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Node‐by‐node boll mapping has been used to determine the effects of several environmental and management strategies, including irrigation rate and cultivar, on cotton (Gossypium hirsutum L.) boll distribution. The advent of consumer 3D digital imaging may make rapid node‐by‐node mapping of greater acreage possible in the future. Effects of irrigation rate and cultivar on boll distribution were determined from line plots of node‐specific boll distribution and boll accumulation estimate data and from vertical box and whisker plots of boll fraction using data collected by a 3D sensor system to rapidly detect open bolls before harvest over three seasons. Differences were observed between the dryland (2018 only), low irrigation, and high irrigation rates in terms of boll fraction and boll accumulation for each cultivar. The low irrigation tended to produce bolls more toward the bottom of the plant, while the high irrigation produced bolls towards the top of the plant. Yield correlation between sensor obtained and manually counted measurements was strong, with r2 values as high as 0.87. Results obtained indicated that differences among irrigation rate and cultivar were identifiable using the sensor system during the 3 yr research study. Similar systems may allow rapid, broad‐scale identification of boll distribution and yield in breeding and physiology research in the future, leading to improved identification of elite cultivars and improved management practices in cotton. [ABSTRACT FROM AUTHOR]

This paper presents a novel transfer learning regression method that utilizes data from an older instrument to train a new instrument to assess the same measurement. The method assumes that the instruments measure the same property but by different methodologies, and that samples presented to one apparatus are not available to the other. The algorithm makes use of a single feature common to both instruments to create a link with which to transfer information regarding the distribution of the resulting measurements, or labels. The goal is to generate a model in the domain of the new instrument that maps data from analyzed samples to an output measurement. This modeling process is accomplished through an iterative algorithm that supports many types of regression schemes. Results are shown using both synthetic and real world data sets, which demonstrate the effectiveness of the proposed method. Finally, we present how this technique is used to train a new instrument designed to measure cotton fiber maturity. [ABSTRACT FROM PUBLISHER]

Technologies currently used for cotton contaminant assessment suffer from some fundamental limitations. These limitations result in the misassessment of the cotton quality, and have a serious impact on its economic value. Through our research, we have shown that X-ray microtomographic image analysis may be applied with a high degree of success to noninvasive evaluation of cotton for the recognition of contaminants. We believe that this procedure, when realized in real time, will have a serious impact on the cotton cleaning process, and indeed on the economic value of cotton. [ABSTRACT FROM AUTHOR]

• Data collection in standard agronomic field conditions. • Two and three-dimensional cotton fruit segmentation in four stages of development. • Fruit counting algorithm tracks and enumerates fruits across a large plant population. • Edge device enables cost-effective and power-efficient data collection and processing. High-throughput phenotyping (HTP) has become a powerful tool for gaining insights into the genetic and environmental factors that affect cotton (Gossypium spp.) growth and yield. With the recent advances in the field of computer vision, namely the integration of deep learning algorithms, the accuracy and efficiency of HTP systems have improved dramatically, enabling them to automatically quantify such fundamental phenotypic traits as fruit identification and enumeration. However, there is currently no HTP system available for counting all the reproductive phases of cotton crop that can be deployed in agronomic field conditions throughout the growing season. This study presents CottonSense, an advanced HTP system that overcomes the challenges of deployment across multiple growth periods by effectively segmenting and enumerating cotton fruits at four stages of growth, including square, flower, closed boll, and open boll. Consequently, CottonSense enhances agronomic management through increased opportunities for data collection and analysis. Using RGB-D cameras, it captures and processes both two and three-dimensional data, facilitating a wider range of phenotypic trait extractions such as crop biomass and plant architecture. To segment the cotton fruits, a Mask-RCNN model is trained and optimized for faster inference using TensorRT. The model yields an average AP score of 79% in segmentation across the four fruit categories. Moreover, the model's accuracy in estimating total fruit count per image is validated by a strong agreement with the counts given by ten domain experts, as reflected by an R2 value of 0.94. Furthermore, to accurately count the segmented fruits over large populations of plants, an enumeration algorithm based on a tracking strategy is developed that achieves an R2 value of 0.93 when compared to hand-counted fruits in the field. The proposed HTP system, which is implemented entirely on an edge computing device, is cost-effective and power-efficient, making it an effective tool for high-yield cotton breeding and crop improvement. The code for CottonSense is publicly available at https://github.com/FeriBolour/CottonSense. [ABSTRACT FROM AUTHOR]