Machine learning methods for soil moisture prediction in vineyards using digital images

Hajjar, Chantal; Hajjar, Celine; Esta, Michel; Chamoun, Yolla Ghorra

doi:10.1051/e3sconf/202016702004

Cited by 16 publications

(11 citation statements)

References 17 publications

(20 reference statements)

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“…Finally, on determining the dielectric constant, the volumetric water constant can be computed using the Topps equation as given by Equation (3). This equation does not entail any prior information of the surface roughness or the soil texture [ 18 ]:

where,

is the dielectric constant of the soil and

is the volumetric water content.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Models for Enhanced Estimation of Soil Moisture Using Wideband Radar Sensor

Uthayakumar

Mohan

Khoo

et al. 2022

Sensors

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In this paper, machine learning models for an effective estimation of soil moisture content using a microwave short-range and wideband radar sensor are proposed. The soil moisture is measured as the volumetric water content using a short-range off-the-shelf radar sensor operating at 3–10 GHz. The radar captures the reflected signals that are post processed to determine the soil moisture which is mapped to the input features extracted from the reflected signals for the training of the machine learning models. In addition, the results are compared and analyzed with a contact-based Vernier soil sensor. Different machine learning models trained using neural network, support vector machine, linear regression and k-nearest neighbor are evaluated and presented in this work. The efficiency of the model is computed using root mean square error, co-efficient of determination and mean absolute error. The RMSE and MAE values of KNN, SVM and Linear Regression are 11.51 and 9.27, 15.20 and 12.74, 3.94 and 3.54, respectively. It is observed that the neural network gives the best results with an R2 value of 0.9894. This research work has been carried out with an intention to develop cost-effective solutions for common users such as agriculturists to monitor the soil moisture conditions with improved accuracy.

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where,

is the dielectric constant of the soil and

is the volumetric water content.…”

Section: Methodsmentioning

confidence: 99%

“…The soil moisture can be determined for a small piece of land rather than over a large area. Even though there are few methods [ 18 ] where cameras were used to measure the soil moisture, but their performance during night is not known.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models for Enhanced Estimation of Soil Moisture Using Wideband Radar Sensor

Uthayakumar

Mohan

Khoo

et al. 2022

Sensors

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“…To enhance the precision of center-pivot irrigation system a simple and effective method proposed to increase shape bias in the object detection networks and experimentally validated [21]. Using digital photography, samples of six different soil types were gathered from grape soils of Chateau Kefraya terroirs in Lebanon [23]. Features such as thermal and texture features, canopy structure extracted from the data collected by UAV are used to estimate grain yield using Partial Least Squares Regression, Support Vector Regression, Random Forest Regression, input-level feature fusion based DNN and immediate-level Feature fusion based DNN [24].…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Review of Emerging Technologies: Machine Learning and UAV in Crop Management

Yagnasree

Jain

2022

J. Phys.: Conf. Ser.

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It is a known fact that India is one among the major food producers in the world. Due to the rapid urbanization and Agrochemical interventions, plants are infected by insects, pathogens, massive infestations, which leads to the deficiency of growth nutrients and ions, thereby minimizing agricultural crop yield. These infections occur in several forms like aphids, black root rot, cankers, leaf curls, rusts, anthracnose, and blights make agriculture fail to thrive and increase the impact of crop diseases. Since plant disease prevention is a continuous process that occurs regularly, agriculturists follow several strategies to keep their crops disease-free. An efficient monitoring and supporting system for continuous and long-term plant and soil health monitoring is needed to meet the needs of growing population. In this paper, existing research works in Precision agriculture, emerging technologies – GPS, GIS, Machine learning and UAVs in analyzing crop health analysis, soil health monitoring, and crop yield prediction are reviewed.

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“…Further, Ref. [15] describes a novel soil moisture prediction method in vineyards based on digital images and a multilayer perceptron (MLP) and support vector regression (SVR) implementation. Both methods presented by the authors were successful in soil moisture forecasting, with high correlation values between the predicted and measured soil moisture value when tested on unseen data.…”

Section: Introductionmentioning

confidence: 99%

Field Data Forecasting Using LSTM and Bi-LSTM Approaches

et al. 2021

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Water, an essential resource for crop production, is becoming increasingly scarce, while cropland continues to expand due to the world’s population growth. Proper irrigation scheduling has been shown to help farmers improve crop yield and quality, resulting in more sustainable water consumption. Soil Moisture (SM), which indicates the amount of water in the soil, is one of the most important crop irrigation parameters. In terms of water usage optimization and crop yield, estimating future soil moisture (forecasting) is an essentially valuable task for crop irrigation. As a result, farmers can base crop irrigation decisions on this parameter. Sensors can be used to estimate this value in real time, which may assist farmers in deciding whether or not to irrigate. The soil moisture value provided by the sensors, on the other hand, is instantaneous and cannot be used to directly compute irrigation parameters such as the best timing or the required water quantity to irrigate. The soil moisture value can, in fact, vary greatly depending on factors such as humidity, weather, and time. Using machine learning methods, these parameters can be used to predict soil moisture levels in the near future. This paper proposes a new Long-Short Term Memory (LSTM)-based model to forecast soil moisture values in the future based on parameters collected from various sensors as a potential solution. To train and validate this model, a real-world dataset containing a set of parameters related to weather forecasting, soil moisture, and other related parameters was collected using smart sensors installed in a greenhouse in Chiang Mai province, Thailand. Preliminary results show that our LSTM-based model performs well in predicting soil moisture with a 0.72% RMSE error and a 0.52% cross-validation error (LSTM), and our Bi-LSTM model with a 0.76% RMSE error and a 0.57% cross-validation error. In the future, we aim to test and validate this model on other similar datasets.

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Machine learning methods for soil moisture prediction in vineyards using digital images

Cited by 16 publications

References 17 publications

Machine Learning Models for Enhanced Estimation of Soil Moisture Using Wideband Radar Sensor

Machine Learning Models for Enhanced Estimation of Soil Moisture Using Wideband Radar Sensor

A Comprehensive Review of Emerging Technologies: Machine Learning and UAV in Crop Management

Field Data Forecasting Using LSTM and Bi-LSTM Approaches

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