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Quantitative Extraction and Evaluation of Tomato Fruit Phenotypes Based on Image Recognition.

ABSTRACT: Tomato fruit phenotypes are important agronomic traits in tomato breeding as a reference index. The traditional measurement methods based on manual observation, however, limit the high-throughput data collection of tomato fruit morphologies. In this study, fruits of 10 different tomato cultivars with considerable differences in fruit color, size, and other morphological characters were selected as samples. Constant illumination condition was applied to take images of the selected tomato fruit samples. Based on image recognition, automated methods for measuring color and size indicators of tomato fruit phenotypes were proposed. A deep learning model based on Mask Region-Convolutional Neural Network (R-CNN) was trained and tested to analyze the internal structure indicators of tomato fruit. The results revealed that the combined use of these methods can extract various important fruit phenotypes of tomato, including fruit color, horizontal and vertical diameters, top and navel angles, locule number, and pericarp thickness, automatically. Considering several corrections of missing and wrong segmentation cases in practice, the average precision of the deep learning model is more than 0.95 in practice. This suggests a promising locule segmentation and counting performance. Vertical/horizontal ratio (fruit shape index) and locule area proportion were also calculated based on the data collected here. The measurement precision was comparable to manual operation, and the measurement efficiency was highly improved. The results of this study will provide a new option for more accurate and efficient tomato fruit phenotyping, which can effectively avoid artificial error and increase the support efficiency of relevant data in the future breeding work of tomato and other fruit crops.

SUBMITTER: Zhu Y

PROVIDER: S-EPMC9044966 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundDry eye syndrome is one of the most common ocular diseases, and meibomian gland dysfunction (MGD) is the leading cause of evaporative dry eye syndrome. When the tear film lipid layer becomes thin due to obstructive or hyposecretory meibomian gland dysfunction, the excessive evaporation of the aqueous layer can occur, and this causes evaporative dry eye syndrome. Thus, measuring the lipid layer thickness (LLT) is essential for accurate diagnosis and proper treatment of evaporative dry eye syndrome.MethodsWe used a white LED panel with a slit lamp microscope to obtain videos of the lipid layer interference patterns on the cornea. To quantitatively analyze the LLT from interference colors, we developed a novel algorithm that can automatically perform the following processes on an image frame: determining the radius of the iris, locating the center of the pupil, defining region of interest (ROI), tracking the ROI, compensating for the color of iris and illumination, and producing comprehensive analysis output. A group of dry eye syndrome patients with hyposecretory MGD, dry eye syndrome without MGD, hypersecretory MGD, and healthy volunteers were recruited. Their LLTs were analyzed and statistical information-mean and standard deviation, the relative frequency of LLT at each time point, and graphical LLT visualization-were produced.ResultsUsing our algorithm, we processed the lipid layer interference pattern and automatically analyzed the LLT distribution of images from patients. The LLT of hyposecretory MGD was thinner (45.2 ± 11.6 nm) than that of dry eye syndrome without MGD (69.0 ± 9.4 nm) and healthy volunteers (68.3 ± 13.7 nm) while the LLT of hypersecretory MGD was thicker (93.5 ± 12.6 nm) than that of dry eye syndrome without MGD. Patients' LLTs were statistically analyzed over time, visualized with 3D surface plots, and displayed using 3D scatter plots of image pixel data for comprehensive assessment.ConclusionsWe developed an image-based algorithm for quantitative measurement as well as statistical analysis of the LLT despite fluctuation and eye movement. This pilot study demonstrates that the quantitative LLT analysis of patients is consistent with the functions of meibomian glands clinically evaluated by an ophthalmologist. This approach is a significant step forward in developing a fully automated instrument for evaluating dry eye syndrome and for providing proper guidance of treatment.

Project description:In regions with intensive agricultural production, large amounts of organic waste are produced by livestock animals. Liquid digestate from manure-based biogas production could potentially serve as fertilizer if integrated with closed horticultural irrigation systems. The aim of this experiment was to investigate how fertilizer based on liquid biogas by-products of pig manure digestion can affect the growth and production of tomato plants. Integration of a nitrification bioreactor presumes a significantly lower concentration of nutrient solutions and a higher level of oxygenation than classical mineral cultivation. Therefore, additional controls were included. We compared plant growth and fruit quality traits of tomato plants grown in a hydroponic solution with organic fertilizer with two levels of mineral fertilizer. The tomatoes grown with organic waste-based liquid fertilizer showed reduced growth rates but increased mean fruit size, resulting in no significant change in total yield compared with high-mineral cultivation. The growth rate was similarly reduced in plants cultivated with low-mineral fertilizer. Plants cultivated with organic waste-based fertilizer had high Cl- concentration in xylem sap, leaves, and, ultimately, fruits. The leaves of plants cultivated with organic waste-based fertilizer contained higher concentrations of starch and soluble carbohydrate and low concentrations of phosphorous (P) and sulfur (S). The plants grown with organic waste-based or low-mineral medium showed significantly poorer fruit quality than the plants cultivated with the high-mineral solution. The low-mineral treatment increased xylem sap contribution to fruit weight because of higher root power. The organic waste-based fertilization did not change the root power but increased fruit size. In conclusion, organic waste-based cultivation is a possible solution for sustainable plant production in greenhouses. However, additional adjustment of nutrient supply is required to improve fruit quality.

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Quantitative Extraction and Evaluation of Tomato Fruit Phenotypes Based on Image Recognition.

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