Cotton, a globally significant crop grown in over 100 countries, sustains a $40 billion market and provides employment for over 350 million people worldwide. However, plastic contamination remains a persistent challenge within the industry, degrading cotton fiber quality and disrupting ginning. Manual inspection and optical machine-vision systems struggle when plastic fragments are concealed by fibers or lack sufficient color contrast. To address these challenges, we developed an ultrasonic phased-array imaging system operating at 40 kHz under field-programmable gate array (FPGA) control. Transmitter elements emit pulsed ultrasound along radial paths, separate reflection receivers record echo amplitudes to form acoustic images, and a set of transmission receivers captures signal attenuation, which is overlaid onto the reflection-based image to highlight potential contaminants. In preliminary laboratory-based tests on both seed cotton and lint samples, the system successfully detected visually obscured plastic fragments as small as $2\mathrm{cm}\times 2\mathrm{cm}$ with an angular resolution limit of $\pm 3°$. Distinct reflection peaks and corresponding attenuation overlays were produced across the field of view, validating the system’s detection capabilities. These results demonstrate the feasibility of using ultrasonic imaging to reveal concealed plastics in cotton processing. Integrating this approach with existing optical methods could enhance contaminant-removal workflows and improve overall fiber quality and processing efficiency.

Advances in spinning technology have increased the demand for upland cotton (Gossypium hirsutum L.) cultivars with superior fiber quality. However, progress in breeding for traits such as fiber length is constrained by limited phenotypic and genetic diversity within upland cotton. Introgression from Gossypium barbadense, a closely related species known for its superior fiber traits, offers a promising strategy. Sealand 883 is an obsolete upland germplasm developed through G. barbadense introgression and is known for its long and fine fibers. Previous studies have identified a fiber length quantitative trait locus (QTL) on Chromosome 25, designated qFL-Chr.25, in Sealand 883, conferred by an allele introgressed from G. barbadense. This study evaluated the effect of qFL-Chr.25 in near-isogenic introgression lines (NIILs) using Advanced Fiber Information System (AFIS) measurements. Across four genetic backgrounds, NIILs carrying qFL-Chr.25 consistently exhibited longer fibers, as reflected in multiple length parameters, including UHML, L(n), L(w), UQL(w), and L5%. Newly developed TaqMan SNP diagnostic markers flanking the QTL enable automated, reproducible, and scalable screening of large populations typical in commercial breeding programs. These markers will facilitate the incorporation of qFL-Chr.25 into commercial breeding pipelines, accelerating fiber quality improvement and enhancing the competitiveness of cotton against synthetic fibers.

In this study, software add-ins were developed and presented to allow data processing and statistical analysis of the unique shape of cotton fiber length distribution. The approach uses VBA coding in Excel to process the data, as well as the JMP 14-17 application and add-in builder tools to fit finite mixture models to empirical fiber length distributions. The resulting model derives a parametric expression for the fiber length probability density function. The analysis add-in was applied and validated on a wide range of empirical length distributions and proved to parameterize the complex distribution patterns with an excellent goodness of fit. Both tools were compiled into installable add-ins that extended the capabilities of MS Excel for the processing of AFIS distribution reports and the statistical toolbox of JMP using the Application Builder JSL coding. Installable add-ins, along with a user manual, are available for download by cotton researchers.