Tomato (Solanum spp.) is the second most-consumed vegetable after potato and grown all over the world. Tomato brown rugose fruit virus (ToBRFV) was first identified in 2014 on tomato plants, since then it has been reported in many countries. It is a significant threat to tomato production. This work aimed to identify the disease resistance source(s). To achieve this aim, a total of 44 tomato materials including 28 accessions of eight wild species, two accessions of Solanum arcanum Peralta, S. pennellii Correll, and S. sitiens I.M. Johnst., seven accessions of S. chilense (Dunal) Reiche, five accessions of S. pimpinellifolium L., four accessions of S. habrochaites S. Knapp & D.M. Spooner, three accessions of S. peruvianum L., one accession of S. chmielewskii (C.M. Rick et al.) D.M. Spooner et al. and S. huaylasense Peralta, 5 cultivated tomatoes (S. lycopersicum L.) and 11 interspecific F₁ hybrids derived from S. habrochaites and S. pennellii were tested with ToBRFV isolates by using the biological testing method. Mechanical inoculation method was used for biological testing. ToBRFV was inoculated to 10 plants with 2-3 true leaves two replicates for each genotype. As a result, S. pimpinellifolium (LA1651), S. penellii (LA0716), and S. chilense (LA4117A, LA2747) were found tolerant to ToBRFV with the lowest disease severity index (DSI) with 19.6%, 28.3% and 35.0%, respectively. Also, molecular genetic analysis of the plant material by using molecular markers revealed that there was no interaction between other virus resistance genes (Tm-2 ² and Tm-1) and ToBRFV resistance. These wild tomato species identified in the present study are valuable genetic resources to develop new resistance cultivars for ToBRFV resistance in tomato breeding programs.

The tomato as both a fresh consumption and industrial product is one of the most profitable vegetables and has a large cultivation area in the world. Parallel to intense production activities, Tomato Spotted Wilt Virus (TSWV), like viral diseases, results in significant economic losses every year. Use of resistant cultivars is the most efficient and environmental-friendly method of fighting against these diseases. This study was conducted to develop new tomato genetic resources resistant to TSWV because of the Sw-5 resistance breaking (RB) isolates that were determined in tomato cultivation areas. In this study, a total of 40 tomato materials including 15 lines, 9 commercial varieties and 16 wild genotypes were by tested with molecular and biological testing methods. Mechanical inoculation method was used for biological testing and SCAR marker was used in molecular analysis. S. penellii, S. chmielewski, S. habrochaites, S. peruvianum and S. sitiens, LA0716, LA1028, LA1777, LA2744 and LA4110 genotypes were found as resistant against breaking isolates of Tomato Spotted Wilt Virus. These genotypes may be a good resistance source for the future breeding studies in tomato.

Ornamental pumpkin (Cucurbita pepo L. var. ovifera) seeds are highly morphologically variable, and their classification is hence a complex task for the seed industry. Efficient and accurate classification is critical for agricultural production, breeding programs, and seed sorting for commerce. This study employs machine learning models—Random Forest (RF), LightGBM, and k-Nearest Neighbors (KNN)—to classify ornamental pumpkin seeds based on their morphological (mass, elongation, width, thickness) and colorimetric characteristics (L*, a*, b* values from CIELAB color space). Prior to model training, the data set was preprocessed through normalization and balancing to enhance classification performance. In this study, six different types of ornamental pumpkin seeds were used, with a total of 900 (150 each of SDE0619, SDE1020, SDE1620, SDE2621, SDE4521, and SDE7721). The classification performance of the models was evaluated using different metrics, such as Accuracy, Balanced Accuracy, Precision, Recall, F1 Score, Matthews Correlation Coefficient (MCC), and Cohen’s Kappa. Among the tested models, the RF model performed best, with Accuracy of 0.959, Balanced Accuracy of 0.961, Precision (Macro) of 0.962, Recall (Macro) of 0.961, F1 Score (Macro) of 0.961, MCC of 0.951, and Cohen’s Kappa of 0.951. In contrast, the worst classification performance of the tested models was with the KNN model across all the evaluation metrics. These outcomes reflect the potential of machine learning-based approaches for seed classification automation, error minimization in seed classification, and maximization of efficiency in the seed industry. The high classification performance of the Random Forest model with 95.9% accuracy and 0.951 MCC value shows that artificial intelligence-based automatic classification of ornamental pumpkin seeds according to their morphological and colorimetric characteristics can make significant contributions to the seed industry, while the integration of this approach into seed sorting and quality determination processes can enable the creation of effective breeding schemes for optimum seed selection by maximizing the accuracy of agricultural processes.

Tomatoes are among the world’s most significant vegetables, both in terms of production and consumption. Harvesting takes place in tomato production when the important quality attribute of total soluble solids content reaches its maximum possible level. Tomato total soluble solids content (TSS) is among the most crucial attribute parameters for assessing tomato quality and for tomato commercialization. Determination of total soluble solids content by conventional measurement methods is both destructive and time-consuming. Therefore, the tomato processing industry needs a rapid identification method to measure total soluble solids content (TSS). In this study, we aimed to estimate how much soluble solids there are in beef tomato fruit by Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) methods. The models were assessed using the Coefficient of Determination (R²), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. The training data set results of the MLR model established to estimate the amount of brix in tomato fruit, calculated as MAE: 0.2349, RMSE: 0.3048, R²: 0.8441, and MAPE: 5.5368, while, according to the ANN model, MAE: 0.0250, RMSE: 0.031, R²: 0.9982 and MAPE: 0.5814. According to the metric outcomes, the ANN-based model performed better in both the training and testing parts.

The article demonstrates the Brix content of melon fruits grafted with different varieties of rootstock using Support Vector Regression (SVR) and Multiple Linear Regression (MLR) model approaches. The analysis yielded primary fruit biochemical measurements on the following rootstocks, Sphinx, Albatros, and Dinero: nitrogen, phosphorus, potassium, calcium, and magnesium. Established models were evaluated with Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²) metrics. In the test section, the results of the MLR model were calculated as MAE: 0.0728, MAPE: 0.0117, MSE: 0.0088, RMSE: 0.0936, and R²: 0.9472, while the results of the SVR model were calculated as MAE: 0.0334, MAPE: 0.0054, MSE: 0.0016, RMSE: 0.0398, and R²: 0.9904. Despite both models performing well, the SVR model showed superior accuracy, outperforming MLR by 54% to 82% in terms of predictions. The relationships between Brix levels and various nutrients, such as sucrose, glucose, and fructose, were found to be strong, while titratable acidity had a minimal effect. SVR was found to be a more reliable, non-destructive method for melon quality assessment. These findings revealed the relationship between Brix and sugar levels on melon quality. The study highlights the potential of these machine learning models in optimizing the rootstock effect and managing melon cultivation to improve fruit quality.