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Quality assessment of fruits plays a key part in the global economy’s agricultural sector. In recent years, it has been shown that fruits are affected by different diseases, which can lead to widespread economic failure in the agricultural industry. Traditional manual visual grading of fruits could be more accurate, making it difficult for agribusinesses to assess quality efficiently. Automatic grading of fruits using computer vision has become a prominent area of study for many researchers. In this study, a deep learning-based model called FruitVision is proposed for the automatic grading of various fruits. The results showed that FruitVision performed all the existing models and obtained an accuracy of 99.42, 99.50, 99.24, 99.12, 99.38, 99.38, 99.17, 98.86, and 97.96% for the apple, banana, guava, lime, orange, pomegranate, Ajwa date, Mabroom date, and mango, respectively, using 5-fold cross-validation. This is a remarkable achievement in the field of AI-based fruit grading systems.

Radiologists interprets X-ray samples by visually inspecting them to diagnose the presence of fractures in various bones. Interpretation of radiographs is a time-consuming and intense process involving manual examination of fractures. In addition, clinician's shortage in medically under-resourced areas, unavailability of expert radiologists in busy clinical settings or fatigue caused due to demanding workloads could lead to false detection rate and poor recovery of the fractures. A comprehensive study is imparted here covering fracture diagnosis with the aim to assist investigators in developing models that automatically detects fracture in human bones. The paper is presented in five folds. Firstly, we discuss data preparation stage. Second, we present various image-processing techniques used for fracture detection. Third, we analyze conventional and deep learning based techniques for diagnosing bone fractures. Fourth, we make comparative analysis of existing techniques. Fifth, we discuss different issues and challenges faced by researches while dealing with fracture detection. [ABSTRACT FROM AUTHOR]

• The research is conducted using a self-collected and labeled surface crack dataset consisting of 3000 images. • The weights obtained from Mask RCNN model pre-trained on COCO dataset in phase I are utilized for fine-tuning in phase II. • Additionally, the hyperparameters are optimized resulting in reduced training time and improved accuracy. • The technique of freeing and unfreezing specific layers of the proposed architecture has improved the model performance. The detection of surface cracks or defects in roads, sewage pipes, pavements, shield tunnels lining, etc. has become a promising area of research creating a significant impact on various application domains. Maintenance and monitoring of the manufactured and natural structures are crucial for their correct functioning and integrity. An effective way to predict structural degradation and deterioration is by detecting crack formation, which defines bad health for any artificial or natural structure. Deep learning-based techniques are prominent in accurately identifying the cracks as opposed to the conventional machine learning techniques, which require large processing time and involves handcrafted features. A deep learning-based architecture for automatically detecting and segmenting the cracks in concrete based material surface is presented in the paper. The training and testing results are demonstrated on a newly created dataset consisting of 3000 images of surface cracks. We have manually annotated the cracks present in each image by drawing a bounding box and segmented mask around them. The testing results are evaluated on parameters such as crack detection accuracy, mean average precision rate (mAP), and crack detection speed. To overcome the data collection and labeling barriers, a part of the dataset is made publicly available for research purposes. The training speed and the convergence rate of the proposed algorithm are optimized by using the concept of transfer learning through freezing and unfreezing certain layers of the network. Additionally, the choice of appropriate hyperparameters for improving the model accuracy is discussed in the article. The performance of the model is evaluated against the ground truth labels generated. The AP is reported 74.156% and 93.445% at IoU (Intersection over union) threshold of 0.7 and 0.5 respectively for crack segmentation. [ABSTRACT FROM AUTHOR]