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題名 以深度學習建構蔬果辨識之知識模型
Constructing knowledge model of vegetable and fruit recognition with deep learning
作者 賴暘晟
Lai, Yang-Cheng
貢獻者 羅崇銘
Lo, Chung-Ming
賴暘晟
Lai, Yang-Cheng
關鍵詞 智慧農業
深度學習
影像辨識
蔬果影像
Smart agriculture
Deep learning
Image recognition
Fruit and vegetable images
日期 2021
上傳時間 2-Sep-2021 16:36:41 (UTC+8)
摘要 食物的需求將因世界總人口的成長而跟著成長，農作物的產量供給要能滿足人們的需求；但是勞動力不足會導致農作物無法全部種植與收成而導致產量降低，智慧農業以資訊與通訊科技來解決全世界農業的產量需求以及農民的品質競爭並取代人工勞動力，其中應用於蔬果的成熟與種類判斷的即是影像辨識技術，過去的影像辨識需要人為設計特徵，技術層次高且適用性低，本研究提出使用深度學習技術進行蔬果辨識，藉由大量影像的特徵學習達到建立知識模型的目的，實驗中收集多種蔬果的數位影像並以多種深度學習技術的卷積神經網路訓練，包括採用transfer learning與train from scratch訓練方式，結果呈現上以準確率、效率、參數多寡做為比較依據，在區分15種蔬果的知識模型上，基於transfer learning的SqueezeNet準確率為99.68%與train from scratch的Densenet201可達到準確率為99.25%，但是兩者訓練時間差距約14倍，在單一蔬果不同品種知識模型與相似顏色蔬果知識模型的表現上，train from scratch的Densenet201準確率優於transfer learning的SqueezeNet準確率，除此之外，將影像資料轉成灰階影像訓練的辨識結果有達到98%以上的準確率。目前蔬果辨識之知識模型只能辨識收集來的影像資料集場景，想要應用知識模型在辨識其他的場景，則必須用該場景的影像資料再次進行訓練，才能達到足夠理想的準確率。
The demand for food will grow due to the growth of the world`s total population. The output of crops must be able to meet people`s needs; but the shortage of labor will cause crops to be planted and harvested, resulting in a decrease in output. Smart agriculture is solved by information and communication technology. The world’s agricultural production needs and the quality of farmers’ competition to replace human labor. Among them, image recognition technology is used to determine the maturity and variety of fruits and vegetables. In the past, image recognition required artificial design features, with high technical levels and low applicability. The research proposes the use of deep learning technology for vegetable and fruit identification. The purpose of building a knowledge model is achieved through feature learning of a large number of images. In the experiment, a variety of digital images of vegetables and fruits are collected and convolutional neural network training with a variety of deep learning techniques, including the use of transfer learning Compared with the train from scratch training method, the results are presented on the basis of accuracy, efficiency, and the number of parameters. In the knowledge model for distinguishing 15 kinds of vegetables and fruits, the accuracy of SqueezeNet based on transfer learning is 99.68% and the accuracy of Densenet201 of train from scratch is comparable. The accuracy rate is 99.25%, but the training time gap between the two is about 14 times. In terms of the performance of the knowledge model of different varieties of vegetables and fruits and the knowledge model of similar colors, the accuracy of Densenet201 of train from scratch is better than the accuracy of SqueezeNet of transfer learning. In addition, the recognition result of the training of converting the image data into gray-scale images has an accuracy rate of over 98%. The current knowledge model of vegetable and fruit identification can only identify the scenes of the collected image data set. If you want to apply the knowledge model to identify other scenes, you must use the image data of the scene to perform training again to achieve a sufficiently ideal accuracy.
