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Cashew is a high-value and the third largest edible tree nut that is universally consumed as a snack or included in food preparations. Classification of whole and split cashews in industries is carried out manually by visual inspection and hand-picking, which is time-consuming, tedious, challenging, and laborious. A machine vision methodology of capturing cashew shadows and associated image processing ImageJ plugin to classify cashews into whole and splits were developed and tested. The developed classification algorithm was based on a novel idea of using surface grayscale-intensity-profile for split-up cashews and object shadows for split-down and whole cashews. Out of several features derived from grayscale-intensity-profile values, the “length of curve” best classified the split-up cashews from others. The challenge of classifying the whole and split-down cashews was addressed by a new “shadow to total-area ratio.” An accuracy of 100 % was achieved by the algorithm. A process scale-up by increasing the height and power of light source was also proposed. The promising results suggest that the developed algorithm can be coupled with a suitable hardware system to perform accurate classification of the whole and split cashews. [ABSTRACT FROM AUTHOR]

Considering that grasses have been targeted as raw material for bioenergy and will have to be handled in mass flow manner to be efficient, there is need to understand how grasses can be ground into easily flowing particles. For a biorefinery, the biomass will inevitably have to be stored, more than likely in an environment exposed to the elements. Consequently, there is a need to understand how the weathered material will interact with processing equipment like a hammermill. Grinding tests with switchgrass stored for a year in the open (weathered) and undercover (unweathered) were undertaken. The switchgrass was ground in an 18.64 kW hammermill with screen openings 9.52, 31.75, and ∞ mm (no screen). Results from this preliminary evaluation show that both weathered switchgrass (WSG) and unweathered switchgrass (USG) had grinding rates similarly increasing as the screen openings size increased with the unweathered material having a slightly higher throughput. When analyzed as a volume distribution of ground particles in machine vision methodology, switchgrass condition did not affect the particle size distribution parameters of the ground material. However, the geometric mean length for WSG increased from 6 mm to 37 mm with increasing screen size for WSG, whereas the geometric mean length for USG increased but only from 6 mm to 31 mm. While future studies are needed, the results however serve as a reference for preprocess grinding of biomass, either fresh or weathered (common in large-scale storage), for bioenergy/industrial use in obtaining the desired particle size at a given production rate. [Display omitted] • Machine vision methodology produced the hammer-milled switchgrass particle size distribution (PSD) analysis. • Switchgrass conditions, weathered or unweathered, did not affect the PSD parameters of the ground material. • Geometric mean length of grinds from weathered switchgrass tended to be longer than unweathered switchgrass. • Hammer mill grinding throughput increased with screen opening sizes regardless of switchgrass condition. [ABSTRACT FROM AUTHOR]

Mechanized coal mining generates a substantial amount of fines that represents significant economic value. Coal fines had several applications, such as gasification, liquefaction, combustion, and beneficiation, but also pose health and safety hazards. Particle size and particle size distribution (PSD) literature on fine coal are highly limited. In this study, PSD of laboratory ball- and gyro-milled lignite and hard coal using machine vision analyzes was determined and compared with standard mechanical sieving. The developed machine vision ImageJ plugin utilized length- and a new width-based sieveless volume-based analysis for PSD evaluation. The plugin automated PSD analysis with high speed, and produced better insight of the fine coal (− 300 μm) PSD characteristics through textual and visual outputs. The method of analysis, coal, and mill types, produced statistically significant differences between these variables more on cumulative undersize and less on PSD parameters using pooled data. Ground fine coal PSD exhibited an unimodal normal distribution, and can be classified poorly-graded, positively skewed and leptokurtic, very well-sorted, symmetrical, and platykurtic to mesokurtic. Mechanical sieving PSD characteristics, in general, fell between machine vision width- and length-based characteristics. Unlike limited standard sieves of mechanical sieving, the machine vision methods, with limitless virtual sieves, accurately analyzed the PSD with the necessary number of sieves. The outlined machine vision methods can be readily utilized to analyze similar particulate materials. [ABSTRACT FROM AUTHOR]

