近年來，深度學習已成為智慧農業和精準農業的變革性補充，對已開發國家產生了重大影響。然而，在發展中國家，特別是小農中，這些技術的廣泛採用受到限制。這項研究的重點是在智慧農業和精準農業的背景下增強影像分類，特別是利用新型深度學習架構的基本屬性。農產品通常表現出獨特的表面紋理，我們的目標是利用這一點。我們的方法名為上下文優化殘差網路（CORNet），採用深度學習技術，透過合併特定層級的資訊並利用上下文選擇機制來增強功能。透過利用作物圖像中存在的上下文相關性，我們的方法證明了上下文選擇機制的有效性。此外，我們調整模型中的特徵提取參數和池化層。我們使用源自印尼真實可可豆產業的可可豆資料集以及現成的開源資料集來評估我們的模型。

In the recent years, deep learning has emerged as a transformative addition to smart farming and precision agriculture, significantly impacting developed nations. However, in developing countries, particularly among smallholder farmers, the widespread adoption of these technologies has been limited. This study focuses on enhancing image classification in the context of smart farming and precision agriculture, especially in exploiting fundamental properties in novel deep learning architectures. Agricultural products often exhibit distinct surface textures, which we aim to leverage. Our approach, named Context Optimized Residual Network (CORNet), employs deep learning techniques to enhance features by incorporating level-specific information and utilizing a context selection mechanism. By capitalizing on the contextual correlations present in crop images, our method demonstrates the effectiveness of the context selection mechanism. Further, we adjust feature extraction parameters and pooling layers in our model. We evaluated our model using the cocoa bean dataset derived from the real cocoa bean industry in Indonesia, as well as readily available open source datasets.

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