This work was aimed at evaluating the properties of pectin from unripe cavendish banana peel using different acidic extractions. Hydrochloric (HCl), citric, and malic acid solutions at various pH values (1.5, 2.0, and 2.5) were used in this study. The properties of a raspberry jam added with the obtained pectins were also investigated. The extraction yield, galacturonic acid content, degrees of esterification (DE) and methylation (DM) of the samples were quantified and compared. The highest pectin yield was obtained using extraction with citric at pH 2.0. The citric extraction also gave the highest percentages of DE (50.27 %) and DM (59.57 %) at pH 1.5. Extraction with HCl gave higher galacturonic acid content to the extracted pectin. Additionally, the use of HCl at pH 1.5 also provided the highest gel hardness (30.26 g). For food application, most of the pectins significantly decreased raspberry jam hardness along with decreasing lightness and redness when compared with the control.

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A real-time kinematic global navigation satellite system (RTK-GNSS) mounted on a drone was used for the generation of a relatively accurate orthophoto and a digital elevation model (DEM) with a minimum number of ground control points (GCPs) in a large-scale onion field. Subsequently, deep learning was applied to detect onion bulbs in the orthophotos. Consequently, 16,812 onion bulbs were annotated using 250 images for object detection. After the machine learning process, the detection rate was 0.60, whereas the recall was 0.16. When the trained model was applied to the orthophotos, the number of onion bulbs ranged from 29,970 to 109,694, which was 5–19 % of the estimated number of onion plants. When the size of the annotation bounding box was expanded to include the surrounding image of the onion bulb, the detection rate was improved to 41–47 %. In the future, our objective will be to improve the trained model by reducing the false-negative value in the confusion matrix.

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This study aimed to develop dumpling skin with high barley content. To 100 g of barley flour, 5 and 10 % gluten, 1.05 and 1.10 % salt, 50, 60, 65, 70 and 75 % water were added. The mixture was kneaded, cut to form raw dumpling skin, and then cooked. The wheat dumpling skin was also prepared. No significant difference in firmness was observed between cooked wheat dumpling skin and barley dumpling skin with added 10 % gluten and 70 % moisture. Compared to cooked wheat dumpling skin, the cooked barley dumpling skin had a higher β-glucan content and total polyphenol content, and there was no difference in sensory evaluation except for appearance. The proposed method provides barley dumpling skin with high palatability and functionality.

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Smart agricultural machinery is indispensable for modern postharvest. This study introduces an artificial intelligence system to detect and evaluate the root trimming condition of garlics based on garlic images using convolutional neural network algorithms. We aimed to develop a real-time and autonomous classification system of garlic during the root trimming process. The classification considered as three classes namely, good, bad and scratch classes. The system automatically operated when a garlic was placed under the webcam. The analysis results were sent to two replays via serial ports for further automation processes. The classification was instant, and its accuracy was about 96 %. This system has the potential for high-impact applications in agricultural imaging, especially in postharvest machinery.

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