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Title:	農作物空拍影像種類辨識與無人機農檢任務應用效能評估 Assessment of Crop Aerial Image Classification and Performance Evaluation of Drone-Based Agricultural Inspection Tasks
Authors:	蔡孟宗 Tsai, Meng-Tsung
Contributors:	劉吉軒彭彥璁蔡孟宗 Tsai, Meng-Tsung
Keywords:	無人機人工智慧深度學習農作物辨識物件分類語意分割 UAV artificial intelligence deep learning crop identification object classification semantic segmentation
Date:	2025
Issue Date:	2025-03-03 14:29:16 (UTC+8)
Abstract:	本研究旨在建立本研究旨在將無人機與AI模型整合，進行農作物種類辨別以及農田面積估算。使用無人機收集欲辨識之農作物照片及高空農田照片為資料來源。以收集之農作物照片，對物件分類模型（Zhao等人，2017）進行微調，此模型用以農作物種類辨識之用。以收集之高空農田影像，對語意分割（Wang等人，2018）模型進行微調，此模型用於控制無人機飛行高度，飛行高度需達到可辨識所指定之完整農田面積。系統成效評估是基於以下原則，準確性、效率性、安全性。首先，系統需具備高準確度，以確保農作物種類辨識及農田面積估算的精確性。其次，系統需具備高效性，以快速完成巡查工作，減少人工干預。最後，系統需確保巡查人員的安全，降低人員出行的風險。本研究的主要貢獻在於提升農田作物巡檢效率，使用無人機替代人工巡檢行為，從而降低人力成本與時間以及巡檢之風險。透過微調的物件分類模型，進行農作物辨識，為農田作物種類提供更可靠的數據支持以及記錄相關影像作為日後查驗。同時，使用語意分割模型的應用使農田面積估算，研究使用語意分割模型進行無人機飛行高度控制。此外，本研究優化了農業補助申請流程，透過自動化檢查與數據記錄，提高巡檢工作的效率與準確性，減少人工作業的誤差。無人機與 AI 技術的整合為農業應用提供了實際資料佐證，推動精準農業的發展，同時降低巡查人員的安全風險，並減少農業活動對環境的影響。最終，本研究為建立可持續的農業巡檢系統奠定基礎，助力實現智慧農業與可持續發展的長遠目標。 The purpose of this study is to integrate drones with AI models for crop type identification and farmland area estimation. Drones will be used to collect photos of the crops to be identified and aerial images of the farmland as data sources. The collected crop photos will be used to fine-tune the object classification model, which is intended for crop type identification. The collected aerial farmland images will be used to fine-tune the semantic analysis model, which is used to control the flight altitude of the drone, ensuring the altitude is sufficient to recognize the specified complete farmland area. The effectiveness evaluation of the system is based on the principles of accuracy, efficiency, and safety. First, the system must have high accuracy to ensure precise crop type identification and farmland area estimation. Second, the system must be efficient to complete inspection tasks quickly and reduce manual intervention. Lastly, the system must ensure the safety of inspection personnel, reducing the risks associated with their fieldwork. This study enhances the efficiency of crop inspection in agricultural fields by utilizing unmanned aerial vehicles (UAVs) to replace manual inspections, thereby reducing labor costs, time consumption, and operational risks. Through the fine-tuning of an object classification model, the system enables accurate crop identification, providing reliable data support for crop classification while recording relevant images for future verification. Additionally, the application of a semantic segmentation model facilitates precise farmland area estimation and is employed to control UAV flight altitude, ensuring optimal data acquisition. Furthermore, this study optimizes the agricultural subsidy application process by integrating automated inspection and data recording, improving monitoring efficiency and accuracy while minimizing human errors. The integration of UAVs with artificial intelligence (AI) technology provides empirical validation for agricultural applications, advancing precision agriculture while reducing inspection risks and mitigating environmental impacts. Ultimately, this research lays the foundation for a sustainable agricultural inspection system, contributing to the long-term goals of smart agriculture and sustainable development.
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Description:	碩士國立政治大學資訊科學系碩士在職專班 111971015
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0111971015
Data Type:	thesis
Appears in Collections:	[Executive Master Program of Computer Science of NCCU] Theses

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