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袋栽菇類智慧採收模組之研究 = = Research on Intell...

郭名翔

袋栽菇類智慧採收模組之研究 = = Research on Intelligent Harvesting Module of Bag Cultivated Mushrooms /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	袋栽菇類智慧採收模組之研究 =/ 郭名翔.
其他題名:	Research on Intelligent Harvesting Module of Bag Cultivated Mushrooms /
其他題名:	Research on Intelligent Harvesting Module of Bag Cultivated Mushrooms.
作者:	郭名翔
出版者:	雲林縣 :國立虎尾科技大學 , : 民113.08.,
面頁冊數:	[8], 77面 :圖, 表 ; : 30公分.;
附註:	指導教授: 周榮源.
標題:	菇類栽培. -
電子資源:	電子資源

袋栽菇類智慧採收模組之研究 = = Research on Intelligent Harvesting Module of Bag Cultivated Mushrooms /
郭名翔

袋栽菇類智慧採收模組之研究 =Research on Intelligent Harvesting Module of Bag Cultivated Mushrooms /Research on Intelligent Harvesting Module of Bag Cultivated Mushrooms.郭名翔. - 初版. - 雲林縣 :國立虎尾科技大學 ,民113.08. - [8], 77面 :圖, 表 ;30公分.

指導教授: 周榮源.

碩士論文--國立虎尾科技大學機械設計工程系碩士班.

含參考書目.

菇類採收方式分成採摘(harvesting by picking)或切割(harvesting by cutting)兩種。傳統的人工採收方法存在勞動強度大、效率低、成本高等問題，且易受人為因素影響，導致採收品質不穩定。本研究提出AI採收高度辨識分法及改善文獻[1][2]機構之缺點，以最適裁切高度進行杏鮑菇與秀珍菇之完株採收研究，將採收機構優化並結合影像辨識系統來提升袋栽太空包完株採收品質。透過實際觀察人工採收過程之採摘與切割方式，提出以子實體與廢料之交接面為其最佳裁切位置，並透過YOLO神經網路學習，將辨識之裁切高度位置傳輸至刀具高度移動機構，使其位移至裁切高度進行裁切。此外，採收模式以單次一包方式進行採收。本研究所設計之改良版採收機構與影像辨識功能結合後，經實驗研究結果可提升菇類採收良率達77.94%。未來在智慧採收技術成熟後，將能使菇類產業朝智慧化更邁進一步。.

(平裝)Subjects--Topical Terms:

1407592
菇類栽培.

袋栽菇類智慧採收模組之研究 = = Research on Intelligent Harvesting Module of Bag Cultivated Mushrooms /
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246 1 1 $a Research on Intelligent Harvesting Module of Bag Cultivated Mushrooms.
250 $a 初版.
260 # $a 雲林縣 : $b 國立虎尾科技大學 , $c 民113.08.
300 $a [8], 77面 : $b 圖, 表 ; $c 30公分.
500 $a 指導教授: 周榮源.
500 $a 學年度: 112.
502 $a 碩士論文--國立虎尾科技大學機械設計工程系碩士班.
504 $a 含參考書目.
520 3 $a 菇類採收方式分成採摘(harvesting by picking)或切割(harvesting by cutting)兩種。傳統的人工採收方法存在勞動強度大、效率低、成本高等問題，且易受人為因素影響，導致採收品質不穩定。本研究提出AI採收高度辨識分法及改善文獻[1][2]機構之缺點，以最適裁切高度進行杏鮑菇與秀珍菇之完株採收研究，將採收機構優化並結合影像辨識系統來提升袋栽太空包完株採收品質。透過實際觀察人工採收過程之採摘與切割方式，提出以子實體與廢料之交接面為其最佳裁切位置，並透過YOLO神經網路學習，將辨識之裁切高度位置傳輸至刀具高度移動機構，使其位移至裁切高度進行裁切。此外，採收模式以單次一包方式進行採收。本研究所設計之改良版採收機構與影像辨識功能結合後，經實驗研究結果可提升菇類採收良率達77.94%。未來在智慧採收技術成熟後，將能使菇類產業朝智慧化更邁進一步。.
520 3 $a There are two types of mushroom harvesting: harvesting by picking or harvesting by cutting. Traditional manual harvesting methods have problems such as high labor intensity, low efficiency, high cost, and are susceptible to human factors, resulting in unstable harvesting quality. In this study, we proposed an AI harvesting height identification method and improved the shortcomings of the literature [1][2] mechanism, and used the optimal cutting height to study the harvesting of Pleurotus eryngii and Pleurotus ostreatus, and optimized the harvesting mechanism and combined with the image recognition system to improve the harvesting quality of bagged space wrapping plants. Through the actual observation of the picking and cutting methods in the manual harvesting process, it is proposed that the interface between the fruiting body and the waste is the best cutting position, and through the YOLO neural network learning, the identified cutting height position is transmitted to the tool height moving mechanism, so that it can be displaced to the cutting height for cutting. In addition, the harvest mode is carried out in a single package. After combining the improved harvesting mechanism and image recognition function designed in this study, the experimental results showed that the yield of mushroom harvesting could be improved by 77.94%. In the future, after the intelligent harvesting technology matures, the mushroom industry will be able to take a step forward towards intelligence..
563 $a (平裝)
650 # 4 $a 菇類栽培. $3 1407592
650 # 4 $a YOLO物件偵測. $3 1416613
650 # 4 $a 裁切高度最佳化. $3 1451984
650 # 4 $a 採收機構. $3 1384308
650 # 4 $a 智慧化. $3 1451985
650 # 4 $a Mushroom Cultivation. $3 1451986
650 # 4 $a YOLO Object Detection. $3 1451987
650 # 4 $a Optimized Cutting Height. $3 1451988
650 # 4 $a Harvesting Mechanism. $3 1451989
650 # 4 $a Intelligent. $3 1451990
856 7 # $u https://handle.ncl.edu.tw/11296/732j9a $z 電子資源 $2 http