• Korean Journal of Agricultural Science
• /
• v.50 no.4
• /
• pp.883-902
• /
• 2023

Water is critical to the health and productivity of fruit trees. Efficient monitoring of water stress is essential for optimizing irrigation practices and ensuring sustainable fruit production. Short-range sensing can be reliable, rapid, inexpensive, and used for applications based on well-developed and validated algorithms. This paper reviews the recent advancement in fruit tree water stress detection via short-range sensing, which can be used for irrigation scheduling in orchards. Thermal imagery, near-infrared, and shortwave infrared methods are widely used for crop water stress detection. This review also presents research demonstrating the efficacy of short-range sensing in detecting water stress indicators in different fruit tree species. These indicators include changes in leaf temperature, stomatal conductance, chlorophyll content, and canopy reflectance. Short-range sensing enables precision irrigation strategies by utilizing real-time data to customize water applications for individual fruit trees or specific orchard areas. This approach leads to benefits, such as water conservation, optimized resource utilization, and improved fruit quality and yield. Short-range sensing shows great promise for potentially changing water stress monitoring in fruit trees. It could become a useful tool for effective fruit tree water stress management through continued research and development.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.2
• /
• pp.421-441
• /
• 2021

Fruit detection in orchards is one of the most crucial tasks for designing the visual system of an automated harvesting robot. It is the first and foremost tool employed for tasks such as sorting, grading, harvesting, disease control, and yield estimation, etc. Efficient visual systems are crucial for designing an automated robot. However, conventional fruit detection methods always a trade-off with accuracy, real-time response, and extensibility. Therefore, an improved method is proposed based on coarse-to-fine multitask cascaded convolutional networks (MTCNN) with three aspects to enable the practical application. First, the architecture of Fruit-MTCNN was improved to increase its power to discriminate between objects and their backgrounds. Then, with a few manual labels and operations, synthetic images and labels were generated to increase the diversity and the number of image samples. Further, through the online hard example mining (OHEM) strategy during training, the detector retrained hard examples. Finally, the improved detector was tested for its performance that proved superior in predicted accuracy and retaining good performances on portability with the low time cost. Based on performance, it was concluded that the detector could be applied practically in the actual orchard environment.

• Korean Journal Plant Pathology
• /
• v.14 no.6
• /
• pp.630-635
• /
• 1998

Tobacco mosaic tobamovirus (TMV), cucumber mosaic cucumovirus (CMV), cucumber green mottle mosaic tobamovirus (CGMMV) and zucchini yellow mosaic potyvirus (ZYMV) from individual fruits and seeds of hot pepper and cucumber were detected by the reverse transcription-polymerase chain reaction (RT-PCR). The dilution end-points for RT-PCR in curde sap from TMV. and CMV - infected hot pepper leaves and CMV - and CGMMV-infected cucumber leaves were 10-5. However, the amount of PCR product obtained from preparation of ZYMV-infected cucumber leaf was 10-fold lower than those of CMV or CGMMV-infected cucumber leaves. In hot pepper, both TMV and CMV were detected in all parts of the fruit wall tissue, but the yields of PCR products in the fruit stalk and its surrounding tissues were higher than those of the end parts of the fruit. On the other hand, in cucumber fruit infected with CMV, CGMMV or ZYMV, the fruit wall tissue and seed located in both stalk and end parts showed higher yields of PCR products than those of intermediate parts. Of five viruses that were analysed, only TMV in hot pepper seed, and CGMMV and CMV in cucumber seed were detected in testa parts.

• The Plant Pathology Journal
• /
• v.38 no.4
• /
• pp.417-422
• /
• 2022

Apple stem grooving virus (ASGV) is a destructive viral pathogen of pome fruit trees that causes significant losses to fruit production worldwide. Obtaining ASGV-free propagation materials is essential to reduce economic losses, and accurate and sensitive detection methods to screen ASGV-free plantlets during in vitro propagation are urgently necessary. In this study, ASGV was sensitively and accurately quantified from in vitro propagated apple plantlets using a reverse transcription droplet digital polymerase chain reaction (RT-ddPCR) assay. The optimized RT-ddPCR assay was specific to other apple viruses, and was at least 10-times more sensitive than RT-real-time quantitative PCR assay. Furthermore, the optimized RT-ddPCR assay was validated for the detection and quantification of ASGV using micropropagated apple plantlet samples. This RT-ddPCR assay can be utilized for the accurate quantitative detection of ASGV infection in ASGV-free certification programs, and can thus contribute to the production of ASGV-free apple trees.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.19 no.2
• /
• pp.73-81
• /
• 2024

Recently, mobile manipulators have been utilized in agriculture industry for weed removal and harvest automation. This paper proposes a domain adaptive fruit detection method for harvest automation, by utilizing OWL-ViT model which is an open-vocabulary object detection model. The vision-language model can detect objects based on text prompt, and therefore, it can be extended to detect objects of undefined categories. In the development of deep learning models for real-world problems, constructing a large-scale labeled dataset is a time-consuming task and heavily relies on human effort. To reduce the labor-intensive workload, we utilized a large-scale public dataset as a source domain data and employed a domain adaptation method. Adversarial learning was conducted between a domain discriminator and feature extractor to reduce the gap between the distribution of feature vectors from the source domain and our target domain data. We collected a target domain dataset in a real-like environment and conducted experiments to demonstrate the effectiveness of the proposed method. In experiments, the domain adaptation method improved the AP50 metric from 38.88% to 78.59% for detecting objects within the range of 2m, and we achieved 81.7% of manipulation success rate.

