Development and Evaluation of an Ultrasonic Humidifier to Control Humidity in a Cold Storage Room for Postharvest Quality Management of Dates

Impact of Modified Atmosphere Packaging Conditions on Quality of Dates: Experimental Study and Predictive Analysis Using Artificial Neural Networks

Dates are highly perishable fruits, and maintaining their quality during storage is crucial. The current study aims to investigate the impact of storage conditions on the quality of dates (Khalas and Sukary cultivars) at the Tamer stage and predict their quality attributes during storage using artificial neural networks (ANN). The studied storage conditions were the modified atmosphere packing (MAP) gases (CO2, O2, and N), packaging materials, storage temperature, and storage time, and the evaluated quality attributes were moisture content, firmness, color parameters (L*, a*, b*, and ∆E), pH, water activity, total soluble solids, and microbial contamination. The findings demonstrated that the storage conditions significantly impacted (p < 0.05) the quality of the two stored date cultivars. The use of MAP with 20% CO2 + 80% N had a high potential to decrease the rate of color transformation and microbial growth of dates stored at 4 °C for both stored date cultivars. The developed ANN models efficiently predicted the quality changes of stored dates closely aligned with observed values under the different storage conditions, as evidenced by low Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. In addition, the reliability of the developed ANN models was further affirmed by the linear regression between predicted and measured values, which closely follow the 1:1 line, with R2 values ranging from 0.766 to 0.980, the ANN models demonstrate accurate estimating of fruit quality attributes. The study's findings contribute to food quality and supply chain management through the identification of optimal storage conditions and predicting the fruit quality during storage under different atmosphere conditions, thereby minimizing food waste and enhancing food safety.

Deep Learning in Precision Agriculture: Artificially Generated VNIR Images Segmentation for Early Postharvest Decay Prediction in Apples

Food quality control is an important task in the agricultural domain at the postharvest stage for avoiding food losses. The latest achievements in image processing with deep learning (DL) and computer vision (CV) approaches provide a number of effective tools based on the image colorization and image-to-image translation for plant quality control at the postharvest stage. In this article, we propose the approach based on Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) techniques to use synthesized and segmented VNIR imaging data for early postharvest decay and fungal zone predictions as well as the quality assessment of stored apples. The Pix2PixHD model achieved higher results in terms of VNIR images translation from RGB (SSIM = 0.972). Mask R-CNN model was selected as a CNN technique for VNIR images segmentation and achieved 58.861 for postharvest decay zones, 40.968 for fungal zones and 94.800 for both the decayed and fungal zones detection and prediction in stored apples in terms of F1-score metric. In order to verify the effectiveness of this approach, a unique paired dataset containing 1305 RGB and VNIR images of apples of four varieties was obtained. It is further utilized for a GAN model selection. Additionally, we acquired 1029 VNIR images of apples for training and testing a CNN model. We conducted validation on an embedded system equipped with a graphical processing unit. Using Pix2PixHD, 100 VNIR images from RGB images were generated at a rate of 17 frames per second (FPS). Subsequently, these images were segmented using Mask R-CNN at a rate of 0.42 FPS. The achieved results are promising for enhancing the food study and control during the postharvest stage.

Combined Impact of Irrigation, Potassium Fertilizer, and Thinning Treatments on Yield, Skin Separation, and Physicochemical Properties of Date Palm Fruits

Orchard cultural practices, i.e., irrigation, fertilizer, and fruit thinning, are crucially encompassed to enhance fruit yield and quality. Appropriate irrigation and fertilizer inputs improve plant growth and fruit quality, but their overuse leads to the degradation of the ecosystem and water quality, and other biological concerns. Potassium fertilizer improves fruit sugar and flavor and accelerates fruit ripening. Bunch thinning also significantly reduces the crop burden and improves the physicochemical characteristics of the fruit. Therefore, the present study aims to appraise the combined impact of irrigation, sulfate of potash (SOP) fertilizer, and fruit bunch thinning practices on fruit yield and quality of date palm cv. Sukary under the agro-climatic condition of the Al-Qassim (Buraydah) region, Kingdom of Saudi Arabia. To achieve these objectives, four irrigation levels (80, 100, 120, and 140% of crop evapotranspiration (ETc), three SOP fertilizer doses (2.5, 5, and 7.5 kg palm^-1), and three fruit bunch thinning levels (8, 10, and 12 bunches palm^-1) were applied. The effects of these factors were determined on fruit bunch traits, physicochemical fruit characteristics, fruit texture profile, fruit color parameters, fruit skin separation disorder, fruit grading, and yield attributes. The findings of the present study showed that the lowest (80% ETc) and highest (140% ETc) irrigation water levels, lowest SOP fertilizer dose (2.5 kg palm^-1), and retaining the highest number of fruit bunch per tree (12 bunches) had a negative effect on most yield and quality attributes of date palm cv. Sukary. However, maintaining the date palm water requirement at 100 and 120% ETc, applying SOP fertilizer doses at 5 and 7.5 kg palm^-1, and retaining 8-10 fruit bunches per palm had significantly positive effects on the fruit yield and quality characteristics. Therefore, it is concluded that applying 100% ETc irrigation water combined with a 5 kg palm^-1 SOP fertilizer dose and maintaining 8-10 fruit bunches per palm is more equitable than other treatment combinations.