[Display omitted] • Plant stand count methods using unmanned aerial vehicle imagery were reviewed. • Computer vision methods produced promising results in plant stand count analysis. • Several research gaps were identified and recommendations provided. • Results will be helpful to stakeholders like farmers, producers, and researchers. Plant stand count helps in estimating the yield and evaluating the planter's efficiency and seed quality. Traditional methods of counting the plants by manual measurement are time-consuming, laborious, and error-prone. In contrast, the ground-based sensing methods are limited to smaller spaces. High spatial resolution images obtained from unmanned aerial vehicles (UAV) can be used in conjunction with computer vision algorithms to evaluate plant stand count, as it directly influences the yield. In spite of the importance of high-throughput plant stand count in row crop agriculture, no synthesized information in this specific subject matter is available. Therefore, the objective of this paper was to perform a systematic literature review of the current studies that focus on evaluating plant stand count using UAV imagery to provide well-synthesized information, identify research gaps, and provide suitable recommendations. In this study, a comprehensive literature search was performed on three academic databases (Agricola, Web of Science, and Scopus), and a total of 29 articles were found based on search terms and selection criteria for review. From the systematic review, it can be concluded that: an appropriate stage after plant emergence without canopy overlap is necessary for image acquisition; optimal flying height should be selected to balance the field coverage and accuracy; L*a*b* color space can provide better segmentation; hyperspectral camera imagery can provide good discrimination; deep learning with data augmentation and transfer learning models can be used to reduce the computational time and resources; the stand count methodology that is successful with corn and cotton could be extended to other row crops and horticultural crops; and application of direct image processing and use of open-source platforms is required for stakeholder participation. The review will be helpful to the farmers, producers, and researchers in selecting and employing the UAV-based algorithms for evaluating plant stand count. [ABSTRACT FROM AUTHOR]

Abstract: A reliable and accurate measurement of particle size and particle size distribution (PSD) is central to characterization of particulate minerals. Using mineral celestite (SrSO4) as the test material, an inexpensive machine vision approach as an alternative to standard mechanical sieving was proposed and results were compared. The machine vision approach used a user-coded ImageJ plugin that processed the digital image in a sieveless manner and automated the PSD analysis. A new approach of employing sum of volumes (ΣVolume) as weighting factor was developed and utilized in the ASABE standard PSD analysis. The plugin also evaluated 22 significant dimensions characterizing samples and 21 PSD parameters. According to Folk and Ward''s classification, the PSD of ball-milled celestite was “very finely skewed” and “leptokurtic”. The PSD of celestite followed a lognormal distribution, and the plot against particle size exhibited almost a linear trend for both machine vision and mechanical sieving methods. The cumulative undersize PSD characteristics of both methods matched closely when the width-based mechanical sieving results were transformed to lengths by applying the shape factor (width/length). Based on the study, this machine vision approach can be utilized for PSD analysis of particulate minerals and similar products. [Copyright &y& Elsevier]

Abstract: Dust management strategies in industrial environment, especially of airborne dust, require quantification and measurement of size and size distribution of the particles. Advanced specialized instruments that measure airborne particle size and size distribution apply indirect methods that involve light scattering, acoustic spectroscopy, and laser diffraction. In this research, we propose a simple and direct method of airborne dust particle dimensional measurement and size distribution analysis using machine vision. The method involves development of a user-coded ImageJ plugin that measures particle length and width and analyzes size distribution of particles based on particle length from high resolution scan images. Test materials were airborne dust from soft pine wood sawdust pellets and ground pine tree bark pellets. Subsamples prepared by dividing the original dust using 230 mesh (63 μm) sieve were analyzed as well. A flatbed document scanner acquired the digital images of the dust particles. Proper sampling, layout of dust particles in singulated arrangement, good contrast smooth background, high resolution images, and accurate algorithm are essential for reliable analysis. A “halo effect” around grey-scale images ensured correct threshold limits. The measurement algorithm used Feret''s diameter for particle length and “pixel-march” technique for particle width. Particle size distribution was analyzed in a sieveless manner after grouping particles according to their distinct lengths, and several significant dimensions and parameters of particle size distribution were evaluated. Results of the measurement and analysis were presented in textual and graphical formats. The developed plugin was evaluated to have a dimension measurement accuracy in excess of 98.9% and a computer speed of analysis of <8 s/image. Arithmetic mean length of original wood and bark pellets airborne dust particles were 0.1138±0.0123 and 0.1181±0.0149 mm, respectively. The airborne dust particles of wood and bark pellets can be described as non-uniform, finer particles dominated, very finely skewed with positive skewness, leptokurtic, and very well sorted category. Experimental mechanical sieving and machine vision methods produced comparable particle size distribution. The limitations and merits of using the machine vision technique for the measurement of size and size distribution of fine particles such as airborne dust were discussed. [Copyright &y& Elsevier]

