ABSTRACT. In recent years, several efforts have been made to develop tomato cultivars displaying both late blight resistance and good organoleptic fruit quality. Selection indexes are considered the best option to perform genotype selection when many different traits are being considered to select genotypes as close to the desired ideotype as possible. Therefore, this study aimed at selecting late blight-resistant tomato families based on their fruit quality attributes using factor analysis and ideotype-design / best linear unbiased predictor (FAI-BLUP) index. For this purpose, we assessed the fruit quality parameters of 81 F3:5 tomato families previously selected as late blight resistant. The tomato cultivars Thaise, Argos, and Liberty were included in the trial as checks. The experimental arrangement consisted of complete randomized blocks with three replicates. Each plot was formed by five plants, three of which were used in the fruit quality assessment. The quality parameters assessed were fruit diameter, fruit length, fruit color (L, a*, C, and H), fruit firmness, titratable acidity, soluble solids content, hydrogen potential, and SS:TA ratio. Fruit quality data were analyzed using the mixed model methodology via REML/BLUP (restricted residual maximum likelihood / best linear unbiased prediction) to obtain BLUPs that were further subjected to the FAI-BLUP selection index. The FAI-BLUP was efficient in selecting late blight-resistant tomato genotypes based on their fruit quality attributes. Fourteen tomato families were classified as closest to the desirable ideotype for fruit quality. These genotypes should move on to the following stages of the tomato breeding program. ABSTRACT years possible Therefore blightresistant resistant ideotypedesign design FAIBLUP FAI BLUP (FAI-BLUP index purpose 8 F35 F F3 5 F3: Thaise Argos checks replicates plants assessment diameter length L, L (L a a* C H, H , H) firmness acidity content potential SSTA SS TA ratio REMLBLUP REML restricted prediction program

ABSTRACT Traditional germination tests which assess seed quality are costly and time-consuming, mainly when performed on a large scale. In this study, we assessed the efficiency of X-ray imaging analyses in predicting the physiological quality of tomato seeds. A convolutional neural network (CNN) called mask region convolutional neural network (MaskRCNN) was also tested for its precision in adequately classifying tomato seeds into four seed quality categories. For this purpose, X-ray images were taken of seeds of 49 tomato genotypes (46 Solanum pennellii introgression lines) from two different growing seasons. Four replicates of 25 seeds for each genotype were analyzed. These seeds were further assessed for germination and seedling vigor-related traits in two independent trials. Correlation analysis revealed significant linear association between germination and image-based variables. Most genotypes differed in terms of germination and seed development performance considering the two independent trials, except LA 4046, LA 4043, and LA4047, which showed similar behavior. Our findings point out that seeds with low opacity and percentage of damaged seed tissue and high values for living tissue opacity have greater physiological quality. In short, our work confirms the reliability of X-ray imaging and deep learning methodologies in predicting the physiological quality of tomato seeds. timeconsuming, timeconsuming time consuming, consuming time-consuming scale study Xray X ray CNN (CNN MaskRCNN (MaskRCNN categories purpose 4 46 (4 lines seasons 2 analyzed vigorrelated vigor related trials imagebased image based variables 4046 4043 LA4047 behavior short ( 404 LA404 40 LA40 LA4

ABSTRACT. To be considered drought-tolerant, a tomato cultivar is required to present some level of tolerance at all developmental stages of plant growth. Since drought tolerance is a stage-specific phenomenon, genotype assessment must be performed separately at all developmental stages. In this study, we used a multi-trait index based on factor analysis and genotype-ideotype distance (FAI-BLUP index) to properly rank 49 tomato genotypes according to their tolerance to drought stress conditions at germination and seedling stages. Seeds of 47 introgression lines, which cultivar M82 is considered drought-sensitive, and the Solanum pennellii wild accession LA 716, which is considered drought-tolerant, were subjected to a control condition, where seeds were soaked in distilled water, and a drought condition, where seeds were soaked in a polyethylene glycol (PEG) solution (-0.3 MPa). Drought stress, induced by PEG, had a significant impact on all nine germination and growth performance-related traits; there was a reduction in shoot length (SL), total length (TL), initial germination percentage (IGP), final germination percentage (FGP), germination velocity index (GVI), and germination rate (GR). In contrast, the root-to-shoot ratio (R/S) and time to reach 50% germination (T50) increased under drought stress. Root length (RL) was less affected by drought, and in some genotypes, it was even increased. As expected, LA 716 ranked closest to the drought tolerance ideotype. IL 1-4-18, IL 2-3, IL 1-2, IL 9-2, and IL 10-1 were the most drought-tolerant at the germination stage. These results will serve as guidance for breeders who are aiming at developing drought-resistant tomato cultivars.