The application of Deep Learning models in fruit analysis has garnered significant attention due to its potential to revolutionize the agricultural sector and enhance crop monitoring. This paper presents a comprehensive review of recent research efforts in fruit analysis using Deep Learning techniques. The study delves into model selection, dataset characteristics, evaluation metrics, challenges, and future directions in this domain. Various model architectures, including classical Convolutional Neural Networks (CNNs) and advanced detection models like R-CNN and YOLO, have been explored for tasks ranging from fruit classification to detection. Evaluation metrics such as precision, recall, F1-score, and mean Average Precision (mAP) have been commonly used to assess model performance. Challenges, including data scarcity, labeling complexities, variations in fruit characteristics, and computational efficiency, have been identified and discussed. The paper also presents an overview of available datasets, encompassing both proprietary and publicly accessible sources. Future research directions involve exploring enhanced data augmentation, multi-modal integration, knowledge transfer across species, robustness in dynamic environments, improved computational efficiency, and practical integration of models into real-world agricultural systems. This review provides valuable insights for researchers and practitioners aiming to leverage Deep Learning for fruit analysis in the pursuit of sustainable agriculture and food production.

Analysis of Fruit Images with Deep Learning: A Systematic Literature Review and Future Directions

Predicting Apple Plant Diseases in Orchards Using Machine Learning and Deep Learning Algorithms

Breast cancer (BC) remains a pivotal concern in global health, and existing methods for early detection are often limited by accessibility and diagnostic accuracy. We present “Pink Guardian,” a mobile application that offers a significant advancement in the early detection of breast cancer, leveraging a proposed BC-Inception V3 architecture within TensorFlow Lite for real-time, precise classification of mammogram images. This sophisticated model has been integrated into a user-friendly interface, enabling users to easily obtain diagnostic predictions and corresponding confidence scores. Through rigorous experiments, BC-Inception-V3 emerged as the best model, showcasing superior training and test accuracies, thus signifying a break-through in clinical breast cancer screening that could be readily available on users' smartphones. Source code is available at https://github.com/kanchanmaurya95/PinkGuardian.git.

Pink Guardian: A Gateway to Early Breast Cancer Detection

Image analysis is becoming increasingly popular in many industries. Its use is perfect for, among other things, assessing the quality of products on or off the production line. Highly automated, high-performance systems can be used for this purpose. However, there are situations in which automated vision systems cannot be used on the production line due to the specific nature of the process. One such situation is testing the resistance of paint applied to glass when washing in automatic dishwashers. It is carried out outside the production line, and typical production vision systems are not used here. An attempt was made to develop a cheap and easy-to-implement research method enabling quantitative measurement of paint loss on glass when testing the coating’s resistance to automatic washing. For this purpose, analysis of images taken during the study was carried out. The developed method is based on taking a series of photos of the tested object between each stage of the wash resistance test. The obtained photographic material is then analyzed by measuring the size of paint losses expressed in the number of pixels. Then, the percentage of paint loss is calculated. This method is cheap to implement and highly accurate. Statistical analysis of the results confirmed the method’s accuracy at 98%.

Vision Inspection Method for the Quality Assessment of Paint Coatings on Glassware

Leveraging Transfer Learning and CNNs for Classification of Breast Cancer via Ultrasound Images

Improving fruit quality is important to wild blueberry producers. Plant debris like leaves and stems caused a reduction in fruit quality during mechanical harvesting. A debris detection system was developed using two Logitech C920 webcam cameras mounted before and after the blower fan on a commercial mechanical wild blueberry harvester. Images were collected from two commercially managed wild blueberry fields located in central Nova Scotia to develop a dataset of 1000 images. Two convolutional neural networks (CNNs), YOLOv3 and YOLOv3-SPP, were used and compared for performance analysis. The CNNs were trained using no augmented images, images with five augmentations (sharpening, brightness, contrast, gamma correction, and saturation), and images with five augmentations that were more heavily weighted towards gamma. Four different computer hardware packages (Hewlett-Packard, Shuttle XPC, Jetson TX2, and GPU-based Desktop) were used to determine the appropriate hardware and CNN model combination for real-time performance. YOLOv3 and YOLOv3-SPP were trained to mAP scores of 72.87% and 74.38% using the dataset with more gamma-augmented images. This was an improvement over the dataset with no augmentation (67.75%; 68.49%) and equally numbered augmentations (71.26%; 73.03%). YOLOv3-SPP combined with an Intel® Core™ i9-7900X CPU and Nvidia GeForce RTX™ 2080 Ti GPU in a desktop computer achieved the fastest detection rate of 33.30 ms. The detection speed on all other hardware configurations exceeded 833.33 ms, indicating that a powerful desktop GPU is required for real-time performance of this task. With adequate processing hardware, the developed technology could be integrated into a control system to automatically adjust brushes based on conveyor information from mechanical wild blueberry harvesters to make cleaning berries easier and enhance fruit quality. 

Machine vision system for real-time debris detection on mechanical wild blueberry harvesters

Cytological evaluation through microscopic image analysis of fine needle aspiration cytology (FNAC) is pivotal in the initial screening of breast cancer. The sensitivity of FNAC as a screening tool relies on both image quality and the pathologist’s expertise. To enhance diagnostic accuracy and alleviate the pathologist’s workload, a computer-aided diagnosis (CAD) system was developed. A comparative study was conducted, assessing twelve candidate pre-trained models. Utilizing a locally gathered FNAC image dataset, three superior models-MobileNet-V2, DenseNet-121, and Inception-V3-were selected based on their training, validation, and testing accuracies. Further, these models underwent evaluation in four transfer learning scenarios to enhance testing accuracy. While the outcomes were promising, they left room for improvement, motivating us to create a novel deep convolutional neural network (CNN). The newly proposed model exhibited robust performance with testing accuracy at 85%. Our research concludes that the most lightweight, high-accuracy model is the one we propose. We’ve integrated it into our user-friendly Android App, “Breast Cancer Detection System,” in TensorFlow Lite format, with cloud database support, showcasing its effectiveness. Implementing an artificial intelligent (AI)-based diagnosis system with a user-friendly interface holds the potential to enhance early breast cancer detection using FNAC.

A new deep learning model with interface for fine needle aspiration cytology image-based breast cancer detection

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Machine vision system for real-time debris detection on mechanical wild blueberry harvesters

A new deep learning model with interface for fine needle aspiration cytology image-based breast cancer detection