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All Journal Hanif Journal of Information Systems
Ahmad Rofi'i
Politeknik Negeri Lampung
Computer Science & IT Library & Information Science

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Improving the Accuracy of Lettuce and Weed Classification Based on MobileNetV2 Features Through Segmentation Akhmad Jayadi; Kurniawan Saputra; Ahmad Rofi'i
Hanif Journal of Information Systems Vol. 3 No. 2 (2026): February Edition
Publisher : Ilmu Bersama Center

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Abstract

Automating the separation of commodity crops and weeds is a major challenge in the implementation of precision agriculture . The presence of complex backgrounds such as soil, rocks, and shadows often degrades the performance of feature extraction in computer vision classification models. This study proposes an image preprocessing approach using the GrabCut segmentation method to extract key crop objects cleanly before performing Deep Learning- based feature extraction . Representative features from the image are extracted using the lightweight and efficient MobileNetV2 architecture. Next, classification is performed by comparing three Machine Learning algorithms , namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF). Testing is carried out on two data scenarios, namely the original dataset ( Original ) and the segmented dataset ( GrabCut ). The experimental results show that the use of original images produces an accuracy of 98.89% for all three classification models. However, after being integrated with GrabCut segmentation, the accuracy of all three models increases significantly to 100.00%. These results prove that GrabCut-based segmentation effectively eliminates background noise information , thereby improving the generalization capabilities of classification models perfectly on edge computing devices .
Computer Vision-Based Lettuce and Plantweed Segmentation Using YOLO and Segment Anything for Precision Agriculture Akhmad Jayadi; Adi Ahmad Fauzi; Muhammad Ikhsan; Jaka Persada Sembiring; Ahmad Rofi'i
Hanif Journal of Information Systems Vol. 4 No. 1 (2026): August Edition
Publisher : Ilmu Bersama Center

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The presence of weeds in lettuce cultivation areas can reduce plant productivity because they compete for nutrients, water, and light. Manual weed identification requires considerable time and effort, so an automated system based on computer vision is needed . This study proposes the integration of the YOLO11s model and the Segment Anything Model (SAM) to detect and segment lettuce plants and weeds in agricultural environments. The dataset used consists of 741 training images , 212 validation images , and 106 testing images with two object classes, namely Lettuce and Plantweed . The YOLO11s model was trained for 100 epochs using an image size of 640 × 640 pixels and a batch size of 106 . 16. The training results show that the model obtained an mAP@50 value of 87.8%. And mAP@50–95 was 80.0% , indicating good object detection capability. Furthermore, the bounding box coordinates of the detection results were used as prompts in the Segment Anything Model to generate mask -shaped segmentations that follow the object contours more precisely. The experimental results show that the integration of YOLO11s and SAM is able to produce more detailed object representations compared to detection using bounding boxes alone. This approach has the potential to support various precision agriculture applications, such as plant morphology analysis, leaf area estimation, and the development of automated weed control systems .
Co-Authors Adi Ahmad Fauzi Akhmad Jayadi Jaka Persada Sembiring Kurniawan Saputra Muhammad Ikhsan