精準農業之整合植物健康診斷與灌溉系統

Abstract

中文摘要 這篇論文針對農業中技術勞動力短缺的問題，提出了一個創新的AI驅動自動化系統。該系統將先進的視覺目標識別技術與四軸機械框架整合，提供基於圖像的植物護理評估的端對端解決方案。這包括了肥料施用、自動灌溉、產品成熟度識別（以便銷售）、以及成長週期追蹤和未來發展分析等關鍵任務的自動化。系統利用約926張不同狀態的 Catharanthus roseus 植物圖像數據集，設計了圖像分類、目標檢測和分割的自動識別過程，專注於葉片狀況和花朵計數。綜合實驗顯示，系統的高效能表現為：葉片分割的平均準確率約為95%，葉片枯萎分析的整體準確率為96.20%，葉片下垂分類的準確率為97.59%，花朵計數任務的F1分數為85.37%。這一創新旨在革新作物管理實踐，提高操作效率，並通過解決勞動力短缺問題和提升植物護理效率，促進農業生產力的提升及「農業4.0」的出現。

Abstract Addressing the significant skilled labor shortage in agriculture, this paper proposes an innovative AI-driven automation system that integrates advanced visual object recognition with a four-axis mechanical framework to provide an end-to-end solution for image-based plant care assessment. This includes the automation of essential tasks such as fertilization, watering, identification of product maturity for sale, and growth cycle tracking and analysis for future development. Utilizing a dataset of approximately 926 images of Catharanthus roseus plants in various states, the system designs automated recognition processes for image classification, object detection, and segmentation, focusing on leaf conditions and flower counting. Comprehensive experiments demonstrate the system's high efficacy, with an average accuracy of approximately 95% for leaf segmentation, 96.20% overall accuracy in leaf withering analysis, 97.59% accuracy in leaf drooping classification, and an F1-score of 85.37% for the flower counting task. This innovation aims to revolutionize crop management practices, enhance operational efficiency, and contribute to the advancement of agriculture by addressing labor shortages and improving plant care efficiency, ultimately promoting agricultural productivity and the emergence of Agriculture 4.0.