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產品展(zhan)示PRODUCTS
| 品牌 | 其他品牌 | 成像(xiang)方式 | 色散(san)型 |
|---|---|---|---|
| 價格(ge)區間 | 面(mian)議 | 使用(yong)狀態(tai) | 地面(mian) |
| 工(gong)作(zuo)原理(li) | 推(tui)掃型 | 應(ying)用領(ling)域 | 環保(bao),農林牧(mu)漁,地礦 |
本(ben)系統憑(ping)借(jie)便攜(xie)、輕(qing)巧、智(zhi)能(neng)化、即開即(ji)用(yong)、在(zai)線測量(liang)、實時分析的特點,廣泛(fan)適(shi)用於實(shi)驗(yan)室或(huo)野(ye)外(wai)等(deng)多(duo)種場(chang)景(jing),通過對(dui)葉(ye)片(pian)或(huo)冠(guan)層(ceng)水(shui)平(ping)光譜反射(she)及溫(wen)度(du)進行(xing)高(gao)分辨率(lv)成像(xiang),可應(ying)用於快速(su)無損、高通量(liang)原(yuan)位(wei)生(sheng)態遙(yao)感監測、植(zhi)被(bei)生(sheng)物及非(fei)生(sheng)物脅迫監測、植(zhi)物(wu)蒸騰(teng)及氣孔(kong)導(dao)度(du)研究(jiu)、生(sheng)物多(duo)樣性(xing)監測等(deng),尤(you)其(qi)對葉(ye)片(pian)及(ji)冠(guan)層(ceng)尺(chi)度(du)植(zhi)被(bei)生(sheng)長監測、物(wu)種多(duo)樣性(xing)調(tiao)查、環(huan)境(jing)及生(sheng)態系統動態變化(hua)等具有重要意義(yi)。

本(ben)系統主要由光(guang)譜成像(xiang)傳(chuan)感器(qi)及(ji)便攜(xie)臺(tai)架(jia)組(zu)成(cheng),成像(xiang)傳(chuan)感器(qi)包括內置推(tui)掃智(zhi)能(neng)高光譜成像(xiang)單元和LWIR紅(hong)外(wai)熱(re)成(cheng)像(xiang)單元。高光(guang)譜成像(xiang)單元集采(cai)集(ji)、分析處(chu)理、結(jie)果可視(shi)化等功(gong)能(neng)特點於壹(yi)體(ti)(ALL-IN-ONE),具(ju)備IP等(deng)級防(fang)護和全自(zi)動運行(xing)特點,內置WiFi可(ke)遠程(cheng)控(kong)制(zhi),實現(xian)無人(ren)機(ji)值守(shou)工(gong)作。曾榮(rong)獲2018年(nian)德(de)國設(she)計(ji)協會(hui)“紅(hong)點(dian)設(she)計(ji)獎(jiang)”—*的工業(ye)設計(ji)獎(jiang)項、連續(xu)兩年(nian)獲得(de)“inVISION創(chuang)意(yi)獎(jiang)”。紅(hong)外(wai)熱(re)成(cheng)像(xiang)單元具有(you)高(gao)達(da)640×512px的像(xiang)素分辨率(lv)及0.03℃超高(gao)靈(ling)敏(min)度(du),其(qi)低(di)能(neng)耗、輕量(liang)級、堅(jian)固結(jie)構設(she)計(ji)完(wan)美適(shi)用於野(ye)外(wai)復雜嚴(yan)苛條(tiao)件(jian)下原(yuan)位(wei)監測場(chang)景(jing)。
應(ying)用領(ling)域:
適用(yong)於光(guang)合作(zuo)用(yong)研究(jiu)和植(zhi)被(bei)脅迫研究(jiu),農業、林(lin)業(ye)、生(sheng)態系統監測等(deng)領域。研究(jiu)內容(rong)涉及光合活(huo)性(xing)、脅迫響應(ying)、病蟲害(hai)監測、農田測繪(hui)及普查等(deng)。

