Physico-Chemical Properties Prediction of Flame Seedless Grape Berries Using an Artificial Neural Network Model

The grape is a very well-liked fruit that is valued for its distinct flavor and several health benefits, including antioxidants, anthocyanins, soluble sugars, minerals, phenolics, flavonoids, organic acids, and vitamins, which significantly improve the product’s overall quality. Today’s supply chain as a whole needs quick and easy methods for evaluating fruit quality. Thus, the objective of this study was to estimate the quality attributes of Flame Seedless grape berries cultivated under various agronomical management and other practices using color space coordinates (berry L*, berry a*, and berry b*) as inputs in an artificial neural network (ANN) model with the best topology of (3-20-11). Satisfactory predictions based on the R² range, which was 0.9817 to 0.9983, were obtained for physical properties (i.e., berry weight, berry length, and berry diameter as well as berry adherence strength) and chemical properties (i.e., anthocyanin, total soluble solids (TSS), TSS/titratable acidity, total sugars, titratable acidity, reducing sugars, and non-reducing sugars). Meanwhile, we also performed a contribution analysis to analyze the relative importance of CIELab colorimeter parameters of berries L*, a*, and b* to determine the main fruit quality. In terms of relative contribution, berry b* contributed relatively largely to berry weight, berry adherence strength, TSS, TSS/titratable acidity, titratable acidity, total sugars, reducing sugars, and non-reducing sugars and a* contributed relatively largely to anthocyanin, berry length, and berry diameter. The developed ANN prediction model can aid growers in enhancing the quality of Flame Seedless grape berries by selecting suitable agronomical management and other practices to avoid potential quality issues that could affect consumers of them. This research demonstrated how color space coordinates and ANN model may well be utilized to evaluate the Flame seedless grape berries’ quality.

Identification of Indian jujube varieties cultivated in Saudi Arabia using an artificial neural network

This study aimed to develop a method for identifying different cultivars of Indian jujube fruits (Ziziphus mauritiana Lamk.) based on a single Indian jujube fruit color and morphological attributes using an artificial neural network (ANN) classifier. Eleven Indian jujube fruit cultivars were collected during winter of season 2020 from a local orchard located at Riyadh region, Saudi Arabia to measure their lengths, major diameters, and minor diameters. Different morphological descriptors were calculated, including the arithmetic mean diameter, the sphericity percent, and the surface area. Moreover, the color values of L*, a*, and b* of the skin of fruits were recorded. The ANN classifier was used to identify the appropriate class of Indian jujube fruit by using a combination of morphological and color descriptors. The proposed method achieved an overall identification rate of 98.39% and 97.56% in training and testing phases, respectively. In addition to color and morphological features, ANN classifier is a useful tool for identifying Indian jujube fruit cultivars and circumventing the difficulties met during fruit grading.

Application of a multilayer perceptron artificial neural network for identification of peach cultivars based on physical characteristics

In the fresh fruit industry, identification of fruit cultivars and fruit quality is of vital importance. In the current study, nine peach cultivars (Dixon, Early Grande, Flordaprince, Flordastar, Flordaglo, Florda 834, TropicSnow, Desertred, and Swelling) were evaluated for differences in skin color, firmness, and size. Additionally, a multilayer perceptron (MLP) artificial neural network was applied for identification of the cultivars according to these attributes. The MLP was trained with an input layer including six input nodes, a single hidden layer with six hidden nodes, and an output layer with nine output nodes. A hyperbolic tangent activation function was used in the hidden layer and the cross entropy error was given because the softmax activation function was functional to the output layer. Results showed that the cross entropy error was 0.165. The peach identification process was significantly affected by the following variables in order of contribution (normalized importance): polar diameter (100%), L∗ (89.0), b∗ (88.0%), a∗ (78.5%), firmness (71.3%), and cross diameter (37.5.3%). The MLP was found to be a viable method of peach cultivar identification and classification because few identifying attributes were required and an overall classification accuracy of 100% was achieved in the testing phase. Measurements and quantitative discrimination of peach properties are provided in this research; these data may help enhance the processing efficiency and quality of processed peaches.

Impact of organic manure on fruit set, fruit retention, yield, and nutritional status in pomegranate (Punica granatum L. "Wonderful") under water and mineral fertilization deficits

This research was conducted on mature pomegranate (Punica granatum L. "Wonderful") trees growing at a site located in North Coast, Matrouh Governorate, Egypt. The aim was to investigate the impacts of different irrigation regimes in combination with different fertilizer regimes on the fruit set, fruit retention, yield, and nutritional status of the trees. The experimental factors were arranged in a split-plot design, with four replicates per treatment combination. The results indicated that all of the characteristics measured, including leaves nutritional status, percentages of fruit set, fruit drop, fruit retention, fruit cracking, fruit sunburn, and marketable fruit, and yield were significantly affected by the interaction between the irrigation treatment which denoted by percentages of reference evapotranspiration (ETo) and fertilizer regime. The application of 75% mineral fertilizer + 25% organic manure under deficit irrigation of 80% ETo increased the yield by an average of 18.23% over the 2 years compared with 100% mineral fertilization under full irrigation, while 50% mineral fertilizer + 50% organic matter under 80% ETo gave the maximum percentage of marketable fruit (86.23% and 86.84% in 2018 and 2019, respectively). The maximum water use efficiency was obtained with the 80% ETo treatment combined with 75% mineral fertilizer + 25% organic manure in both seasons with values of 9.69 and 10.06 kg/m³ applied water, respectively. These results demonstrate that under the field conditions at the experimental site, the fruit set and retention could be improved by applying a reduced amount of mineral fertilizer in combination with organic manure and less irrigation water.

Application of artificial neural network and support vector regression in predicting mass of ber fruits (Ziziphus mauritiana Lamk.) based on fruit axial dimensions

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.