Precision agriculture employs cutting-edge technologies to increase agricultural productivity while reducing adverse impacts on the environment. Precision agriculture is a farming approach that uses advanced technology and data analysis to maximize crop yields, cut waste, and increase productivity. It is a potential strategy for tackling some of the major issues confronting contemporary agriculture, such as feeding a growing world population while reducing environmental effects. This review article examines some of the latest recent advances in precision agriculture, including the Internet of Things (IoT) and how to make use of big data. This review article aims to provide an overview of the recent innovations, challenges, and future prospects of precision agriculture and smart farming. It presents an analysis of the current state of precision agriculture, including the most recent innovations in technology, such as drones, sensors, and machine learning. The article also discusses some of the main challenges faced by precision agriculture, including data management, technology adoption, and cost-effectiveness.

A digital twin is a digital representation that closely resembles or replicates a real world object by combining interdisciplinary knowledge and advanced technologies. Digital twins have been applied to various fields, including to the agricultural field. Given big data and systematic data management, digital twins can be used for predicting future outcomes. In this study, we endeavor to create an agricultural digital twin using mandarins as a model crop. We employ an Open API to aggregate data from various sources across Jeju Island, covering an area of approximately 185,000 hectares. The collected data are visualized and analyzed at regional, inter-orchard, and intra-orchard scales. We observe that the intra-orchard analysis explains the variation of fruit quality substantially more than the inter-orchard analysis. Our data visualization and analysis, incorporating statistical models and machine learning algorithms, demonstrate the potential use of agricultural digital twins in the future, particularly in the context of micro-precision and individualized agriculture. This concept extends the current management practices based on data-driven decisions, and it offers a glimpse into the future of individualized agriculture by enabling customized treatment for plants, akin to personalized medicine for humans.

An assay for detecting Apple scar skin viroid (ASSVd) was developed based on nucleic acid sequence based amplification (NASBA) in combination with real-time detection during the amplification process using molecular beacon. The ASSVd specific primers for amplification of the viroid RNA and molecular beacon for detecting the viroid were designed based on highly conserved regions of several ASSVd sequences including Korean isolate. The assay had a detection range of 1 × 104 to 1 × 1012 ASSVd RNA copies/μl with reproducibility and precision. Following the construction of standard curves based on time to positive (TTP) value for the serial dilutions ranging from 1 × 107 to 1 × 1012 copies of the recombinant plasmid, a standard regression line was constructed by plotting the TTP values versus the logarithm of the starting ASSVd RNA copy number of 10-fold dilutions each. Compared to the established RT-PCR methods, our method was more sensitive for detecting ASSVd. The real-time quantitative NASBA method will be fast, sensitive, and reliable for routine diagnosis and selection of viroid-free stock materials. Furthermore, real-time quantitative NASBA may be especially useful for detecting low levels in apple trees with early viroid-infection stage and for monitoring the influence on tree growth.

Background: Managing and investigating all available genetic resources are challenging. As an alternative, breeders and researchers use core collection—a representative subset of the entire collection. A good core is characterized by high genetic diversity and low repetitiveness. Among the several available software, GenoCore uses a coverage criterion that does not require computationally expensive distance-based metrics. Results: ShinyCore is a new method to select a core collection through two phases. The first phase uses the coverage criterion to quickly attain a fixed coverage, and the second phase uses a newly devised score (referred to as the rarity score) to further enhance diversity. It can attain a fixed coverage faster than a currently available algorithm devised for the coverage criterion, so it will benefit users who have big data. ShinyCore attains the minimum coverage specified by a user faster than GenoCore, and it then seeks to add entries with the rarest allele for each marker. Therefore, measures of genetic diversity and distance can be improved. Conclusion: Although GenoCore is a fast algorithm, its implementation is difficult for those unfamiliar with R, ShinyCore can be easily implemented in Shiny with RStudio and an interactive web applet is available for those who are not familiar with programming languages.

Lisianthus, a globally popular ornamental plant, has a variable vase life (5–28 days). This study investigated biomass-related characteristics of four cultivars grown in soil or hydroponic cultivation with different treatment timings (vegetative and reproductive stage) and concentrations (0, 0.1, 0.3, and 0.5 mM) of salicylic acid (SA) in order to explain vase life. The results show that the SA treatment effects varied depending on cultivar, SA treatment timing, concentration, and cultivation method. Principle component analysis revealed that Blue Picote cultivar cultivated hydroponically with 0.5 mM SA at the reproductive stage had the longest vase life. Furthermore, vase life demonstrated a high positive correlation with dry weight, SPAD, Mg content, and flowering day. We developed a model using automated machine learning algorithms to estimate postharvest vase life, based on biomass-related characteristics measured during the pre-harvest period. Similar to the PCA results, this model also identified dry weight as the most influential predictor of vase life. This model proposes the possibility of estimating vase life by setting characteristics highly correlated with vase life as features for machine learning. It is anticipated that this model will be widely utilized in the floriculture industry for standardizing cut flower quality assessments in the future.

In the smart farm, we can control every detail for production. Collecting every factor that affects the crop’s final yield is necessary to optimize its efficiency. The SPAD values were observed in the ‘Star’ cultivar blueberry (Vaccinium darrowii) three times a day and at three different plant heights. The pattern of SPAD value change was different by the planting position. Ground planted blueberry (V. darrowii) represented a stable SPAD value during the day and at the different heights. However, the SPAD value was increased by time in pot-planted blueberry (V. darrowii). Also, the SPAD value of pot-planted blueberry was lower than ground planted blueberry (V. darrowii). Even when plants were of the same cultivar and age, planting conditions affected the changing pattern of SPAD in a day. Each planting condition had merit. Therefore, proper management is needed to compensate SPAD value in pot-planted blueberry (V. darrowii). This study suggests that environmental conditions like planting factors affect the final products. Therefore, to maximize the efficiency at the smart farm, the factors that could affect the final yield should be investigated and accumulated.