Fruit quality attributes are important factors for designing a market for agricultural goods and commodities. Support vector regression (SVR), MLR, and ANN models were established to predict the mass of ber fruits (Ziziphus mauritiana Lamk.) based on the axial dimensions of the fruit from manual measurements of fruit length, minor fruit diameter, and maximum fruit diameter of four ber cultivars. The precision and accuracy of the established models were assessed given their predicted values. The results revealed that using the validation dataset, the developed ANN (R² = 0.9771; root mean square error [RMSE] = 1.8479 g) and SVR (R² = 0.9947; RMSE = 1.8814 g) models produced better results when predicting ber fruit mass than those obtained by the MLR model (R² = 0.4614; RMSE = 11.3742 g). In estimating ber fruit mass, the established SVR and ANN models produced more precise prediction values than those produced by the MLR model; however, the performance differences between the SVR and ANN models were not clear.

To study the effect of potassium nitrate, calcium nitrate and kaolin (Aluminum silicate) on pomegranate cv. Wonderful, this study was conducted during 2020–2021 to investigate the possibility of minimizing the percentages of sunburn and fruit cracking and ameliorating the yield and fruit quality of pomegranate during the aforementioned period. Four sprays consisting of potassium nitrate at 1%, 2% and 3%, calcium nitrate at 2%, 3% and 4%, kaolin at 2%, 4% and 6% and water only (control) were sprayed on pomegranate trees during May, July, and August. The results showed that through spraying the fruit at set percentages, fruit yield was greatly increased through the spraying of potassium nitrate, calcium nitrate and kaolin, particularly the application of potassium nitrate at 3% and 4% and kaolin at 6% as opposed to than the other percentages. In addition, the percentages of fruit cracking and sunburn were markedly lessened by the application of calcium nitrate at 4% and 6% and also by kaolin at 6%. Moreover, the fruit content from TSS, total sugars and anthocyanin, was improved through the spraying of potassium nitrate at 2% and 3%, whereas the fruit weight and firmness were improved by the application of calcium nitrate at 4% and kaolin at 6%.

Different chemical attributes, measured via total soluble solids (TSS), acidity, vitamin C (VitC), total sugars (Tsugar), and reducing sugars (Rsugar), were determined for three groups of citrus fruits (i.e., orange, mandarin, and acid); each group contains two cultivars. Artificial neural network (ANN) and multiple linear regression (MLR) models were developed for TSS, acidity, VitC, Tsugar, and Rsugar from fresh citrus fruits by applying different independent variables, namely the dimensions of the fruits (length (FL) and diameter (FD)), fruit weight (FW), yield/tree, and soil electrical conductivity (EC). The results of ANN application showed that a feed-forward back-propagation network type with four input neurons (Yield/tree, FW, FL, and FD) and eight neurons in one hidden layer provided successful modeling efficiencies for TSS, acidity, VitC, Tsugar, and Rsugar. The effect of the EC variable was not significant. The hyperbolic tangent of both the hidden layer and the output layer of the developed ANN model was chosen as the activation function. Based on statistical criteria, the ANN developed in this study performed better than the MLR model in predicting the chemical attributes of fresh citrus fruits. The root mean square error of TSS, acidity, VitC, Tsugar, and Rsugar ranged from 0.064 to 0.453 and 0.068 to 0.634, respectively, for the ANN model, and 0.568 to 4.768 and 0.550 to 4.830, respectively, for the MLR model using training and testing datasets. In addition, the relative errors obtained through the ANN approach provided high model predictability and feasibility. In chemical attribute modeling, the FD and FL variables exhibited high contribution ratios, resulting in a reliable predictive model. The developed ANN model generally showed a good level of accuracy when estimating the chemical attributes of fresh citrus fruit.