Abstract: This paper explores the inconsistency of “length-based separation” by mechanical sieving of particulate materials with standard sieves, which is the standard method of particle size distribution (PSD) analysis. We observed inconsistencies of length-based separation of particles using standard sieves with manual measurements, which showed deviations of 17–22 times. In addition, we have demonstrated the “falling through” effect of particles cannot be avoided irrespective of the wall thickness of the sieve. We proposed and utilized a computer vision with image processing as an alternative approach; wherein a user-coded Java ImageJ plugin was developed to evaluate PSD based on length of particles. A regular flatbed scanner acquired digital images of particulate material. The plugin determines particles lengths from Feret''s diameter and width from pixel-march method, or minor axis, or the minimum dimension of bounding rectangle utilizing the digital images after assessing the particles area and shape (convex or nonconvex). The plugin also included the determination of several significant dimensions and PSD parameters. Test samples utilized were ground biomass obtained from the first thinning and mature stand of southern pine forest residues, oak hard wood, switchgrass, elephant grass, giant miscanthus, wheat straw, as well as Basmati rice. A sieveless PSD analysis method utilized the true separation of all particles into groups based on their distinct length (419–639 particles based on samples studied), with each group truly represented by their exact length. This approach ensured length-based separation without the inconsistencies observed with mechanical sieving. Image based sieve simulation (developed separately) indicated a significant effect (P <0.05) on number of sieves used in PSD analysis, especially with non-uniform material such as ground biomass, and more than 50 equally spaced sieves were required to match the sieveless all distinct particles PSD analysis. Results substantiate that mechanical sieving, owing to handling limitations and inconsistent length-based separation of particles, is inadequate in determining the PSD of non-uniform particulate samples. The developed computer vision sieveless PSD analysis approach has the potential to replace the standard mechanical sieving. The plugin can be readily extended to model (e.g., Rosin–Rammler) the PSD of materials, and mass-based analysis, while providing several advantages such as accuracy, speed, low cost, automated analysis, and reproducible results. [Copyright &y& Elsevier]

Machine vision has been increasingly used to address agricultural issues. One such case is corn field harvest losses and image-based object detection approaches, namely image processing, machine learning, and deep learning were investigated to detect and count infield corn kernels, immediately after harvest for combine harvester performance evaluation. A hand-held low-cost RGB camera was used to collect images with kernels of different backgrounds, based on which a 420 images dataset (200, 40, and 180 for training, validation, and testing, respectively) was generated. Three different models for kernel detection were constructed based on image processing, machine learning, and deep learning. For the imaging processing method, the images were preprocessed (color thresholding, graying, and erosion), followed by Hough circle detection to identify kernels. For the machine learning (cascade detector) and deep learning (Mask R-CNN, EfficientDet, YOLOv5, and YOLOX), models were trained, validated, and tested. Experimental results showed the overall performance of the deep learning network YOLOv5 was superior to the other approaches, with a small model size (89.3 MB) and a high model average precision (78.3 %) for object detection. The detection accuracy, undetection rate and F 1 value were 90.7 %, 9.3 %, and 91.1 %, respectively, and the average detection rate was 55 fps. This study demonstrates that the YOLOv5 model has the potential to be used as a real-time, reliable, and robust method for infield corn kernel detection. [ABSTRACT FROM AUTHOR]