Using UAV-based thermal imagery to detect crop water status variability in cotton

Lacerda, Lorena N.; Snider, John L.; Cohen, Yafit; Liakos, V.; Gobbo, Stefano; Vellidis, George

doi:10.1016/j.atech.2021.100029

Cited by 17 publications

(5 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each tile was painted with a matte paint in either white, gray, or black to account for different absorptive properties. Additional details on the tile construction process can be found in Lacerda et al (2022) [22]. To collect other ground-truth temperature data, the FLIR E6-XT handheld was set to record two images simultaneously: both the thermal image in the rjpg file format and the standard RGB image, which would aid in surface type interpretation alongside the thermal image.…”

Section: Methodsmentioning

confidence: 99%

“…UAVs present the opportunity for fine-resolution spatial data over larger areas and are currently implemented in other fields, such as precision agriculture and geology [19][20][21][22]. In contrast, a limited number of studies have leveraged the potential of UAVs in urban environments for any application, including addressing the gap in the urban surface temperature literature [23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Surface Heat Monitoring with High-Resolution UAV Thermal Imaging: Assessing Accuracy and Applications in Urban Environments

Henn,

Peduzzi

2024

Remote Sensing

View full text Add to dashboard Cite

The urban heat island (UHI) effect, where urban areas experience higher temperatures than surrounding rural regions, necessitates effective monitoring to estimate and address its diverse impacts. Many existing studies on urban heat dynamics rely on satellite data with coarse resolutions, posing challenges in analyzing heterogeneous urban surfaces. Unmanned aerial vehicles (UAVs) offer a solution by providing thermal imagery at a resolution finer than 1 m. Despite UAV thermal imaging being extensively explored in agriculture, its application in urban environments, specifically for surface temperatures, remains underexplored. A pilot project conducted in Athens, Georgia, utilized a UAV with a FLIR Vue Pro R 640 thermal camera to collect thermal data from two neighborhoods. Ground data, obtained using a handheld FLIR E6-XT infrared imaging camera, were compared with UAV thermal imagery. The study aimed to assess the accuracy of the UAV camera and the handheld camera for urban monitoring. Initial testing revealed the handheld’s accuracy but tendency to underpredict, while UAV camera testing highlighted considerations for altitude in both the rjpg and tiff image pixel conversion models. Despite challenges, the study demonstrates the potential of UAV-derived thermal data for monitoring urban surface temperatures, emphasizing the need for careful model considerations in data interpretation.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Surface Heat Monitoring with High-Resolution UAV Thermal Imaging: Assessing Accuracy and Applications in Urban Environments

Henn,

Peduzzi

2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Thus, two recent research studies [107,108] propose the use of quantum sensors for the precise measurement of photosynthetically active radiation (PAR), which is regarded as one of the most important environmental metrics required to evaluate plant photosynthesis. Furthermore, in [109], the authors employed devices equipped with multiple quantum sensors in order to precisely measure PAR as part of a cost-effective UAV-based thermal system to generate predicted maps of leaf water potential (LWP). Moreover, quantum sensors can be employed to accurately measure pH levels and ions, enabling the precise assessment of essential nutrients such as nitrogen, phosphorus, and potassium, which facilitates the implementation of precise fertilization strategies.…”

Section: Enhancing Sensing Accuracy and Reliabilitymentioning

confidence: 99%

The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review

Polymeni,

Plastras,

Skoutas

et al. 2023

Electronics

View full text Add to dashboard Cite

Throughout human history, agriculture has undergone a series of progressive transformations based on ever-evolving technologies in an effort to increase productivity and profitability. Over the years, farming methods have evolved significantly, progressing from Agriculture 1.0, which relied on primitive tools, to Agriculture 2.0, which incorporated machinery and advanced farming practices, and subsequently to Agriculture 3.0, which emphasized mechanization and employed intelligent machinery and technology to enhance productivity levels. To further automate and increase agricultural productivity while minimizing agricultural inputs and pollutants, a new approach to agricultural management based on the concepts of the fourth industrial revolution is being embraced gradually. This approach is referred to as “Agriculture 4.0” and is mainly implemented through the use of Internet of Things (IoT) technologies, enabling the remote control of sensors and actuators and the efficient collection and transfer of data. In addition, fueled by technologies such as robotics, artificial intelligence, quantum sensing, and four-dimensional communication, a new form of smart agriculture, called “Agriculture 5.0,” is now emerging. Agriculture 5.0 can exploit the growing 5G network infrastructure as a basis. However, only 6G-IoT networks will be able to offer the technological advances that will allow the full expansion of Agriculture 5.0, as can be inferred from the relevant scientific literature and research. In this article, we first introduce the scope of Agriculture 5.0 as well as the key features and technologies that will be leveraged in the much-anticipated 6G-IoT communication systems. We then highlight the importance and influence of these developing technologies in the further advancement of smart agriculture and conclude with a discussion of future challenges and opportunities.

show abstract

“…In parallel, PSO's global optimisation capability avoids the issue of artificial neural networks getting trapped in local optima during parameter optimisation. Cotton's water status can be categorised into five stages based on the magnitude of the leaf water potential values [24], as shown in Table 1. In the present study, both the LS-SVM and PSO-LS-SVM models were implemented with a radial basis kernel function.…”

Section: Establishment and Parameter Optimisation Of The Ls-svm Leaf ...mentioning

confidence: 99%

A Cotton Leaf Water Potential Prediction Model Based on Particle Swarm Optimisation of the LS-SVM Model

Gao,

Zhao,

Zheng

et al. 2023

Agronomy

View full text Add to dashboard Cite

Frequent monitoring of crop moisture levels can significantly improve crop production efficiency and optimise water resource utilisation. The aim of the present study was to generate moisture status maps using thermal infrared imagery, centring on the development of a predictive model for the cotton leaf water potential. The model was constructed using particle swarm optimisation (PSO) in conjunction with the least squares support vector machine (LS-SVM). Traditional SVM models suffer from high computational complexity, long training times, and inequality constraints in predicting leaf water potential. To address such issues, the PSO algorithm was introduced to improve the performance of the LS-SVM model. The PSO-optimised LS-SVM model exhibited notable improvements in performance when evaluated on two distinct test datasets (Alaer and Tumushuke). The research results indicate that the predictive accuracy of the PSO-LS-SVM model significantly improved, as evidenced by an increase of 0.05 and 0.04 in the R2 values, both of which reached 0.95. This improvement is reflected in the corresponding RMSE values, which were reduced to 0.100 and 0.103. Furthermore, a model was established based on data from three cotton growth stages, achieving high predictive accuracy even with fewer training samples. By using the PSO-LS-SVM model to predict leaf water potential information, the predicted data were mapped onto drone images, enabling the transformation of the leaf water potential from a point to an area. The present findings contribute to a more comprehensive understanding of the cotton leaf water potential by visually representing the spatial distribution of crop water status on a large scale. The results hold substantial significance for the improvement of crop irrigation management.

show abstract

Using UAV-based thermal imagery to detect crop water status variability in cotton

Cited by 17 publications

References 46 publications

Surface Heat Monitoring with High-Resolution UAV Thermal Imaging: Assessing Accuracy and Applications in Urban Environments

Surface Heat Monitoring with High-Resolution UAV Thermal Imaging: Assessing Accuracy and Applications in Urban Environments

The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review

A Cotton Leaf Water Potential Prediction Model Based on Particle Swarm Optimisation of the LS-SVM Model

Contact Info

Product

Resources

About