Outdoor infrared imaging for spatial and temporal thermography: A case study of necrotic versus healthy leaf areas on woody plants

Park, Jun Hyung; Kim, Ki-Woo

doi:10.1111/jph.12959

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…Park and Kim [50] conducted a study using infrared thermography to compare the temperatures of healthy leaves and those with necrotic spots on oriental cherry, Japanese cornel, and sawtooth oak. The results indicate a significant difference in temperature between healthy and diseased leaves.…”

Section: Related Workmentioning

confidence: 99%

Detection of Biotic or Abiotic Stress in Vineyards Using Thermal and RGB Images Captured via IoT Sensors

Fevgas,

Lagkas,

Argyriou

et al. 2023

IEEE Access

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The abiotic and biotic plant stress is a critical factor for the agriculture industry, considering that plant stress increases production costs and reduces the quantity and quality of the output product. The early detection of plant stress using new technologies and even more utilizing Internet of Things (IoT) sensors is significant for the growers to act as early as a problem is in the nascent. Furthermore, the insights from the early detection of plant stress can be used as actionable data for fertilization and pesticide optimization. Our proposed state-of-the-art method uses Thermal Infrared (TIR) and high-resolution visible-spectrum (RGB) images acquired by IoT sensors of Unmanned Aerial Vehicles (UAVs) from two experiment vineyards (Vitis vinifera L.) for two years in a total of twelve flights. The OTSU method is used for the plants' canopy isolation from the soil. The k-Means clustering is used in the relative temperature values of the plant's canopy to detect the leaves' stomatal closure. The clusters' pixel coordinates of the TIR image, which represents leaves' stressed areas, are used, and a pseudo-coloring of yellow is assigned in the corresponding pixels of the aligned RGB image. Finally, an RGB image is generated with yellow pseudo-coloring over the stressed areas of the vineyards' canopy. The stressed plants caused by abiotic and biotic factors were validated and compared with the Triangular Greenness Index (TGI), which measures the leaf chlorophyll content like other multispectral indexes. Finally, the proposed method shows significantly higher accuracy and precision than TGI based on the two years of experimentation results, considering that the F1-score of the proposed method is better than TGI in the cumulative of the two years by 70.55%.

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Section: Related Workmentioning

confidence: 99%

Detection of Biotic or Abiotic Stress in Vineyards Using Thermal and RGB Images Captured via IoT Sensors

Fevgas,

Lagkas,

Argyriou

et al. 2023

IEEE Access

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“…Temperature increases measured on pines infected with Dothistroma septosporum (causal agent of red band needle blight) and on winter wheat infected with Zymoseptoria tritici (causing septoria leaf blotch) could be positively correlated to the damages caused by these hemibiotrophic fungi [90,129]. Necrotic spots on leaves from three woody trees (oriental cherry, Japanese cornel and sawtooth oak) caused by three fungi (Mycosphaerella cerasella, Elsinoe corni and Tubakia dryina, respectively) can also be detected as dots with higher temperatures relative to healthy areas [130].…”

Section: Remote Sensing On Crop Fieldsmentioning

confidence: 99%

Thermal Imaging for Plant Stress Detection and Phenotyping

2020

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In the last few years, large efforts have been made to develop new methods to optimize stress detection in crop fields. Thus, plant phenotyping based on imaging techniques has become an essential tool in agriculture. In particular, leaf temperature is a valuable indicator of the physiological status of plants, responding to both biotic and abiotic stressors. Often combined with other imaging sensors and data-mining techniques, thermography is crucial in the implementation of a more automatized, precise and sustainable agriculture. However, thermal data need some corrections related to the environmental and measuring conditions in order to achieve a correct interpretation of the data. This review focuses on the state of the art of thermography applied to the detection of biotic stress. The work will also revise the most important abiotic stress factors affecting the measurements as well as practical issues that need to be considered in order to implement this technique, particularly at the field scale.

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“…Also, it is non-destructive approach that has recently attracted great attention as a useful tool for food quality and safety assessment 14 , 15 . The advantages of this method caused to apply thermal imaging technique to detect diseases in plant leaves 16 – 20 and diagnosis of fungal contamination in crops 21 – 23 .…”

Section: Introductionmentioning

confidence: 99%

A machine learning system to identify progress level of dry rot disease in potato tuber based on digital thermal image processing

Farokhzad,

Modaress Motlagh,

Ahmadi Moghaddam

et al. 2024

Sci Rep

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This study proposed a quick and reliable thermography-based method for detection of healthy potato tubers from those with dry rot disease and also determination of the level of disease development. The dry rot development inside potato tubers was classified based on the Wiersema Criteria, grade 0 to 3. The tubers were heated at 60 and 90 °C, and then thermal images were taken 10, 25, 40, and 70 s after heating. The surface temperature of the tubers was measured to select the best treatment for thermography, and the treatment with the highest thermal difference in each class was selected. The results of variance analysis of tuber surface temperature showed that tuber surface temperature was significantly different due to the severity of disease development inside the tuber. Total of 25 thermal images were prepared for each class, and then Otsu’s threshold method was employed to remove the background. Their histograms were extracted from the red, green, and blue surfaces, and, finally, six features were extracted from each histogram. Moreover, the co-occurrence matrix was extracted at four angles from the gray level images and five features were extracted from each co-occurrence matrix. Totally, each thermograph was described by 38 features. These features were used to implement the artificial neural networks and the support vector machine in order to classify and diagnose the severity of the disease. The results showed that the sensitivity of the models in the diagnosis of healthy tubers was 96 and 100%, respectively. The overall accuracy of the models in detecting the severity of tuber tissue destruction was 93 and 97%, respectively. The proposed methodology as an accurate, nondestructive, fast, and applicable system reduces the potato loss by rapid detection of the disease of the tubers.

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Outdoor infrared imaging for spatial and temporal thermography: A case study of necrotic versus healthy leaf areas on woody plants

Cited by 5 publications

References 35 publications

Detection of Biotic or Abiotic Stress in Vineyards Using Thermal and RGB Images Captured via IoT Sensors

Detection of Biotic or Abiotic Stress in Vineyards Using Thermal and RGB Images Captured via IoT Sensors

Thermal Imaging for Plant Stress Detection and Phenotyping

A machine learning system to identify progress level of dry rot disease in potato tuber based on digital thermal image processing

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