Parameters of finger stalk lifter cut branches in intensive gardens

In Uzbekistan, the areas of intensive orchards are expanding, in which branches are regularly pruned according to agrotechnical rules. In intensive orchards, the number of fruit trees is usually greater, so cut branches occupy most of the row-spacing area. They interfere with many operations, so they must be removed. Usually, this work is done manually. True, some rakes collect cut branches and take them out of the field. The parameters of existing rakes do not always meet the requirements, so the authors proposed a branch pick-up that works satisfactorily. This article provides a rationale for its kinematic parameters. This branch pick-up also allows you to pick up short-cut branches in intensive orchards

Automatic Assessment of Seed Germination Percentage

This research was designed to investigate an automatic seed germination rate for the top of paper germination method. Chili and guinea were adopted to be used in the experiment with a 4-time repetition and 2 sets of the germination group (4-separated plates with 50 seeds per plate, 2 sets per seed type, totally 400 seeds of chili and 400 seeds of quinea). Two detection methods were proposed binary thresholding and maximum likelihood; based on color analysis. An uncontrolled environment image taking was the way to collect image data. The results were compared to a hand-labeling groundtruth. Both methods achieved accuracy rate higher than 93% which was promising to implement this system. The binary thresholding was a lightweight method suitable for a very limited resource software environment system. The maximum likelihood was more complex. The method had more potential than the binary thresholding, it was flexible to the light condition, returned few false alarms per image (less than 3 false alarms per image). Maximum likelihood could be adopted to implement in a proper environment which still could be in a mobile device

Exploring ResNet-18 Estimation Design through Multiple Implementation Iterations and Techniques in Legacy Databases

In a rapidly evolving landscape where automated systems and database applications are increasingly crucial, there is a pressing need for precise and efficient object recognition methods. This study contributes to this burgeoning field by examining the ResNet-18 architecture, a proven deep learning model, in the context of fruit image classification. The research employs an elaborate experimental setup featuring a diverse fruit dataset that includes Rambutan, Mango, Santol, Mangosteen, and Guava. The efficacy of single versus multiple ResNet-18 models is compared, shedding light on their relative classification accuracy. A unique aspect of this study is the establishment of a 90% decision threshold, introduced to mitigate the risk of incorrect classification. Our statistical analysis reveals a significant performance advantage of multiple ResNet-18 models over single models, with an average improvement margin of 15%. This finding substantiates the study’s central hypothesis. The implemented 90% decision threshold is determined to play a pivotal role in augmenting the system’s overall accuracy by minimizing false positives. However, it’s worth noting that the increased computational complexity associated with deploying multiple models necessitates further scrutiny. In sum, this study provides a nuanced evaluation of single and multiple ResNet-18 models in the realm of fruit image classification, emphasizing their utility in practical, real-world applications. The research opens avenues for future exploration by refining these methodologies and investigating their applicability to broader object recognition tasks

Automated Quality Control in Manufacturing Production Lines: A Robust Technique to Perform Product Quality Inspection

Quality control (QC) in manufacturing processes is critical to ensuring consumers receive products with proper functionality and reliability. Faulty products can lead to additional costs for the manufacturer and damage trust in a brand. A growing trend in QC is the use of machine vision (MV) systems because of their noncontact inspection, high repeatability, and efficiency. This thesis presents a robust MV system developed to perform comparative dimensional inspection on diversely shaped samples. Perimeter, area, rectangularity, and circularity are determined in the dimensional inspection algorithm for a base item and test items. A score determined with the four obtained parameter values provides the likeness between the base item and a test item. Additionally, a surface defect inspection is offered capable of identifying scratches, dents, and markings. The dimensional and surface inspections are used in a QC industrial case study. The case study examines the existing QC system for an electric motor manufacturer and proposes the developed QC system to increase product inspection count and efficiency while maintaining accuracy and reliability. Finally, the QC system is integrated in a simulated product inspection line consisting of a robotic arm and conveyor belts. The simulated product inspection line could identify the correct defect in all tested items and demonstrated the system’s automation capabilities

Hyperspectral imaging for detection of corrosion on intermediate level nuclear waste containers

Intermediate level nuclear waste (ILW) will be stored above ground in 304L stainless steel (SS) containers for the next 100 years. During this period, the containers need to be monitored for atmospheric pitting corrosion - a known precursor of atmospherically induced stress corrosion cracking. Hyperspectral (HS) and optical imaging of pitting corrosion products from droplet experiments have been investigated towards developing a system for long term monitoring of atmospheric pitting corrosion of stainless steel containers in ILW stores. Common corrosion products were first identified via Raman spectroscopic mapping as akaganeite (β-FeOOH) and lepidocrocite (γ-FeOOH), with a secondary presence of layered double hydroxide (green rust). HS and optical methods were then compared for their efficacy at rust detection. Whilst it was not possible to identify specific corrosion species using HS imaging, HS images of rust under pitted droplets provided better contrast with the background steel than colour photography due to species having lower absorbance the near infrared (850 nm) than red (650 nm). Finally, the relationship between rust area and pit volume was determined by comparing colour photography (rust area) with confocal laser scanning microscopy (pit volume). A good correlation was present for samples exposed to a fixed relative humidity (RH) for MgCl2 droplets and CaCl2 droplets with small pit volumes. Poor correlation was found for samples exposed to natural fluctuations in RH. It was concluded that optical methods are viable for the detection of rust, but less effective for quantification of pit volumes