<![CDATA[Automating weed removal in lettuce cultivation requires a lightweight and fast computer vision system for implementation on mobile or edge devices. This study aims to analyze the minimum number of features extracted from the MobileNetV2 architecture to accurately classify lettuce and weeds. Features extracted from the global average pooling layer of MobileNetV2 yielded 1,280 base features. The SelectKBest method with Mutual Information criteria was used to reduce the feature dimensionality, followed by classification using a Support Vector Machine (SVM) based on the Radial Basis Function (RBF) kernel. Experimental results showed that the model achieved 100% accuracy using only two minimum features, representing a feature reduction of 99.84%. This feature reduction significantly speeds up computation time, making it ideal for mobile-based computing in the smart agriculture sector.]]>
