The tomato as a raw material for processing is globally important and is pivotal in dietary and agronomic research due to its nutritional, economic, and health significance. This study explored the potential of machine learning (ML) for predicting tomato quality, utilizing data from 48 cultivars and 28 locations in Hungary over 5 seasons. It focused on °Brix, lycopene content, and colour (a/b ratio) using extreme gradient boosting (XGBoost) and artificial neural network (ANN) models. The results revealed that XGBoost consistently outperformed ANN, achieving high accuracy in predicting °Brix (R² = 0.98, RMSE = 0.07) and lycopene content (R² = 0.87, RMSE = 0.61), and excelling in colour prediction (a/b ratio) with a R² of 0.93 and RMSE of 0.03. ANN lagged behind particularly in colour prediction, showing a negative R² value of −0.35. Shapley additive explanation’s (SHAP) summary plot analysis indicated that both models are effective in predicting °Brix and lycopene content in tomatoes, highlighting different aspects of the data. SHAP analysis highlighted the models’ efficiency (especially in °Brix and lycopene predictions) and underscored the significant influence of cultivar choice and environmental factors like climate and soil. These findings emphasize the importance of selecting and fine-tuning the appropriate ML model for enhancing precision agriculture, underlining XGBoost’s superiority in handling complex agronomic data for quality assessment.

Good water supply practice provides benefits such as water and energy conservation but also alters the quality of yield. These effects of irrigation on yield quantity and quality are widely researched in the case of many plants. In tomato it can affect the soluble solids content positively together with a slight reduction in yield quantity as confirmed by many studies. There are results in the literature regarding its effect on carotenoid composition and lycopene content as well, but the effect on lycopene isomers has not been revealed yet. In this study, we investigated how different water supply levels affect yield quantity and quality, focusing on lycopene components. A two-year open field irrigation experiment with a center pivot capable of variable rate irrigation was conducted on processing tomato. The water supply levels were 100%, 75%, and 50% of ETc (crop evapotranspiration) until the beginning of the ripening stage, calculated by AquaCrop compared to control without regular irrigation. The results suggested that 75% of ETc supplied till the beginning of ripening, was a balanced water supply level regarding yield quantity, soluble solids content and lycopene concentration and yields, such as higher concentration and ratio to total carotenoids. The evaluation of cis-lycopene concentration and ratio to all-trans did not show clear results in the two years.

A three-year long experiment was conducted on open-field tomato with different levels of water shortage stress. Three different water supply levels were set in 2017 and four levels for 2018 and 2019. Biomass and yield data were collected, along with leaf-temperature-based stress measurements on plants. These were used for calibration and validation of the AquaCrop model. The validation gave various results of biomass and yield simulation during the growing season. The largest errors in the prediction occurred in the middle of the growing seasons, but the simulation became more accurate at harvest in general. The prediction of final biomass and yields were good according to the model evaluation indicators. The relative root mean square error (nRMSE) was 12.1 and 13.6% for biomass and yield prediction, respectively. The modeling efficiency (EF) was 0.96 (biomass) and 0.99 (yield), and Willmott’s index of agreement (d) was 0.99 for both predicted parameters at harvest. The lowest nRMSE (4.17) was found in the simulation of final yields of 2018 (the calibration year). The best accuracy of the validation year was reached under mild stress treatment. No high correlation was found between the simulated and measured stress indicators. However, increasing and decreasing trends could be followed especially in the severely stressed treatments.

Since the beginning of resistance breeding, protection of plants against pathogens has relied on specific resistance genes encoding rapid tissue death. Our work has demonstrated in different host–pathogen relationships that plants can defend themselves against pathogens by cell growth and cell division. We first demonstrated this general defence response (GDR) in plants by identifying the gds gene in pepper. Subsequently, the existence of a genetic system for tissue defence became apparent and we set the goal to analyse it. The gdr 1 + 2 genes, which operate the complete GDR system, protect plant tissues from pathogens in a direcessive homozygous state in both host and non-host relationships. The inheritance pattern of the two genes follows a 12:3:1 cleavage of the dominant epistasis. With the knowledge of the gds and gdr 1 + 2 genes, the role of tissue-preserving (GDR) and tissue-destructive (HR) pathways in disease development and their relationship was determined. The genes encoding the general defence response have a low stimulus threshold and are not tissue-destructive and pathogen-specific. They are able to fulfil the role of the plant immune system by providing a general response to various specific stresses. This broad-spectrum general defence system is the most effective in the plant kingdom.