功(gong)能(neng)特點
主要技術指標:
1.系統化(hua)支(zhi)架(jia)設(she)計(ji):集全太(tai)陽(yang)光譜雙光(guang)源、成像(xiang)單元、雲臺(tai)及三腳(jiao)支(zhi)架(jia)於壹(yi)體(ti),重(zhong)約5kg,便攜(xie)組(zu)裝、易(yi)於操作
2.400-1000nm智(zhi)能(neng)高光譜成像(xiang):集光(guang)譜數據采(cai)集(ji)、自(zi)動掃描(miao)成像(xiang)、自動分析處(chu)理、可視化分析結(jie)果等功(gong)能(neng)於壹(yi)體(ti),可(ke)通過光(guang)譜特征曲(qu)線(xian)創(chuang)建(jian)App導入(ru)相機(ji)直接(jie)應(ying)用,進行(xing)性(xing)狀快速(su)篩選、檢測、識(shi)別等功(gong)能(neng)
光(guang)圈(quan)F/1.7
光(guang)譜分辨率(lv)7nm
光譜波段(duan):204,可(ke)選Bin 2x和Bin 3x
內置GPS,每個(ge)高(gao)光(guang)譜數據立(li)方均(jun)自(zi)帶(dai)地(di)理標(biao)簽,便於定(ding)位(wei)、多(duo)源信(xin)息(xi)融(rong)合分析
內置SAM算(suan)法(fa),無需任何(he)復雜處(chu)理,即(ji)可(ke)快速(su)實時顯(xian)示(shi)分析結(jie)果
自帶(dai)4.3英(ying)寸(cun)觸摸(mo)屏(ping)+13個(ge)物(wu)理(li)按(an)鍵,可(ke)快速(su)實時測量(liang)分析得(de)出結(jie)果
具備(bei)USB或WIFI遠程控(kong)制(zhi)功(gong)能(neng),可(ke)通過USB線(xian)纜(lan)或(huo)無(wu)線WIFI在(zai)軟(ruan)件(jian)中(zhong)控(kong)制(zhi)相機(ji)運行(xing)
3.7.5-13.5μm紅(hong)外(wai)熱(re)成(cheng)像(xiang)成像(xiang),非(fei)制(zhi)冷(leng)紅(hong)外(wai)焦平(ping)面檢(jian)測器(qi),640×512像(xiang)素,出(chu)廠(chang)黑(hei)體(ti)校(xiao)準(zhun),內置NUC校(xiao)準(zhun)含校(xiao)準(zhun)證書溫(wen)度(du)分辨率(lv)0.03℃,9/30/60Hz可選
測溫(wen)範圍(wei):-25℃至+150℃或+40℃至+550℃,可選1500℃
溫度(du)靈(ling)敏(min)度(du)≤0.03℃(30mK)@ 30℃;
數據傳(chuan)輸(shu):USB-3或(huo)GigE千(qian)兆以太(tai)網(wang)
光學(xue)鏡頭,可選配6.8mm、9mm、13mm、19mm鏡頭
具備(bei)14種調(tiao)色板(ban)供(gong)任意(yi)選擇,可多(duo)樣化(hua)設(she)置(zhi)熱(re)成像(xiang)假彩(cai)色
具(ju)備等溫(wen)模式、溫度(du)預(yu)警、ROI分析、溫(wen)度(du)剖面、3D溫度(du)顯(xian)示、輸(shu)出(chu)報告等(deng)功(gong)能(neng)
支(zhi)持(chi)CSV、非(fei)輻(fu)射JPEG、輻(fu)射JPEG、輻(fu)射視(shi)頻、AVI、MP4等(deng)格(ge)式輸(shu)出(chu)
防(fang)護等(deng)級(ji):IP65,適用野(ye)外(wai)嚴(yan)苛條(tiao)件(jian)下適(shi)用(yong) 

參考文(wen)獻(xian):
Jan B , Kelvin A , Dzhaner E , et al. Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection[J]. Sensors, 2018, 18(2):441-.
Xiao Z , Wang J . Rapid Nondestructive Defect Detection of Scindapsus aureus Leaves Based on PCA Spectral Feature Optimization[J]. IOP Conference Series Earth and Environmental Science, 2020, 440:032018.
Detection of Diseases on Wheat Crops by Hyperspectral Data
Barreto, Abel & Paulus, Stefan & Varrelmann, Mark & Mahlein, Anne-Katrin. (2020). Hyperspectral imaging of symptoms induced by Rhizoctonia solani in sugar beet: comparison of input data and different machine learning algorithms. Journal of Plant Diseases and Protection. 10.1007/s41348-020-00344-8.
Sajad Kiani, Saskia M. van Ruth, Leo W.D. van Raamsdonk, Saeid Minaei. Hyperspectral imaging as a novel system for the authentication of spices: A nutmeg case study. LWT - Food Science and Technology. 104(2019)61-69.
Edelman, G.J. & Aalders, M.C.G. (2018). Photogrammetry using visible, infrared, hyperspectral and thermal imaging of crime scenes. Forensic Science International. 292. 10.1016/j.forsciint.2018.09.025.
Yuan, X.; Laakso, K.; Davis, C.D.; Guzmán Q., J.A.; Meng, Q.; Sanchez-Azofeifa, A. Monitoring the Water Stress of an Indoor Living Wall System Using the “Triangle Method”. Sensors 2020, 20, 3261.
Kruglikov, N. & Danilenko, I. & Muftakhetdinova, Razilia & Petrova, Evgeniya & Grokhovsky, V.. (2019). Spectral characteristics of the meteoritic material after the modeling of thermal and shock metamorphism. AIP Conference Proceedings. 2174. 020227. 10.1063/1.5134378.






