有鑑於以飛機空拍可以大量快速取得農田農作物的影像資料與技術相當重要，然而在資訊海量(Big-Data)的時代處理這些資料轉成有用的資訊是件困難而辛苦的工作，傳統上大部分的研究著眼於單一項目的底表觀測影像之分類如水稻，本研究為科技部產學合作案，為了協助中興測量公司，開發多種農作物識別系統，以多目標決策的方式識別嘉義黃金廊道四種主要作物。研究方法中以粗集合理論(RSES)為主軸，經層狀規則後處理， 進行建立四種農作物的識別過程，發展出有效的多目標決策之分類器。

To take photos through airplane is an important technique by obtaining the crop information. However, the in the Big Data ages, it is a tough work to handle them well and to transfer them as available information. Massive past studies focused on the single target category classify method such as paddy rice. This project is entrusted by Chung Hsing Survey Company to help them carry out to build a multi-decision system. The system can automatically discern four corps: rice, corn, cauliflower and cabbage. The Rough-Set theory is used to resolve the problem. The layer-rules are used to process the four major corps. This system can effectively classify the corps on the farmland.