R2: 0.987), as indicated by the R2 index. The RF algorithm identified MRVBF, LST, and B7 as the most influential parameters for clay, sand, and silt prediction, respectively, underscoring the significance of remote sensing, topography, and climate. Our integrated GeoAI-satellite imagery approach provides valuable tools for monitoring soil degradation, optimizing agricultural irrigation, and assessing soil quality. This methodology has significant potential to advance precision agriculture and land resource management practices.

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Developing an automated monitoring system for fast and accurate prediction of soil texture using an image-based deep learning network and machine vision system

Cited by 27 publications

References 42 publications

Soil Texture Prediction Using Machine Learning Approach for Sustainable Soil Health Management

Soil Texture Prediction Using Machine Learning Approach for Sustainable Soil Health Management

Height-Variable Monocular Vision Ranging Technology for Smart Agriculture

Geospatial Artificial Intelligence (GeoAI) and Satellite Imagery Fusion for Soil Physical Property Predicting

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