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.

The increasing popularity of pomegranate (Punica granatum L.), driven by the awareness of its nutraceutical properties and excellent environmental adaptability, is promoting a global expansion of its production area. This investigation reports the variability in the weight, moisture, pH, total soluble solids, carbohydrates, organic acids, phenolic compounds, fatty acids, antioxidant activities, and element composition of different fruit parts (juices, peels, and kernels) from four (Ako, Emek, Kamel, and Wonderful One) of the most widely cultivated Israeli pomegranate varieties in Salento (South Italy). To the best of our knowledge, this is the first systematic characterization of different fruit parts from pomegranate cultivars grown simultaneously in the same orchard and subjected to identical agronomic and environmental conditions. Significant genotype-dependent variability was observed for many of the investigated parameters, though without any correlation among fruit parts. The levels of phenols, flavonoids, anthocyanins, and ascorbic and dehydroascorbic acids of all samples were higher than the literature-reported data, as was the antioxidant activity. This is likely due to positive interactions among genotypes, the environment, and good agricultural practices. This study also confirms that pomegranate kernels and peels are, respectively, rich sources of punicic acid and phenols together, with several other bioactive molecules. However, the variability in their levels emphasizes the need for further research to better exploit their agro-industrial potential and thereby increase juice-production chain sustainability. This study will help to assist breeders and growers to respond to consumer and industrial preferences and encourage the development of biorefinery strategies for the utilization of pomegranate by-products as nutraceuticals or value-added ingredients for custom-tailored supplemented foods.

With the global population projected to reach 8.6 billion by 2050 and urbanization on the rise, sustainable food production in cities becomes imperative. Vertical farming presents a promising solution to meet this challenge by utilizing space-efficient, controlled-environment agriculture techniques. In a vertical farming system, high quality, high nutritional value products can be produced with minimum water consumption, using LEDs as energy-efficient light sources. Microgreens are a new market category of vegetables among sprouts and baby leaf greens. The most critical challenge in their cultivation is the choice of growing medium, lighting, and light spectrum, which affect photosynthesis, plant growth, and yield. This review explores various cultivation methods, including hydroponics, within the context of vertical farming. Using current research, it investigates the effect of LED lighting on the physiological properties and growth of microgreens and baby leaf lettuce, but further research is needed to determine the response of the varieties and the optimal light spectrum ratios to meet their needs.

Drought-tolerant plant growth-promoting rhizobacteria (PGPR) may promote plant development under limited water supply conditions, when plant’s water demand is not completely satisfied under rain-fed conditions or when irrigation water availability is limited. The aim of this study was to examine the effects of two inoculation treatments (B2: Alcaligenes sp. 3573, Bacillus sp. BAR16, and Bacillus sp. PAR11 strains and B3: Pseudomonas sp. MUS04, Rhodococcus sp. BAR03, and Variovorax sp. BAR04 strains) and compare those to a control (B0) without artificial inoculation on chlorophyll fluorescence, leaf chlorophyll content (SPAD value), canopy temperature, and the yield of the processing tomato cultivar H-1015 F1 grown under field conditions. The young seedlings of the hybrid tomato variety H-1015 F1 were immersed in 1% of B2 or B3 products (BAY-BIO, Szeged Hungary) for 5 min. Inoculated and untreated seedlings were grown under three irrigation treatments [regular irrigation (RI), deficit irrigation (DI), and no irrigation (I0)], to reveal the effect of PGPR under different levels of water stress. In the dry year (2018), higher canopy temperature and chlorophyll fluorescence (Fv/Fm) were measured during flowering in plants treated with bacteria than in untreated plants. In the stage of flowering and fruit setting, the B3 treatment led to a significant decrease in the Fv/Fm value, canopy temperature remained high, and the SPAD value was statistically the same in all treatments. Under limited water supply, in most cases, PGPR led to a significantly greater total yield but more unripe green berries compared to untreated plants. Under moderate water shortage (dry year + deficit irrigation), the B3 treatment resulted in 26% more ripe, marketable fruit and 49% less unripe fruit compared to the B2 treatment. On the other hand, in the wet year (2020), the bacterial treatments generally did not affect physiological properties, though the B2 treatment produced a higher marketable yield while the amount of green and diseased fruits did not differ statistically, compared to the B3 treatment under deficit irrigation. Based on our study, we recommend the application of the B3 PGPR product as it positively affected key physiological processes, leading to a higher marketable yield particularly under water shortage.

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.

In light of the great technological progress in non-destructive quality detection methods, sweetness is no longer the essential parameter in evaluating watermelon quality. There is an aspiration to determine physicochemical quality characteristics to enable us to select the best cultivars, agricultural practices, and harvest dates. In the present work, three different watermelon cultivars (Lady, Galander, and Style) were harvested at three consecutive harvest times. Two pieces were taken from each watermelon sample, one from the middle (part A) and the other from the ends (part B), to track the intensity of quality parameters inside the watermelon. Parts A and B were subjected to Vis/NIR spectroradiometer (475:1075 nm), near-infrared spectroscopy (NIRS) (950:1650 nm), and high-performance liquid chromatography to assess the physicochemical quality. Calibration and prediction models were conducted using partial least squares regressions (PLS). The results indicated that the harvesting time significantly influenced the color and chemical parameters. Quality parameters concentrations markedly degraded towards late harvest. The highest concentrations of quality parameters were sighted for the middle zone (part A), especially in the Galander cultivar. Spectroradiometer achieved the best coefficient of prediction (R²_P) ≃ 0.88 and 0.81 attached with the lowest value of the standard error of prediction (SEP) ≃ 0.03 and 1.06 for chroma (C*) and yellowness index (YI). However, the findings showed the superiority of the NIRS compared to the Vis-NIR method. The highest R²_P was achieved by values 0.92, 0.91, 0.90, 0.89, 0.85, and 0.85 for lycopene, total carotenoids, vitamin C, β-carotene, γ-content, and TSS, respectively. It could be concluded that the NIRS has the ability to monitor the maturity development and determine the harvest dates practically and reliably.