Insect pests are a major element influencing agricultural production. According to the Food and Agriculture Organization (FAO), an estimated 20–40% of pest damage occurs each year, which reduces global production and becomes a major challenge to crop production. These insect pests cause sooty mold disease by sucking the sap from the crop’s organs, especially leaves, fruits, stems, and roots. To control these pests, pesticides are frequently used because they are fast-acting and scalable. Due to environmental pollution and health awareness, less use of pesticides is recommended. One of the salient approaches could be to reduce the wide use of pesticides by spraying on demand. To perform spot spraying, the location of the pest must first be determined. Therefore, the growing population and increasing food demand emphasize the development of novel methods and systems for agricultural production to address environmental concerns and ensure efficiency and sustainability. To accurately identify these insect pests at an early stage, insect pest detection and classification have recently become in high demand. Thus, this study aims to develop an object recognition system for the detection of crops damaging insect pests and their classification. The current work proposes an automatic system in the form of a smartphone IP- camera to detect insect pests from digital images/videos to reduce farmers’ reliance on pesticides. The proposed approach is based on YOLO object detection architectures including YOLOv5 (n, s, m, l, and x), YOLOv3, YOLO-Lite, and YOLOR. For this purpose, we collected 7046 images in the wild under different illumination and background conditions to train the underlying object detection approaches. We trained and test the object recognition system with different parameters from scratch. The eight models are compared and analyzed. The experimental results show that the average precision (AP@0.5) of the eight models including YOLO-Lite, YOLOv3, YOLOR, and YOLOv5 with five different scales (n, s, m, l, and x) reach 51.7%, 97.6%, 96.80%, 83.85%, 94.61%, 97.18%, 97.04%, and 98.3% respectively. The larger the model, the higher the average accuracy of the detection validation results. We observed that the YOLOv5x model is fully functional and can correctly identify the twenty-three species of insect pests at 40.5 milliseconds (ms). The developed model YOLOv5x performs the state-of-the-art model with an average precision value of (mAP@0.5) 98.3%, (mAP@0.5:0.95) value of 79.8%, precision of 94.5% and a recall of 97.8%, and F1-score with 96% on our IP-23 dataset. The results show that the system works efficiently and was able to correctly detect and identify insect pests, which can be employed for realistic application while farming.

Human gaits are the patterns of limb movements which involve both the upper and lower body parts. These patterns in terms of step rate, gait speed, stance widening, stride, and bipedal forces are influenced by different factors including environmental (such as social, cultural, and behavioral traits) and physical changes (such as age and health status). These factors are reflected on the imprinted shoeprints generated with body forces, which in turn can be used to predict age, a problem not systematically addressed using any computational approach. We collected 100,000 shoeprints of subjects ranging from 7 to 80 years old and used the data to develop a deep learning end-to-end model ShoeNet to analyze age-related patterns and predict age. The model integrates various convolutional neural network models together using a skip mechanism to extract age-related features, especially in pressure and abrasion regions from pair-wise shoeprints. The results show that 40.23% of the subjects had prediction errors within 5-years of age and the prediction accuracy for gender/sex classification reached 86.07%. Interestingly, the age-related features mostly reside in the asymmetric differences between left and right shoeprints. The analysis also reveals interesting age-related and gender-related patterns in the pressure distributions on shoeprints; in particular, the pressure forces spread from the middle of the toe toward outside regions over age with gender-specific variations of forces on heel regions. Such statistics provide insight into new methods for forensic investigations, medical studies of gait pattern disorders, biometrics, and sport studies.

Shoeprints contain valuable information for tracing evidence in forensic scenes, and they need to be generated into cleaned, sharp, and high-fidelity images. Most of the acquired shoeprints are found with low quality and/or in distorted forms. The high-fidelity shoeprint generation is of great significance in forensic science. A wide range of deep learning models has been suggested for super-resolution, being either generalized approaches or application specific. Considering the crucial challenges in shoeprint based processing and lacking specific algorithms, we proposed a deep learning based GUV-Net model for high-fidelity shoeprint generation. GUV-Net imitates learning features from VAE, U-Net, and GAN network models with special treatment of absent ground truth shoeprints. GUV-Net encodes efficient probabilistic distributions in the latent space and decodes variants of samples together with passed key features. GUV-Net forwards the learned samples to a refinement-unit proceeded to the generation of high-fidelity output. The refinement-unit receives low-level features from the decoding module at distinct levels. Furthermore, the refinement process is made more efficient by inverse-encoded in high dimensional space through a parallel inverse encoding network. The objective functions at different levels enable the model to efficiently optimize the parameters by mapping a low quality image to a high-fidelity one by maintaining salient features which are important to forensics. Finally, the performance of the proposed model is evaluated against state-of-the-art super-resolution network models.