Fruit detection plays a vital role in harvesting robot platforms. However, complicated environment attributes such as illumination variation, occlusion, have made fruit detection a challenging task. A robust YOLOMuskmelon model that is accurate and fast was proposed to solve detection difficulties. The YOLOMuskmelon model incorporated ReLU activated ResNet43 backbone with new 2,3,4,3,2 residual block arrangement, spatial pyramid pooling(SPP), complete Intersection over Union (CIoU) loss, feature pyramid network(FPN), and distance Intersection over Union−Non Maximum Suppression(DIoU−NMS) to improve detection performance. The obtained average precision (AP) results of YOLOMuskmelon at 89.6% is greater than YOLOv3 at 82.3%, YOLOResNet50 at 85.5%, but less than YOLOv4 at 91.6%. However, the detection speed of YOLOMuskmelon at 96.3 frame per second(fps) outperformed YOLOv3 at 56.6fps, YOLOv4 at 54.1fps and YOLOResNet50 at 71.2fps. Meanwhile, the YOLOMuskmelon which is 56.1% faster than YOLOv4 model showed a better generalization and real−time fruit harvesting robots prospect.INDEX TERMS YOLOMuskmelon, fruit detection, speed and accuracy, complicated environment, harvesting robots.

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The ripeness phases of jujube fruits are one factor mitigating against fruit detection, in addition to uneven environmental conditions such as illumination variation, leaf occlusion, overlapping fruits, colors or brightness, similar plant appearance to the background, and so on. Therefore, a method called YOLO-Jujube was proposed to solve these problems. With the incorporation of the networks of Stem, RCC, Maxpool, CBS, SPPF, C3, PANet, and CIoU loss, YOLO-Jujube was able to detect jujube fruit automatically for ripeness inspection. Having recorded params of 5.2 m, GFLOPs of 11.7, AP of 88.8%, and a speed of 245 fps for detection performance, including the sorting and counting process combined, YOLO-Jujube outperformed the network of YOLOv3-tiny, YOLOv4-tiny, YOLOv5s, and YOLOv7-tiny. YOLO-Jujube is robust and applicable to meet the goal of a computer vision-based understanding of images and videos.

The detection of fruit, including accuracy and speed is of great significance for robotic harvesting. Nevertheless, attributes such as illumination variation, occlusion, and so on have made fruit detection a challenging task. A robust YOLOFig detection model was proposed to solve detection challenges and to improve detection accuracy and speed. The YOLOFig detection model incorporated Leaky activated ResNet43 backbone with a new 2,3,4,3,2 residual block arrangement, spatial pyramid pooling network (SPPNet), feature pyramid network (FPN), complete (CIoU) loss, and distance DIoU−NMS to improve the fruit detection performance. The obtained average precision (AP) and speed (frames per second or fps) respectively under 2,3,4,3,2 residual block arranged backbone for YOLOv3b is 78.6% and 69.8 fps, YOLOv4b is 87.6% and 57.1 fps, and YOLOFig is 89.3% and 96.8 fps; under 1,2,8,8,4 residual block arranged backbone for YOLOv3 is 77.1% and 56.3 fps, YOLOv4 is 87.1% and 52.5 fps, and YOLOResNet70 is 87.3% and 79 fps; and under 3,4,6,3 residual block arranged backbone for YOLOResNet50 is 85.4% and 77.1 fps. An indication that the new residual block arranged backbone of 2,3,4,3,2 outperformed 1,2,8,8,4 on an average AP of 1.33% and detection speed of 15.2%. Finally, the compared results showed that the YOLOFig detection model performed better than other models at the same level of residual block arrangement. It can better generalize and is highly suitable for real-time harvesting robots.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.