Although the application of chemical fertilizers to crops promotes plant growth and yield, their continuous use affects soil heath and creates environmental pollution. On the other hand, plant biostimulants improve nutrients absorption, plant growth, yield and produce quality and are environment-friendly. Therefore, an experiment was conducted during 2021-22 to evaluate the effect of some biostimulants on the performance of the apple cv. Anna, planted in a sandy loam soil at Marsa Matruh governorate, Egypt. Ninety trees were randomly selected and sprayed with 4 or 6% moringa leaf extract (MLE), 0.3 or 0.4% seaweed extract (SWE), 1000 or 2000 mg L−1 Fulvic acid (FA), 4% MLE + 0.3% SWE + 1000 mg L−1 FA (combination 1), or 6% MLE + 0.4% SWE + 2000 mg L−1 FA (combination 2) before flowering, during full bloom and one month later and compared with a control (untreated trees). The results demonstrated that spraying MLE, SWE or FA or their combinations positively improved the vegetative growth, fruit set %, fruit yield and fruit physical and chemical characteristics as well as leaf nutritional status. The positive effect of MLE, SWE and FA was increased in parallel to an increase in the used concentration of each one of them. The highest increments in the measured parameters were accompanied by the application of combination 2 over the other treatments.

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.

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%.

Flame Seedless grape is considered one of the most popular and favorite grapes for consumers, since it ripens early, and has good cluster quality. Flame seedless grape marketing value depends upon its desirable appearance, berry, cluster size, and shape. Therefore, it is imperative that the cluster yield and quality are enhanced to ensure profitability. In this study, the prediction of physical characteristics of clusters and berries’ color attributes of Flame Seedless grape grown under different culture practices, in particular fertilization treatments, was carried out using nutritional status concentration (leaf mineral elements, total chlorophyll content, total carotenoids content) and multiple linear regression (MLR). The method was based on the development of two indices: the first is called index 1 (%) and was formulated by combing the mineral elements of N, P, K, Ca, and Mg concentrations; and the second is called index 2 (ppm) and was formulated by combing the elements of Fe, Cu, Mn, Zn, and B concentrations in leaf petioles. The results indicated that the established MLR models can obtain variation accuracy, based on values of coefficients of determination (R2) using the test set. The R2 values were in the range of 0.9286 to 0.9972 for cluster weight, cluster length, shoulder length, berries’ color attributes (L*, a*, b*, chroma, hue, and color index for red grapes (CIRG)). This study highlighted that during a grown season, leaf mineral elements, total chlorophyll content, and total carotenoids coupled with a MLR model can be used successfully to evaluate the physical characteristics of the cluster and berries’ color attributes of Flame seedless grape. This method is easy, fast and reliable as it retains the physical appearance of the fruits by adjusting the concentration of mineral elements, total chlorophyll content, and total carotenoids in leaves. Moreover, total chlorophyll had the greatest weight of all the predicted quality attributes.

A 2020–2021 study was performed on five-year-old guava trees to examine the influence of the foliar application of three amino acids, glycine, arginine, and glutamic acid, at a concentration of 500 or 1000 ppm. Additionally, two combinations of the three mentioned amino acids were also applied: 500 glycine + 500 arginine + 500 glutamic acid (combination 1) and 1000 glycine + 1000 arginine + 1000 glutamic acid (combination 2), and compared with a control (untreated trees). The results indicated that the application of the three amino acids, solely or in combination, was effective at increasing the shoot length, shoot diameter, and leaf chlorophyll. Additionally, the applied treatments also improved markedly the fruit set percentage, fruit yield, fruit firmness, fruit content of total soluble solids (TSS %), vitamin C (VC), and total sugars as well as the leaf mineral content (nitrogen, potassium, and phosphorus) compared with untreated trees in 2020 and 2021. Moreover, the results indicated that the combinations were more effective than individual applications and that glycine had a greater influence than arginine or glutamic acid, particularly when it was applied at 1000 ppm.