Although chemical fertilization has gained a lot of attention due to its ability to increase the yield of fruit trees, it has been known to cause numerous environmental problems such as soil deterioration, alleviating beneficial microorganisms, and reducing fruit quality and safety. Hence, today, we aim to reduce these problems by using eco-friendly and sustainable biostimulants to promote nutritional status, yield, and quality. The effect of wood vinegar (WV) on mango production has yet to be investigated. Therefore, a field trial was conducted during the 2023 and 2024 seasons to evaluate the regulatory effect of individual and combined application of wood vinegar (WV), seaweed extract (SW), and humic acid (HA) on the performance of mango (Mangifera indica L.) cv. Ewais. The results revealed that all treatments had a pronounced effect and significantly improved the total chlorophyll content (107.7 and 106.6%), leaf N (2.02 and 2.23%), P (0.38 and 0.4), and K (1.07 and 1.13%), as well as enhancing the quality of mango fruits by increasing fruit length (11.68 and 12.38 cm), fruit width (7.8 and 8.59 cm), total sugars (40 and 37.3%), and TSS (21.9 and 20.8%) while reducing the total acidity (64.3 and 69.0%) in the 2023 and 2024 seasons, respectively, compared with the control. Based on this study, the treatment of 2 L/ha seaweed + 2 L/ha humic acid + 2 L/ha wood vinegar combined had the greatest effect on enhancing Ewais mango fruit yield by up-regulating leaf mineral acquisition, antioxidant response, and sugar accumulation. This study supports the application of HA and SW in combination with WV to improve mango fruit yield and quality.

The geometric attributes and physical, mechanical, and chemical properties were determined for 11 Indian jujube (Ziziphus mauritiana Lamk.) cultivars grown in Saudi Arabia including Zaytoni, Kashmiri, Komethry, Um-Sulaem with spines, Toffahy, Um-Sulaem without spines, Abdel-Sattar, Pu-Yun, Pu-Pineau, seedy ber, and buddling ber. The geometric mean diameter, surface area, sphericity, and shape index of the fruits ranged from 20.61 to 42.48 mm, 1339.58–5666.92 mm2, 71.39–96.80%, and 110.25–275.18%, respectively. The fruit physical properties, i e fruit weight, fruit volume, stone weight, flesh weight, true fruit density, and pulp-stone ratio varied from 4.71 to 39.02 g, 4.78 to 42.75 cm3, 0.11 to 2.35 g, 4.61 to 36.67 g,0.913 to 1.055 g/cm3, and 7.90 to 43.11, respectively. In addition, the mean color value of L*, a* and b* for the fruits ranged from 64.48 to 96.33, 2.65–4.05, and 6.32–84.36, respectively. The mechanical properties, including elastic range, bioyield force, modulus of elasticity, plastic range, and rupture force ranged from 2.31 to 8.86 mm, 21.64–325.77 N, 14.15–124.09 N/s, 0.83–3.45 mm, 56.42–364.21 N, and 107.77–480.91 N/s, respectively. The greatest rupture force belonged to the Pu-Pineau cultivar and the smallest belonged to the Um-Sulaem without spines cultivar at the horizontal orientation of both cultivars. The fruit chemical analysis values for total soluble solids, titratable acidity, total soluble solids/titratable acidity ratio, and vitamin C ranged from 13.43 to 23.55%, 0.48–1.27%, 14.24–39.85%, and 55.27–164.47 mg/100 g, respectively. The relevant data obtained for the 11 varieties of Indian jujube are valuable for developing and designing machines for processing operations, transportation, separating, packing, sorting, and harvesting. Additionally, the findings revealed considerable variation in chemical content, physical and mechanical characteristics among the tested cultivars.

This investigation aimed to develop a method to predict the total soluble solids (TSS), titratable acidity, TSS/titratable acidity, vitamin C, anthocyanin, and total carotenoids contents using surface color values (L*, Hue and chroma), single fruit weight, juice volume, and sphericity percent of fresh peach fruit. Multiple regression analysis (MLR) and an artificial neural network (ANN) were employed. An ANN model was developed with six inputs and 15 neurons in the first hidden layer for the prediction of six chemical composition parameters. The results confirmed that the ANN model R² = 974–0.998 outperformed the MLR models R² = 0.473–0.840 using testing dataset. Moreover, sensitivity analysis revealed that the juice volume was the most dominating parameter for the prediction of titratable acidity, TSS/titratable acidity and vitamin C with corresponding contribution values of 39.97%, 50.40%, and 33.08%, respectively. In addition, sphericity percent contributed by 23.70% to anthocyanin and by 24.08% to total carotenoids. Furthermore, hue on TSS prediction was the highest compared with the other parameters, with a contribution percentage of 20.86%. Chroma contributed by different values to all variables in the range of 5.29% to 19.39%. Furthermore, fruit weight contributed by different values to all variables in the range of 16.67% to 23.48%. The ANN prediction method denotes a promising methodology to estimate targeted chemical composition levels of fresh peach fruits. The information of peach quality reported in this investigation can be used as a baseline for understanding and further examining peach fruit quality.