Spray coating technology is an advanced manufacturing process to break the liquid down into small droplets and disperse these droplets uniformly on the target substrate. The nozzle used in this fabrication process rapidly disintegrates the liquid jet at a direction of motion governed mainly by the prevailing airflow pattern in the spray region environment. According to Navier–Stokes energy equation, the fluid and environmental temperature capable of supplying additional energy to the droplet imparted the ejected liquid jet from the nozzles. With a view to understanding the liquid droplet behaviour at elevated temperatures, this study investigates the jet velocity, pressure, and temperature of ethanol at its evaporation temperature of 360 K using commercial CFD software (ANSYS FLUENT 2020). A numerical simulation of a simple nozzle system with a cone angle of 56° and an outlet radius of 0.1966 mm was modelled at 1 bar inlet pressure to study the effect of the environment temperature on the liquid jet. The result from the temperature profile showed that the liquid jet exhibited turbulence or a swirling effect at elevated temperature as the liquid absorbed thermal energy. The liquid jet temperature increased to the maximum temperature of +3K at a liquid jet distance of 10 cm when compared with normal environment temperature. This numerical study showed that environmental conditions such as temperature can impact the liquid jet quality and properties, which are difficult to observe during experimental work. It is expected that the finding from this work would benefit the industries, leading to better spray nozzle design for future coating applications.

Variational active contour seeks to segment or extract desired object boundaries for further analysis. The model can be divided into global segmentation and selective segmentation. Selective segmentation, which focuses on segmenting a particular object, is preferable to the global model. Recently, a number of selective segmentation models have been developed to precisely extract an object on grayscale images. Nevertheless, if the input image is vector-valued (colour), these models merely convert it to a grayscale image, resulting in data loss owing to the reduction in image dimension. Furthermore, they may have poor segmentation performance due to the intensity inhomogeneous images. Therefore, a new model on variational selective active contour for segmenting vector-valued images has been proposed that incorporates the concepts of local image fitting and distance-based fitting terms into a variational minimization energy functional. Moreover, a Gaussian function was used as a regularizer to replace the computationally expensive Total Variation term. Then, the proposed model’s Euler Lagrange equation has been provided to solve the model. When segmenting an object in inhomogeneous intensity images, the result of the proposed model was about 30% more accurate based on the Jaccard value and about 3 times faster than other existing methods.

The signed pressure force (SPF) function has recently become a popular function for guiding the curve evolution of the active contour model (ACM) for image segmentation. The aim is to extract the boundaries of digital medical images for shape and image analysis. The recent SPF-based ACM demonstrates effectiveness in image segmentation. However, it may fail if the targeted object is close to a neighbouring object. Additionally, the presence of intensity inhomogeneity and noise in medical images degrades segmentation accuracy and local target areas. Thus, we proposed a new SPF-based ACM, namely the Selective Segmentation with Signed Pressure Force 1 (SSPF1) model, by incorporating the ideas of the SPF function and the distance fitting term based on geometrical constraints. Then, the new SSPF1 model was extended by incorporating an image enhancement technique to develop our second new model, termed the Selective Segmentation with Signed Pressure Force 2 (SSPF2). Numerical results indicated that the SSPF2 model was more recommended than SSPF1 as the SSPF2 model was approximately 4.7% more accurate, as indicated by the Jaccard value and was about 112 times faster in segmenting noisy images compared to the existing selective segmentation model.

A variational model was used to extract or segment the breast ultrasound (BUS) image boundary in order to find a closed curve line of the abnormality region for further diagnosis. A recent selective variational model, termed the Convex Distance Selective Segmentation (CDSS) model, is effective at segmenting a specific image object. However, the CDSS model has difficulty segmenting noisy images. Unavoidable noise in BUS pictures leads to poor segmentation, as is widely recognized. The objective of this work is to propose a reformulation of the Convex Distance Selective Segmentation (CDSS) model for the purpose of segmenting BUS pictures. Consideration of four distinct image Denoising algorithms—Gaussian filter, Median filter, Wiener filter, and Rudin-Osher-Fatemi (ROF) algorithm—as the new fitting terms in the CDSS model leads to four variants of modified CDSS models called Modified CDSS based on Gaussian filter (MCDSS_G), Modified CDSS based on Median filter (MCDSS_M), Modified CDSS based on Wiener filter (MCDSS_W) and Modified CDSS based on ROF (MCDSS_ROF). To solve the modified models, we first derived the associate Euler-Lagrange equation and solved it in Matrix Laboratory (MATLAB) software. Experiments demonstrated that the proposed MCDSS_ROF model based on the ROF denoising algorithm provided the highest average of Peak-Signal-To-Noise-Ratio (PSNR), Dice, and Jaccard Similarity Coefficients, indicating the highest denoising quality and segmentation accuracy in comparison to other models.

Fruit diseases pose a serious challenge to agricultural productivity and require effective and prompt detection strategies. Traditional techniques based on manual observation are often limited by subjectivity and delay, creating a strong need for automated solutions. Deep learning and artificial intelligence (AI) have become revolutionary technologies in detecting fruit diseases, which can be correctly classified and diagnosed at an early stage in various agricultural environments. This study conducts a systematic literature review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to summarize the recent achievements in the field. Unlike prior reviews focused on fruit grading, this work centers on pathological diagnosis. Our initial search across Scopus and Web of Science databases yielded 876 records. After applying inclusion and exclusion criteria, 31 primary studies published between January 2024 and August 2025 were selected. These papers were further analyzed to obtain three core areas, namely recent methods, ongoing challenges, and future research directions. Recent methods demonstrated strong progress with attention mechanisms, transformer hybrids, and YOLO-based detectors specifically optimized for real-time use on mobile phones and drones. However, practical application is heavily hindered by data scarcity and poor cross-domain generalization. In addition, moving these systems from the lab to the farm is often restricted by hardware limitations and a lack of explainable AI. Thus, future research must prioritize diverse, multi-site datasets and ensure that model outputs make practical sense to agronomists. This review consolidates these findings to help researchers build scalable, interpretable, and field-ready disease detection tools.

The region-based Active Contour Model (ACM) is a widely known variational segmentation model for extracting or segmenting a digital image into numerous sections for further analysis. Distinguishing between global and specific segmentation models within this paradigm is possible. The global segmentation model is incapable of selectively segmenting the region of interest (ROI) from the input image, which leads to an over-segmented problem. A variety of models have been devised to address the task of selective segmentation, which involves the extraction of the boundary of a particular region of interest (ROI) inside a digital image. The Primal Dual Selective Segmentation (PDSS) model has been recently introduced and exhibits significant potential in terms of accuracy. Nevertheless, the presence of intensity inhomogeneity in digital images disrupts the precision and localisation of target regions of segmentation. Therefore, it is important to take into account bias field adjustment, also known as correction for intensity inhomogeneity. So, this study came up with a new selective segmentation model called the Selective Segmentation with Bias Field Correction (SSBF) model by combining the idea of the existing PDSS model with the level set-based bias field correction technique. To solve the proposed SSBF model, we first derived the Euler-Lagrange (EL) equation and solved it in MATLAB software. The Intersection over Union (IOU) coefficient, also known as the Dice (DSC) and Jaccard (JSC) similarity metrics, evaluated the proposed model's accuracy. Experimental results demonstrate that the JSC and DSC values of the proposed model were 13.4% and 7.2% higher, respectively, than the competing model.