A. Ballado, J. Lazaro, Glenn O. Avendaño, Clarice An Rosette M. De Claro, Gabriel Kristofer Sandoval, Rez A. Viloria
{"title":"利用快速傅立叶变换表征热带植物的光谱特征","authors":"A. Ballado, J. Lazaro, Glenn O. Avendaño, Clarice An Rosette M. De Claro, Gabriel Kristofer Sandoval, Rez A. Viloria","doi":"10.1109/HNICEM.2017.8269552","DOIUrl":null,"url":null,"abstract":"In classifying plant leaves' spectral signature, different approach were already taken to achieve the general results. There are different Spectral Signatures that a leaf may possess on each reading using the standard and calibrated in the field. These are Spectral properties of different tropical plants found in tropical regions. This study focus on the spectral readings and variations in the spectral signatures found on leaf specifically plants that grows on Arid lands, Wet lands, and Near the Sea. The study uses a mechanism that shows how Spectral Signatures of these plants may vary in readings based on the location where they can be found. This study applies the algorithm known as Fast Fourier Transform in analyzing the gathered spectral data from tropical plants using MATLAB. The processed Reflectance through FFT shows how the Spectral Signature of each plant per location are different from each other. The following values is the peak values of Reflectance — FFT results for each plant: Banana 0.9466 @190Hz (Arid land), 1.112 @190Hz (Near the Sea) and 1.5408 @188Hz (Wet land); Mango: 0.723 @189Hz (Arid land), 1.2401 @189Hz (Near the Sea) and 1.634 @183Hz (Wet land); Coconut: 1.1278 @194Hz (Arid land), 0.8246 @194Hz (Near the Sea) and 1.0816 @194Hz (Wet land); Avocado: 0.8164 @194Hz (Arid land), 0.3852 @194Hz (Near the Sea) and 0.9661 @190Hz (Wet land); Pineapple: 0.654 @189Hz (Arid land), 1.3586 @189Hz (Near the Sea) and 1.524 @183Hz (Wet land).","PeriodicalId":104407,"journal":{"name":"2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)","volume":"464 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Characterization using spectral signature of tropical plants through fast fourier transform\",\"authors\":\"A. Ballado, J. Lazaro, Glenn O. Avendaño, Clarice An Rosette M. De Claro, Gabriel Kristofer Sandoval, Rez A. Viloria\",\"doi\":\"10.1109/HNICEM.2017.8269552\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In classifying plant leaves' spectral signature, different approach were already taken to achieve the general results. There are different Spectral Signatures that a leaf may possess on each reading using the standard and calibrated in the field. These are Spectral properties of different tropical plants found in tropical regions. This study focus on the spectral readings and variations in the spectral signatures found on leaf specifically plants that grows on Arid lands, Wet lands, and Near the Sea. The study uses a mechanism that shows how Spectral Signatures of these plants may vary in readings based on the location where they can be found. This study applies the algorithm known as Fast Fourier Transform in analyzing the gathered spectral data from tropical plants using MATLAB. The processed Reflectance through FFT shows how the Spectral Signature of each plant per location are different from each other. The following values is the peak values of Reflectance — FFT results for each plant: Banana 0.9466 @190Hz (Arid land), 1.112 @190Hz (Near the Sea) and 1.5408 @188Hz (Wet land); Mango: 0.723 @189Hz (Arid land), 1.2401 @189Hz (Near the Sea) and 1.634 @183Hz (Wet land); Coconut: 1.1278 @194Hz (Arid land), 0.8246 @194Hz (Near the Sea) and 1.0816 @194Hz (Wet land); Avocado: 0.8164 @194Hz (Arid land), 0.3852 @194Hz (Near the Sea) and 0.9661 @190Hz (Wet land); Pineapple: 0.654 @189Hz (Arid land), 1.3586 @189Hz (Near the Sea) and 1.524 @183Hz (Wet land).\",\"PeriodicalId\":104407,\"journal\":{\"name\":\"2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)\",\"volume\":\"464 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HNICEM.2017.8269552\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HNICEM.2017.8269552","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Characterization using spectral signature of tropical plants through fast fourier transform
In classifying plant leaves' spectral signature, different approach were already taken to achieve the general results. There are different Spectral Signatures that a leaf may possess on each reading using the standard and calibrated in the field. These are Spectral properties of different tropical plants found in tropical regions. This study focus on the spectral readings and variations in the spectral signatures found on leaf specifically plants that grows on Arid lands, Wet lands, and Near the Sea. The study uses a mechanism that shows how Spectral Signatures of these plants may vary in readings based on the location where they can be found. This study applies the algorithm known as Fast Fourier Transform in analyzing the gathered spectral data from tropical plants using MATLAB. The processed Reflectance through FFT shows how the Spectral Signature of each plant per location are different from each other. The following values is the peak values of Reflectance — FFT results for each plant: Banana 0.9466 @190Hz (Arid land), 1.112 @190Hz (Near the Sea) and 1.5408 @188Hz (Wet land); Mango: 0.723 @189Hz (Arid land), 1.2401 @189Hz (Near the Sea) and 1.634 @183Hz (Wet land); Coconut: 1.1278 @194Hz (Arid land), 0.8246 @194Hz (Near the Sea) and 1.0816 @194Hz (Wet land); Avocado: 0.8164 @194Hz (Arid land), 0.3852 @194Hz (Near the Sea) and 0.9661 @190Hz (Wet land); Pineapple: 0.654 @189Hz (Arid land), 1.3586 @189Hz (Near the Sea) and 1.524 @183Hz (Wet land).