• Bulletin of the Korean Chemical Society
• /
• v.18 no.7
• /
• pp.695-702
• /
• 1997

The objective of this work is to determine the sugar profiles in commercial fruit juices, and to obtain chemometric characteristics. Sugar compositions of fruit juices were determined by HPLC-RID and GC-FID via methoxymation and trimethylsilylation with BSTFA. The appearance of multiple peaks in GC analysis for carbohydrates was disadvantageous as described in earlier literatures. Fructose, glucose, and sucrose were major carbohydrates in most fruit juices. Glucose/fructose ratios obtained by GC were lower than those by HPLC. Orange juices are similar to pineapple juices in the sugar profiles. However, grape juices are characterized by its lower or no detectable sucrose content. In addition, it was also found that unsweeten juices contained considerable level of sucrose. Chemometric technique such as principal components analysis was applied to provide an overview of the distinguishability of fruit juices based on HPLC or GC data. Principal components plot showed that different fruit juices grouped into distinct cluster. Principal components analysis was very useful in fruit juices industry for many aspects such as pattern recognition, detection of adulterants, and quality evaluation.

• Proceedings of the Plant Resources Society of Korea Conference
• /
• 2022.09a
• /
• pp.101-101
• /
• 2022

Schisandra chinensis is a fruit which is called Omija in South Korea. The Korean word Omija means "five flavors" (sweet, spicy, sour, bitter, and salty). The aim of his study was to investigate the correlation between growth characteristics and lignans (gomisin A, gomisin N, schisandrin) contents of Schisandra chinensis. The method for determining lignans was validated by measuring the linearity, limit of detection (LOD), limit of quantification (LOQ), precision and accuracy using UPLC-UV. Growth characteristics of S. chinensis such as number of fruits per fruit bunch, length of fruit bunch, width of fruit bunch, fresh weight of fruit bunch, length of fruit, width of fruit, fresh weight of fruit, fresh weight of 30 fruits, and sugar contents of fruit were measured. From the results of correlation analysis, it was found that the contents of lignans showed a significantly negative correlation with fresh weight and sugar contents of fruit. These results will be used to study for quality control of S. chinensis fruit.

• Proceedings of the Korean Society of Plant Pathology Conference
• /
• 2003.10a
• /
• pp.135.1-135
• /
• 2003

A technique for detection of Xanthomonas axonopodis pv. citri, a causal bacterium of canker on Unshiu orange fruits, was developed using bacteriophage. Procedure for the detection was designed on the basis of the previous reports that one group(CPI) of X. axonopodis pv. citri bacteriophage and corresponding two Iysotypes distributed in Korea. First, fruit surface was washed with sterile distilled water and pellet was obtained from centrifugation. The pellet was resuspended in Wakimoto's potato semi-synthetic broth medium and divided equally into two parts. One part was heated in boiling water to kill bacterial cells. Bacteriophages(CP$_1$) were respectively added into two parts and 0.1 ml from each part was mixed with soft agar medium. After incubation for 18 hrs at 25$^{\circ}C$, the causal bacterium of canker was determined based on plaques formed on the medium. This procedure can be effectively used for detection of living bacterial pathogen on fruit surfaces of Unshiu orange.

• Smart Media Journal
• /
• v.11 no.4
• /
• pp.9-18
• /
• 2022

Recently, research and system using AI is rapidly increasing in various fields. Smart farm using artificial intelligence and information communication technology is also being studied in agriculture. In addition, data-based precision agriculture is being commercialized by convergence various advanced technology such as autonomous driving, satellites, and big data. In Korea, the number of commercialization cases of facility agriculture among smart agriculture is increasing. However, research and investment are being biased in the field of facility agriculture. The gap between research and investment in facility agriculture and open-air agriculture continues to increase. The fields of fruit trees and plant factories have low research and investment. There is a problem that the big data collection and utilization system is insufficient. In this paper, we are proposed the system for determining the fruit tree growth stage using a deep learning-based object detection model. The system was proposed as a hybrid app for use in agricultural sites. In addition, we are implemented an object detection function for the fruit tree growth stage determine.

• The Korean Journal of Applied Statistics
• /
• v.16 no.1
• /
• pp.129-140
• /
• 2003

This study was to develop a fruit sorting system using statistical image processing. Histogram was used to compare fruit colors to standard fruit color and edge detector using Wilcoxon test was used to calculate an accurate geometrical characteristics of fruit including perimeter, area, major axis and minor axis length and roundness. The experimental result obtained from using our system for sorting apples was presented.