Intelligent thermal image-based sensor for affordable measurement of crop canopy temperature

Giménez-Gallego, Jaime; González-Teruel, Juan D.; Vallés, Fulgencio Soto; Jiménez-Buendía, Manuel; Navarro-Hellín, H.; Torres-Sánchez, Roque

doi:10.1016/j.compag.2021.106319

Cited by 30 publications

(35 citation statements)

References 52 publications

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“…The very common purpose is for monitoring agricultural fields. The thermal camera is used for detecting free-range rabbits [7], assessing crop water stress [8], measuring crop canopy temperature [9] and detection of stress response in grapevine [4]. The high price of thermal cameras, make them difficult to obtain by the common farmers.…”

Section: Methodsmentioning

confidence: 99%

The Smart Agriculture based on Reconstructed Thermal Image

Ismail

2022

j.inf.tech.comp.eng

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The utilization of thermal image in supporting precision agriculture is tremendous nowadays. There are many applications of thermal images in agricultural fields, such as detecting crop water stress, monitoring of free-range rabbits, measuring of crop canopy temperature and so on. Furthermore, the importance of thermal camera became the urgent need of perform the smart agriculture. Otherwise, the price of thermal camera is very expensive todays. Then, this kind of camera is not easy to find in the market. Therefore, it makes the utilization of implementation thermal images difficult. In order to handle this problem, the proposed method intends to generate thermal image from visible images. Further, the thermal information concerning with the agriculture, especially the fertility of leaves in paddy fields and the water stress can be monitored. The proposed method uses deep learning architecture to learn the thermal and visible image dataset. It applies Generative Adversarial Network architecture. This GAN pre-trained model trained using 150 images of training dataset and tested using many images of testset. The obtained model is used for generating thermal images from visible images. The results show the constructed thermal image has high accuracy. The assessment metric uses SSIM and PSNR methods. Their indexes show that the results have the high accuracy. The visual assessment shows the reconstructed thermal images also have high precision. Finally, the constructed thermal images can be implemented in smart agriculture purposes.

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Section: Methodsmentioning

confidence: 99%

The Smart Agriculture based on Reconstructed Thermal Image

Ismail

2022

j.inf.tech.comp.eng

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“…In general, canopy temperature is extracted from thermal images to calculate CWSI. Reported research has found that thermal imagery could be an ideal approach for the measurement of canopy temperature in field environments [15][16][17]. Region of interest (ROI) analysis has been utilized to estimate the mean temperature of canopies using ground platform-based images.…”

Section: Introductionmentioning

confidence: 99%

Ground-Based Thermal Imaging for Assessing Crop Water Status in Grapevines over a Growing Season

et al. 2022

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The quality of wine grapes in dry climates greatly depends on utilizing optimal amounts of irrigation water during the growing season. Robust and accurate techniques are essential for assessing crop water status in grapevines so that both over-irrigation and excessive water deficits can be avoided. This study proposes a robust strategy to assess crop water status in grapevines. Experiments were performed on Riesling grapevines (Vitis vinfera L.) planted in rows oriented north–south and subjected to three irrigation regimes in a vineyard maintained at an experimental farm in southeastern Washington, USA. Thermal and red–green–blue (RGB) images were acquired during the growing season, using a thermal imaging sensor and digital camera installed on a ground-based platform such that both cameras were oriented orthogonally to the crop canopy. A custom-developed algorithm was created to automatically derive canopy temperature (Tc) and calculate crop water stress index (CWSI) from the acquired thermal-RGB images. The relationship between leaf water potential (Ψleaf) and CWSI was investigated. The results revealed that the proposed algorithm combining thermal and RGB images to determine CWSI can be used for assessing crop water status of grapevines. There was a correlation between CWSI and Ψleaf with an R-squared value of 0.67 for the measurements in the growing season. It was also found that CWSI from the shaded (east) side of the canopy achieved a better correlation with Ψleaf compared to that from the sunlit (west) side around solar noon. The created algorithm allowed real-time assessment of crop water status in commercial vineyards and may be used in decision support systems for grapevine irrigation management.

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“…In addition, the overall temperature of a vine or canopy temperature is an important consideration. Canopy temperature can be measured by infrared thermometry, including remote thermal imaging, and is therefore non-invasive and non-destructive (Leinonen and Jones, 2004; Giménez-Gallego et al, 2021). Temperature can influence many developmental processes in grapevines, with higher temperatures accelerating development, including the timing of budbreak, bloom, and onset of fruit ripening, a particular concern in the face of climate change (Keller and Tarara, 2010; Parker et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Grapevine leaf size influences vine canopy temperature

Migicovsky

Swift

Helget

et al. 2022

Preprint

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Premise: Grapevine leaves have diverse shapes and sizes. Their shape and size is known to be influenced by many factors including genetics, vine phytosanitary status, environment, leaf and vine age, and node position on the shoot. In order to determine the importance of grapevine leaf shape and size to canopy temperature, we examined the relationship in five seedling populations grown in a vineyard in California, USA. Methods: All of the populations had one parent with compound leaves of the Vitis piasezkii type and each population had a different second parent with non-compound leaves. In previous work, we measured leaf shape and size using 21 homologous landmarks. Here, we paired these morphology data with measurements taken using an infrared thermometer to measure the temperature of the canopy. By recording time of sampling and canopy temperature, we were able to determine which vines were cooler or hotter than expected, using a linear model. Results: We established a relationship between leaf size and canopy temperature: vines with larger leaves were cooler than expected. In contrast, leaf shape was not strongly correlated with variation in temperature. Conclusions: Ultimately, these findings indicate that vines with larger leaves may contribute to the reduction of overall vine canopy temperature, but further work is needed to determine if this is due to variation in leaf size, differences in the openness of the canopy, or other related traits.

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Intelligent thermal image-based sensor for affordable measurement of crop canopy temperature

Cited by 30 publications

References 52 publications

The Smart Agriculture based on Reconstructed Thermal Image

The Smart Agriculture based on Reconstructed Thermal Image

Ground-Based Thermal Imaging for Assessing Crop Water Status in Grapevines over a Growing Season

Grapevine leaf size influences vine canopy temperature

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