Image measurements of leaf scorches on landscape trees subjected to extreme meteorological events

Wang, Fei; Omasa, Kenji

doi:10.1016/j.ecoinf.2012.07.005

Cited by 11 publications

(4 citation statements)

References 27 publications

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“…Image analysis using expert systems plays an important role in determining plant characteristics holistically. Such systems measure the heterogeneity of leaf pieces to determine leaf scorch, which in turn helps determine symptoms of leaf damage [54]. Image analysis particularly helps in two main fields of agriculture, namely ecological informatics and biometeorology, which require the quick interpretation of plant photo images to save time in the treatment process.…”

Section: Expert Systems In Agriculturementioning

confidence: 99%

Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review

et al. 2023

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Due to the increasing global population and the growing demand for food worldwide as well as changes in weather conditions and the availability of water, artificial intelligence (AI) such as expert systems, natural language processing, speech recognition, and machine vision have changed not only the quantity but also the quality of work in the agricultural sector. Researchers and scientists are now moving toward the utilization of new IoT technologies in smart farming to help farmers use AI technology in the development of improved seeds, crop protection, and fertilizers. This will improve farmers' profitability and the overall economy of the country. AI is emerging in three major categories in agriculture, namely soil and crop monitoring, predictive analytics, and agricultural robotics. In this regard, farmers are increasingly adopting the use of sensors and soil sampling to gather data to be used by farm management systems for further investigations and analyses. This article contributes to the field by surveying AI applications in the agricultural sector. It starts with background information on AI, including a discussion of all AI methods utilized in the agricultural industry, such as machine learning, the IoT, expert systems, image processing, and computer vision. A comprehensive literature review is then provided, addressing how researchers have utilized AI applications effectively in data collection using sensors, smart robots, and monitoring systems for crops and irrigation leakage. It is also shown that while utilizing AI applications, quality, productivity, and sustainability are maintained. Finally, we explore the benefits and challenges of AI applications together with a comparison and discussion of several AI methodologies applied in smart farming, such as machine learning, expert systems, and image processing.

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Section: Expert Systems In Agriculturementioning

confidence: 99%

Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review

et al. 2023

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“…The appearance of scorched symptoms in the tips and margins of the leaves of many tree species is related to their water metabolism (Wang and Omasa, 2012;Wang et al, 2009aWang et al, , 2009b, particularly during periods of summer drought. Similarly, the scorched patterns observed in the tips and margins of Japanese spindle during the winter freezing dehydration process appear to be also related to a water imbalance.…”

Section: Identifying the Leaf Tip And Margin Scorches Using Thermal Imentioning

confidence: 99%

“…No special constraint was used in the determination of the leaf scorch area percent (LSAP), with the exception of blurry images. The LSAP values, which are a proportion between the scorched area and total area of the leaves, were defined and calculated using the method described by Wang and Omasa (2012).…”

Section: Analysis Of Rgb Imagesmentioning

confidence: 99%

Thermographic analysis of leaf water and energy information of Japanese spindle and glossy privet trees in low temperature environment

Wang¹,

Omasa²,

Xing³

et al. 2013

Ecological Informatics

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“…In the present study, the color of canopy RGB images has also been analyzed using the RGB analysis method (Wang and Omasa, 2012) accompanying with G/L value calculation (Wang et al, 2009a).…”

Section: Introductionmentioning

confidence: 99%

Digital image analysis of different crown shape of Platycladus orientalis

Wang

Yamamoto

et al. 2016

Ecological Informatics

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Image measurements of leaf scorches on landscape trees subjected to extreme meteorological events

Cited by 11 publications

References 27 publications

Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review

Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review

Thermographic analysis of leaf water and energy information of Japanese spindle and glossy privet trees in low temperature environment

Digital image analysis of different crown shape of Platycladus orientalis

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