Integration of Convolutional Neural Network and Thermal Images into Soil Moisture Estimation

Sobayo, Remilekun; Wu, Hsiang-Huang; Ray, Ram L.; Qian, Lijun

doi:10.1109/icdis.2018.00041

Cited by 29 publications

(15 citation statements)

References 3 publications

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“…Vegetation biomass [22,103] nitrogen status [22,99,103,110] moisture content [109,110] vegetation color [49,54] spectral behavior of chlorophyll [64,99] temperature [64,69] spatial position of an object [32,106] size and shape of different elements and plants vegetation indices [54][55][56] Soil moisture content [109,112] temperature [66,69] electrical conductivity [66] With the use of specialized sensors, UAVs can acquire information for various features of the cultivated field. However, as mentioned above, there is still no standardized workflow or well established techniques to follow for analyzing and visualizing the information acquired.…”

Section: Crop Featuresmentioning

confidence: 99%

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A Review on UAV-Based Applications for Precision Agriculture

2019

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Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.

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Section: Crop Featuresmentioning

confidence: 99%

“…In addition to the common applications mentioned above, UAVs have also been used for soil analysis [108,112], cotton genotype selection [48], mammal detection [24], and assessment of soil electrical conductivity [66].…”

mentioning

confidence: 99%

A Review on UAV-Based Applications for Precision Agriculture

2019

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“…Lastly, a CNN-BLSTM-based temperature prediction model was constructed using 2D data, as proposed in [ 17 ]. Similar to the CNN-based temperature prediction model described above, the (1 × 5)-dimensional observed data were concatenated with (40 × 40)-dimensional RDAPS data, producing (41 × 40) image data.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…This discrepancy in the format was overcome by converting the 2D CMAQ model data into a one-dimensional (1D) time-series prior to using the machine learning and deep learning models. As another example of combining different forms of data, the soil moisture sensing data and digital elevation model data were combined into a 2D format to predict the soil moisture with the combined data input to a neural network [ 17 ].…”

Section: Related Workmentioning

confidence: 99%

Temperature Prediction Based on Bidirectional Long Short-Term Memory and Convolutional Neural Network Combining Observed and Numerical Forecast Data

Jeong

Park

Kim

et al. 2021

Sensors

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Weather is affected by a complex interplay of factors, including topography, location, and time. For the prediction of temperature in Korea, it is necessary to use data from multiple regions. To this end, we investigate the use of deep neural-network-based temperature prediction model time-series weather data obtained from an automatic weather station and image data from a regional data assimilation and prediction system (RDAPS). To accommodate such different types of data into a single model, a bidirectional long short-term memory (BLSTM) model and a convolutional neural network (CNN) model are chosen to represent the features from the time-series observed data and the RDAPS image data. The two types of features are combined to produce temperature predictions for up to 14 days in the future. The performance of the proposed temperature prediction model is evaluated by objective measures, including the root mean squared error and mean bias error. The experiments demonstrated that the proposed model combining both the observed and RDAPS image data is better in all performance measures for all prediction periods compared with the BLSTM-based model using observed data and the CNN-BLSTM-based model using RDAPS image data alone.

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“…Several studies have used DL for aerial image analysis which could be easily captured using an unmanned aerial vehicle (UAV) or drone for SM estimation. Sobayo et al [57] proposed a CNN-based regression model to estimate SM content from aerial captured thermal images from three different farm areas. The model was able to predict SM content more accurately than the plain DNN model.…”

Section: Soil Moisture Estimationmentioning

confidence: 99%

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

2021

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Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations.

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Integration of Convolutional Neural Network and Thermal Images into Soil Moisture Estimation

Cited by 29 publications

References 3 publications

A Review on UAV-Based Applications for Precision Agriculture

A Review on UAV-Based Applications for Precision Agriculture

Temperature Prediction Based on Bidirectional Long Short-Term Memory and Convolutional Neural Network Combining Observed and Numerical Forecast Data

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

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