Design of a Smart IoT-Based Control System for Remotely Managing Cold Storage Facilities

Cold storage is deemed one of the main elements in food safety management to maintain food quality. The temperature, relative humidity (RH), and air quality in cold storage rooms (CSRs) should be carefully controlled to ensure food quality and safety during cold storage. In addition, the components of CSR are exposed to risks caused by the electric current, high temperature surrounding the compressor of the condensing unit, snow and ice accumulation on the evaporator coils, and refrigerant gas leakage. These parameters affect the stored product quality, and the real-time sending of warnings is very important for early preemptive actionability against the risks that may cause damage to the components of the cold storage rooms. The IoT-based control (IoT-BC) with multipurpose sensors in food technologies presents solutions for postharvest quality management of fruits during cold storage. Therefore, this study aimed to design and evaluate a IoT-BC system to remotely control, risk alert, and monitor the microclimate parameters, i.e., RH, temperature, CO2, C2H4, and light and some operating parameters, i.e., the temperature of the refrigeration compressor, the electrical current, and the energy consumption for a modified CSR (MCSR). In addition, the impacts of the designed IoT-BC system on date fruit quality during cold storage were investigated compared with a traditional CSR (TCSR) as a case study. The results showed that the designed IoT-BC system precisely controlled the MCSR, provided reliable data about the interior microclimate atmosphere, applied electrical current and energy consumption of the MCSR, and sent the necessary alerts in case of an emergency based on real-time data analytics. There was no significant effect of the storage time on the most important quality attributes for stored date fruit in the MCSR compared with the TCSR. As a result, the MCSR maintained high-quality attributes of date fruits during cold storage. Based on the positive impact of the designed IoT-BC system on the MCSR and stored fruit quality, this modification seems quite suitable for remotely managing cold storage facilities.

Prediction of Date Fruit Quality Attributes during Cold Storage Based on Their Electrical Properties Using Artificial Neural Networks Models

Evaluating and predicting date fruit quality during cold storage is critical for ensuring a steady supply of high-quality fruits to meet market demands. The traditional destructive methods take time in the laboratory, and the results are based on one specific parameter being tested. Modern modeling techniques, such as Machine Learning (ML) algorithms, offer unique benefits in nondestructive methods for food safety detection and predicting quality attributes. In addition, the electrical properties of agricultural products provide crucial information about the interior structures of biological tissues and their physicochemical status. Therefore, this study aimed to use an alternative approach to predict physicochemical properties, i.e., the pH, total soluble solids (TSS), water activity (a_w), and moisture content (MC) of date fruits (Tamar), during cold storage based on their electrical properties using Artificial Neural Networks (ANNs), which is the most popular ML technique. Ten date fruit cultivars were studied to collect data for the targeted parameters at different cold storage times (0, 2, 4, and 6 months) to train and test the ANNs models. The electrical properties of the date fruits were measured using a high-precision LCR (inductance, capacitance, and resistance) meter from 10 Hz to 100 kHz. The ANNs models were compared with a Multiple Linear Regression (MLR) at all testing frequencies of the electrical properties. The MLR models were less accurate than ANNs models in predicting fruit pH and had low performance and weak predictive ability for the TSS, a_w, and MC at all testing frequencies. The optimal ANNs prediction model consisted of the input layer with 14 neurons, one hidden layer with 15 neurons, and the output layer with 4 neurons, which was determined depending on the measurements of the electrical properties at a 10 kHz testing frequency. This optimal ANNs model was able to predict the pH with R² = 0.938 and RMSE = 0.121, TSS with R² = 0.954 and RMSE = 2.946, a_w with R² = 0.876 and RMSE = 0.020, and MC with R² = 0.855 and RMSE = 0.803 b by using the measured electrical properties. The developed ANNs model is a powerful tool for predicting fruit quality attributes after learning from the experimental measurement parameters. It can be suggested to efficiently predict the pH, total soluble solids, water activity, and moisture content of date fruits based on their electrical properties at 10 kHz.

The Combined Effects of Precision-Controlled Temperature and Relative Humidity on Artificial Ripening and Quality of Date Fruit

Due to climatic variation, in-situ date palm fruit ripening is significantly delayed, and some fruits (Biser) cannot become ripe naturally on the tree. Because of that issue, the vast quantity of produce is mere wasted. Few traditional methods are adopted to ripe these unripe fruits through open sun drying or solar tunnel dehydration techniques. However, these methods have minimal use due to ambient temperature and relative humidity (RH) instability. Therefore, the present study was designed to find a precise combination of temperature and RH to artificially ripe the unripe Biser fruits under controlled environment chambers. For that purpose, eighteen automated artificial ripening systems were developed. The Biser fruits (cv. Khalas) were placed immediately after harvesting in the treatment chambers of the systems with three set-point temperatures (45, 50, and 55 °C) and six set-point RH (30, 35, 40, 45, 50, and 55%) until ripening. The optimal treatment combination for artificial ripening of Biser fruits was 50 °C and 50% RH. This combination provided good fruit size, color, firmness, total soluble solids (TSS), pH, and sugars content. As a result, there was a reduction in fruit weight loss and had optimum fruit ripening time. On the other hand, low temperature and RH delayed the ripening process, deteriorated fruit quality, and caused more weight loss. Although the combination of the highest temperature and RH (55 °C and 55%) reduced ripening time, the fruits have higher weight loss and negative quality. Therefore, the artificial ripening of unripe date palm Biser fruits can be achieved using 50 °C temperature and 50% RH combination. These findings can be applied in the field using solar energy systems on a commercial scale to reduce the postharvest loss of date palm fruits.