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.

Combining Image-Based Phenotyping and Multivariate Analysis to Estimate Fruit Fresh Weight in Segregation Lines of Lowland Tomatoes

“…Here, both analyses identified whole fruit area (WFA) and width (WFW) as potential secondary traits, with crosscheck results revealing a significant direct influence of WFW to the fruit fresh weight, followed by WFA and whole fruit height (WFH). However, the regression analysis presented varying results, emphasizing the importance of further validation to increase the level of confidence in the estimations [40,[82][83][84][85].…”

“…The results were then subjected to secondary trait (i.e., phenotypic predictors) validation, encompassing the (i) analysis of selection intensity, (ii) genetic progress percentage, and (iii) effectiveness of secondary traits. The selected secondary traits were combined into a linear regression analysis model for FFW estimation [38,40,59]. Then, destructive and non-destructive data from the backcross M/K//K and F5 samples were used for validating the predicting mode.…”

“…Previous researches has focused on exploring the potential application of IBP techniques for analyzing both tomato fruits [26,[38][39][40] and canopy [41,42]. Specifically, Nyalala et al [26,38] highlighted the efficacy of IBP in non-destructively predicting tomato fruit weight.…”

Nondestructive 3D phenotyping method of passion fruit based on X-ray micro-computed tomography and deep learning

“…The surface area was calculated by adapting a previously accurate method ( Hu et al., 2020 ). The length and width of the passion fruit and the thickness of the pericarp were obtained by rectangle fitting and random sampling point distance measurement on several middle tomogram images ( Zhu et al., 2022 ). All the traits of passion fruit were divided into three categories: passion fruit traits, sarcocarp traits and pericarp traits, which were the key indicators concerned in the scientific research, planting and production of passion fruit.…”

Feasibility Study on the Classification of Persimmon Trees’ Components Based on Hyperspectral LiDAR

“…To detect and classify fruit trees’ components, researchers utilize different methods, including visible cameras, multispectral/hyperspectral cameras, LiDAR, and these methods in combination. Several methods have been developed to obtain information from trees under various natural conditions with visible light image processing, such as fruit target detection [ 1 , 2 ], segmentation of green object fruit under complex orchard backgrounds [ 3 ], segregation of tomato phenotypes [ 4 ], and fast extraction of tree canopy areas from UAV images [ 5 ].…”