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This study addresses the issue of apple detection and segmentation, which plays a crucial role in agricultural automation systems, by employing multi-object detection using machine learning. A Support Vector Machine (SVM) model was used to accurately distinguish apples from leaves, with apple pixels classified as red and leaf pixels as green. The performance of the SVM model was evaluated using various metrics. Key evaluation metrics included IoU (Intersection over Union), Precision, Recall, and mAP (mean Average Precision). The results showed an IoU of 0.48, a Precision of 0.51, a Recall of 0.90, and an mAP of 0.48. Consequently, the SVM model exhibited a high recall rate, successfully detecting most apples, but also had a high false-positive rate due to its low precision. In the future, the need for models that can simultaneously handle real-time processing and accurate boundary recognition is emerging, which could address a critical issue in agricultural automation systems.