Diseases in apple orchards (rot, scab, and blotch) worldwide cause a substantial loss in the agricultural industry. Traditional hand picking methods are subjective to human efforts. Conventional machine learning methods for apple disease classification depend on hand-crafted features that are not robust and are complex. Advanced artificial methods such as Convolutional Neural Networks (CNN’s) have become a promising way for achieving higher accuracy although they need a high volume of samples. This work investigates different Deep CNN (DCNN) applications to apple disease classification using deep generative images to obtain higher accuracy. In order to achieve this, our work progressively modifies a baseline model by using an end-to-end trained DCNN model that has fewer parameters, better recognition accuracy than existing models (i.e., ResNet, SqeezeNet, and MiniVGGNet). We have performed a comparative study with state-of-the-art CNN as well as conventional methods proposed in the literature, and comparative results confirm the superiority of our proposed model.

Due to rising consumer demand and traffic congestion, last-mile logistics is becoming more challenging. To optimize urban distribution networks, digital image processing plays a key role in addressing these challenges through efficient traffic monitoring systems, an essential component of intelligent transportation systems. This paper introduces the Hyper-heuristic Genetic Algorithm based on Thompson Sampling with Diversity (HHGATSD), a novel approach to efficiently solving complex optimization and versatility problems in image segmentation. We evaluate its efficiency and robustness using the IEEE CEC2017 benchmark function set in general optimization problems with 30 and 50 dimensions. HHGATSD’s applicability extends beyond optimization to computer vision in traffic management. First, the multilevel thresholding segmentation is performed on images extracted from the Berkeley Segmentation Dataset with minimum cross-entropy as the objective function, and its performance is compared using PSNR, SSIM, and FSIM metrics. Following that, the proposed methodology addresses the task of vehicle segmentation in traffic camera videos, reaffirming HHGATSD’s effectiveness, adaptability, and consistency by consistently outperforming alternative segmentation methods found in the state-of-the-art. The results of comprehensive experiments, validated by statistical and non-parametric analyses, show that the proposed hyper-heuristic and methodology produce accurate and consistent segmentations for road traffic surveillance compared to the other methods in the literature.

Breast cancer represents the most common type of cancer worldwide among women. One of the most important diagnostic methods of this disease are mammograms, however, the high prevalence of breast cancer has not been reduced due to the incorrect diagnosis of these images, since they can be complex to interpret. An approach that represents a fundamental process for the improvement of this diagnosis is digital image processing, since it can facilitate the interpretation of the images for the specialists. In this work is proposed the implementation of a new multilevel segmentation approach based on the minimum cross-entropy threshold - Harris Hawks Optimization (MCET-HHO) metaheuristic algorithm, identifying regions within the breast that have abnormal tissue. Then, these regions are subjected to an automatic classification system based on a bag-of-visual-words (BoVW) approach to identify healthy tissue, benign tumors, and malignant tumors. According to the results, the classifier reached an average accuracy of 0.86 in the training stage and 0.73 in the testing, proving to be statistically significant in the automatic classification of mammograms, presenting a preliminary tool for the support of specialists in the diagnosis of mammography images.