## 2 Literature Review
### Fruits and Vegetables Detections
The authors introduce modifications to YOLOv7, including updated anchor box sizes based on observed plum sizes and an SE module for capturing channel interdependencies. The Improved-YOLOv7 model achieves promising results, with Precision, Recall, and mAP scores of 70.2%, 72.1%, and 76.8%, respectively. Comparative analysis demonstrates that the model outperforms other YOLO models in terms of accuracy and generalization in complex environments. Furthermore, in our work, we also included the class of plum, and our YOLOv7 model demonstrates greater precision and recall for this class compared to the research mentioned. In another paper [31], the author presents a fruit detection model called YOLO-Oleifera, specifically designed for oil-seed camellia fruit in orchards. It modifies the YOLOv4-tiny architecture to address challenges such as lighting changes, occlusion, and fruit overlap. The model uses the k-means++ clustering algorithm to improve bounding box priors for accurate fruit detection. Additional convolutional kernels are added to reduce computational complexity while effectively learning fruit features. The model utilizes bounding boxes for region of interest extraction and adaptive stereo matching, enabling precise fruit positioning. Ablation experiments demonstrate the effectiveness of the modifications. Testing shows robust detection performance under varying illumination conditions, with reduced precision and recall for occluded fruit. Compared to other models, YOLO-Oleifera achieves the highest Average Precision with a small data weight of 29 MB. It demonstrates real-time capability and stability in complex orchard environments, serving as a reference for mobile picking robots. In another paper [32], three models for fruit detection and classification were utilized. These models included CNN, YOLOv4, and YOLOv5. The CNN was trained on the fruit360 dataset, which consisted of 131 classes of fruits and vegetables. After fine-tuning, the CNN achieved an accuracy of 98%. However, it was observed that, despite its high accuracy, the CNN did not perform well in real-time scenarios. To address this issue, the author collected two additional datasets with 19 and 12 classes, respectively. Two YOLO models, namely YOLOv4 and YOLOv5, were trained using these datasets. The YOLOv4 model achieved an accuracy of 70%, while the YOLOv5 model achieved an accuracy of 78%. These later models demonstrated improved performance in real-time scenarios compared to the CNN, but their performance did not reach the baseline level. Furthermore, the author developed a Python script for billing purposes. However, no specific device was mentioned for weighing purposes in the research. The detection and classification focused solely on fruits and vegetables, with fixed weights and prices. As a result, these methods may not be suitable for small-scale outlets with low purchasing power, particularly in the context of the majority of the population in Pakistan. In another study [33], the author developed an Internet of Things (IoT)-based system for the sale of fruits and vegetables. The system incorporates a scale that automatically weighs and identifies the fruits and vegetables, providing a corresponding bill to the customer. To enhance the detection capability of the system, the author utilized the fruit360 dataset and supplemented it with additional collected data. The combined dataset consisted of 2100 training images. To train the detection model, the author employed a Single Shot Multi Frame Detector (SSD) implemented in Python. The SSD model takes an image as input and automatically detects and localizes objects within the image. It achieves this by framing the position of the objects and employing correction techniques to refine the object's location. The model also returns labels indicating the type of object detected. The author reported an impressive accuracy of 94.50% for the trained SSD model, accompanied by a precision of 95% and a recall of 95.09%.