Recently,there has been a notable surge of interest in scientific research regarding spectral images.The potential of these images to revolutionize the digital photography industry,like aerial photography through Unma...Recently,there has been a notable surge of interest in scientific research regarding spectral images.The potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable attention.One encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image classification.As a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent beneficiaries.This paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and vegetables.By meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical use.Additionally,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural context.The findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in agriculture.Nevertheless,we also shed light on the various issues and limitations of working with spectral images.This comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.展开更多
Located in Chiba Prefecture, the Yatsu Tidal Flat is an important stopover for birds migrating between cold regions such as Siberia and warm regions such as Southeast Asia and Australia. Its importance led to its sele...Located in Chiba Prefecture, the Yatsu Tidal Flat is an important stopover for birds migrating between cold regions such as Siberia and warm regions such as Southeast Asia and Australia. Its importance led to its selection in 1993 as the first tidal flat in Japan to be registered under the Convention on Wetlands of International Importance especially as Waterfowl Habitat (the Ramsar Convention). However, the Yatsu Tidal Flat has in more recent years witnessed blooms of Ulva spp. (sea lettuce) and an increase in exotic species such as Batillaria attramentaria (Japanese mud snail) and Mercenaria mercenaria (hard clam), fueling concerns that the increasing spatial domination of the tidal flat by such species and competition with other species for food may drive a decline in the habitat’s self-cleaning capabilities. For this study, we focused on Batillaria attramentaria, which is now so widely distributed in the Yatsu Tidal Flat as to preclude reliable monitoring via aerial photographs or satellite imagery. Accordingly, we tested the utility of a simplified method for obtaining data on the distribution of Batillaria attramentaria by using aerial balloon photography and a vegetation index camera capable of generating NDVI data. Our results show that under certain conditions, this method can indeed be used to determine Batillaria attramentaria distribution.展开更多
文摘Recently,there has been a notable surge of interest in scientific research regarding spectral images.The potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable attention.One encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image classification.As a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent beneficiaries.This paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and vegetables.By meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical use.Additionally,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural context.The findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in agriculture.Nevertheless,we also shed light on the various issues and limitations of working with spectral images.This comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.
文摘Located in Chiba Prefecture, the Yatsu Tidal Flat is an important stopover for birds migrating between cold regions such as Siberia and warm regions such as Southeast Asia and Australia. Its importance led to its selection in 1993 as the first tidal flat in Japan to be registered under the Convention on Wetlands of International Importance especially as Waterfowl Habitat (the Ramsar Convention). However, the Yatsu Tidal Flat has in more recent years witnessed blooms of Ulva spp. (sea lettuce) and an increase in exotic species such as Batillaria attramentaria (Japanese mud snail) and Mercenaria mercenaria (hard clam), fueling concerns that the increasing spatial domination of the tidal flat by such species and competition with other species for food may drive a decline in the habitat’s self-cleaning capabilities. For this study, we focused on Batillaria attramentaria, which is now so widely distributed in the Yatsu Tidal Flat as to preclude reliable monitoring via aerial photographs or satellite imagery. Accordingly, we tested the utility of a simplified method for obtaining data on the distribution of Batillaria attramentaria by using aerial balloon photography and a vegetation index camera capable of generating NDVI data. Our results show that under certain conditions, this method can indeed be used to determine Batillaria attramentaria distribution.