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描述 碩士
國立政治大學
圖書資訊與檔案學研究所
108155021
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108155021
資料類型 thesis
dc.contributor.advisor 羅崇銘zh_TW
dc.contributor.advisor Lo, Chung-Mingen_US
dc.contributor.author (Authors) 賴暘晟zh_TW
dc.contributor.author (Authors) Lai, Yang-Chengen_US
dc.creator (作者) 賴暘晟zh_TW
dc.creator (作者) Lai, Yang-Chengen_US
dc.date (日期) 2021en_US
dc.date.accessioned 2-Sep-2021 16:36:41 (UTC+8)-
dc.date.available 2-Sep-2021 16:36:41 (UTC+8)-
dc.date.issued (上傳時間) 2-Sep-2021 16:36:41 (UTC+8)-
dc.identifier (Other Identifiers) G0108155021en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136929-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 圖書資訊與檔案學研究所zh_TW
dc.description (描述) 108155021zh_TW
dc.description.abstract (摘要) 食物的需求將因世界總人口的成長而跟著成長，農作物的產量供給要能滿足人們的需求；但是勞動力不足會導致農作物無法全部種植與收成而導致產量降低，智慧農業以資訊與通訊科技來解決全世界農業的產量需求以及農民的品質競爭並取代人工勞動力，其中應用於蔬果的成熟與種類判斷的即是影像辨識技術，過去的影像辨識需要人為設計特徵，技術層次高且適用性低，本研究提出使用深度學習技術進行蔬果辨識，藉由大量影像的特徵學習達到建立知識模型的目的，實驗中收集多種蔬果的數位影像並以多種深度學習技術的卷積神經網路訓練，包括採用transfer learning與train from scratch訓練方式，結果呈現上以準確率、效率、參數多寡做為比較依據，在區分15種蔬果的知識模型上，基於transfer learning的SqueezeNet準確率為99.68%與train from scratch的Densenet201可達到準確率為99.25%，但是兩者訓練時間差距約14倍，在單一蔬果不同品種知識模型與相似顏色蔬果知識模型的表現上，train from scratch的Densenet201準確率優於transfer learning的SqueezeNet準確率，除此之外，將影像資料轉成灰階影像訓練的辨識結果有達到98%以上的準確率。目前蔬果辨識之知識模型只能辨識收集來的影像資料集場景，想要應用知識模型在辨識其他的場景，則必須用該場景的影像資料再次進行訓練，才能達到足夠理想的準確率。zh_TW
dc.description.abstract (摘要) The demand for food will grow due to the growth of the world`s total population. The output of crops must be able to meet people`s needs; but the shortage of labor will cause crops to be planted and harvested, resulting in a decrease in output. Smart agriculture is solved by information and communication technology. The world’s agricultural production needs and the quality of farmers’ competition to replace human labor. Among them, image recognition technology is used to determine the maturity and variety of fruits and vegetables. In the past, image recognition required artificial design features, with high technical levels and low applicability. The research proposes the use of deep learning technology for vegetable and fruit identification. The purpose of building a knowledge model is achieved through feature learning of a large number of images. In the experiment, a variety of digital images of vegetables and fruits are collected and convolutional neural network training with a variety of deep learning techniques, including the use of transfer learning Compared with the train from scratch training method, the results are presented on the basis of accuracy, efficiency, and the number of parameters. In the knowledge model for distinguishing 15 kinds of vegetables and fruits, the accuracy of SqueezeNet based on transfer learning is 99.68% and the accuracy of Densenet201 of train from scratch is comparable. The accuracy rate is 99.25%, but the training time gap between the two is about 14 times. In terms of the performance of the knowledge model of different varieties of vegetables and fruits and the knowledge model of similar colors, the accuracy of Densenet201 of train from scratch is better than the accuracy of SqueezeNet of transfer learning. In addition, the recognition result of the training of converting the image data into gray-scale images has an accuracy rate of over 98%. The current knowledge model of vegetable and fruit identification can only identify the scenes of the collected image data set. If you want to apply the knowledge model to identify other scenes, you must use the image data of the scene to perform training again to achieve a sufficiently ideal accuracy.en_US
dc.description.tableofcontents 謝辭 ii
摘要 iii
Abstract iv
目次 v
圖目次 vi
表目次 viii
第一章 緒論 1
第一節 世界農業現況 1
第二節 智慧農業 2
第三節 蔬果影像辨識 7
第二章 文獻探討 9
第一節 國外蔬果影像辨識 9
第二節 國內蔬果影像辨識 14
第三節 文獻探討總結 15
第三章 材料與方法 16
第一節 三種知識模型 21
第二節 卷積神經網路 28
第四章 實驗結果 38
第一節 訓練與驗證 38
第二節 測試影像資料集 49
第五章 結論與未來方向 51
第一節 結論 51
第二節 未來方向 51
參考文獻 55		zh_TW
dc.format.extent 3821367 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108155021en_US
dc.subject (關鍵詞) 智慧農業zh_TW
dc.subject (關鍵詞) 深度學習zh_TW
dc.subject (關鍵詞) 影像辨識zh_TW
dc.subject (關鍵詞) 蔬果影像zh_TW
dc.subject (關鍵詞) Smart agricultureen_US
dc.subject (關鍵詞) Deep learningen_US
dc.subject (關鍵詞) Image recognitionen_US
dc.subject (關鍵詞) Fruit and vegetable imagesen_US
dc.title (題名) 以深度學習建構蔬果辨識之知識模型zh_TW
dc.title (題名) Constructing knowledge model of vegetable and fruit recognition with deep learningen_US
dc.type (資料類型) thesisen_US
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dc.identifier.doi (DOI) 10.6814/NCCU202101406en_US