Appe Seetharam Nagesh, G Arulselvi, Gn Balaji
Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamilnadu, India.
Department of Information Technology, CVR College of Engineering, Hyderabad, India.
PeerJ Comput Sci. 2023 Jul 20;9:e1463. doi: 10.7717/peerj-cs.1463. eCollection 2023.
One of the key elements in maintaining the consistent marketing of tomato fruit is tomato quality. Since ripeness is the most important factor for tomato quality in the viewpoint of consumers, determining the stages of tomato ripeness is a fundamental industrial concern with regard to tomato production to obtain a high quality product. Since tomatoes are one of the most important crops in the world, automatic ripeness evaluation of tomatoes is a significant study topic as it may prove beneficial in ensuring an optimal production of high-quality product, increasing profitability. This article explores and categorises the various maturity/ripeness phases to propose an automated multi-class classification approach for tomato ripeness testing and evaluation.
Object detection is the critical component in a wide variety of computer vision problems and applications such as manufacturing, agriculture, medicine, and autonomous driving. Due to the tomato fruits' complex identification background, texture disruption, and partial occlusion, the classic deep learning object detection approach (YOLO) has a poor rate of success in detecting tomato fruits. To figure out these issues, this article proposes an improved YOLOv5 tomato detection algorithm. The proposed algorithm CAM-YOLO uses YOLOv5 for feature extraction, target identification and Convolutional Block Attention Module (CBAM). The CBAM is added to the CAM-YOLO to focus the model on improving accuracy. Finally, non-maximum suppression and distance intersection over union (DIoU) are applied to enhance the identification of overlapping objects in the image.
Several images from the dataset were chosen for testing to assess the model's performance, and the detection performance of the CAM-YOLO and standard YOLOv5 models under various conditions was compared. The experimental results affirms that CAM-YOLO algorithm is efficient in detecting the overlapped and small tomatoes with an average precision of 88.1%.
维持番茄果实持续稳定销售的关键因素之一是番茄品质。从消费者的角度来看，成熟度是影响番茄品质的最重要因素，因此确定番茄的成熟阶段是番茄生产中关乎获得高质量产品的一项基本产业关注点。由于番茄是世界上最重要的作物之一，番茄成熟度的自动评估是一个重要的研究课题，因为它可能有助于确保优质产品的最佳产量，提高盈利能力。本文探索并分类了不同的成熟阶段，以提出一种用于番茄成熟度测试和评估的自动化多类分类方法。
目标检测是众多计算机视觉问题和应用（如制造业、农业、医学和自动驾驶）中的关键组成部分。由于番茄果实的识别背景复杂、纹理干扰和部分遮挡，经典的深度学习目标检测方法（YOLO）在检测番茄果实方面成功率较低。为了解决这些问题，本文提出了一种改进的YOLOv5番茄检测算法。所提出的算法CAM-YOLO使用YOLOv5进行特征提取、目标识别，并引入了卷积块注意力模块（CBAM）。将CBAM添加到CAM-YOLO中，以使模型专注于提高准确性。最后，应用非极大值抑制和距离交并比（DIoU）来增强图像中重叠物体的识别。
从数据集中选择了几张图像进行测试，以评估模型的性能，并比较了CAM-YOLO和标准YOLOv5模型在各种条件下的检测性能。实验结果证实，CAM-YOLO算法在检测重叠和小番茄方面效率较高，平均精度为88.1%。
