• Journal of the Korea Society of Computer and Information
• /
• v.28 no.4
• /
• pp.113-120
• /
• 2023

Smart farms are steadily increasing in research to minimize labor, energy, and quantity put into crops as IoT technology and artificial intelligence technology are combined. However, research on efficiently managing crop growth information in smart farms has been insufficient to date. In this paper, we propose a management technique that can efficiently monitor crop growth information by applying autonomous sensors to smart farms. The proposed technique focuses on collecting crop growth information through autonomous sensors and then recycling the growth information to crop cultivation. In particular, the proposed technique allocates crop growth information to one slot and then weights each crop to perform load balancing, minimizing interference between crop growth information. In addition, when processing crop growth information in four stages (sensing detection stage, sensing transmission stage, application processing stage, data management stage, etc.), the proposed technique computerizes important crop management points in real time, so an immediate warning system works outside of the management criteria. As a result of the performance evaluation, the accuracy of the autonomous sensor was improved by 22.9% on average compared to the existing technique, and the efficiency was improved by 16.4% on average compared to the existing technique.

• Journal of Biosystems Engineering
• /
• v.24 no.1
• /
• pp.25-30
• /
• 1999

The effectiveness of many greenhouse environment control methodologies depends on the growth information of crops. Acquisition of the growth information of crops requires a non-invasive and continuous monitoring method. Crop growth monitoring system using digital imaging technique was developed to conduct non-destructive and intact plant growth analyses. The monitoring system automatically measures crop growth information sends an appropriate control signal to the nutrient solution supplying system. To develop the monitoring system, a linear model that explains the relationship between the fresh weight and the top projected leaf area of a lettuce plant was developed from an experiment. The monitoring system was evaluated buy successive lettuce growing experiments. Results of the experiments showed that the developed system could estimate the fresh weight of lettuce from a lettuce image by using the linear model and generate an EC control signal according to the lettuce growth stage.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_1
• /
• pp.1199-1206
• /
• 2021

Accordingly, attention is also being paid to the agricultural use of remote sensing technique that non-destructively and continuously detects the growth and physiological status of crops. However, when remote sensing techniques are used for crop monitoring, it is possible to continuously monitor the abnormality of crops in real time. For this, standard growth information of crops is required and relative growth considering the cultivation environment must be identified. With the relationship between GDD (Growing Degree Days), which is the cumulative temperature related to crop growth obtained from ideal cultivation management, and the vegetation index as standard growth information, compared with the vegetation index observed with the spectralreflectance sensor(SRS_NDVI & SRS_PRI) in each rice paddy treated with standard cultivation management and non-fertilized, it was quantitatively identified as a time series. In the future, it is necessary to accumulate a database targeting various climatic conditions and varieties in the standard cultivation management area to establish a more reliable standard growth information.

• Korean Journal of Organic Agriculture
• /
• v.25 no.2
• /
• pp.279-293
• /
• 2017

This study suggests the yield forecast models for autumn chinese cabbage and radish using crop growth and development information. For this, we construct 24 alternative yield forecast models and compare the predictive power using root mean square percentage errors. The results shows that the predictive power of model including crop growth and development informations is better than model which does not include those informations. But the forecast errors of best forecast models exceeds 5%. Thus it is important to establish reliable data and improve forecast models.

• Journal of Biosystems Engineering
• /
• v.23 no.3
• /
• pp.285-290
• /
• 1998

Growth information of crops is essential for efficient control of greenhouse environment. However, a few non-invasive and continuous monitoring methods of crop growth has been developed. A computer vision system with a CCD camera and a frame grabber was developed to conduct non-destructive and intact plant growth analyses. The developed system was evaluated by conducting the growth analysis of lettuce. A linear model that explains the relationship between the relative crop coverage by the crop canopy and dry weight of a lettuce was presented. It was shown that this measurement method could estimate the dry weight from the relative crop coverage by the crop canopy. The result also showed that there was a high correlation between the projected top leaf area and the dry weight of the lettuce.

• Journal of Biosystems Engineering
• /
• v.36 no.3
• /
• pp.180-186
• /
• 2011

The objective of this research was to establish the estimation method of growth information on potato using Multi-Spectral Image Sensor (MSIS) and Global Positioning System (GPS). And growth estimation map for determining a prescription map over the entire field was generated. To determine the growth model, 10 ground-truth points of areas of $4m^2$ each were selected and investigated. The growth information included stem number, crop height and SPAD value. In addition, images information involving the ground-truth points were also taken by an unmanned helicopter, and reflectance value of Green, Red, and NIR bands were calculated with image processing. Then, growth status of potato was modeled by multi-regression analysis using these reflectance value of Green, Red, and NIR. As a result, potato growth information could be detected by analyzing Green, Red, and NIR images. Stem number, crop height and SPAD value could be estimated with $R^2$ values of 0.600, 0.657 and 0.747 respectively. The generated GIS map would describe variability of the potato growth in a whole field.

• Korean Journal of Remote Sensing
• /
• v.33 no.5_2
• /
• pp.751-762
• /
• 2017

A crop information system can provide information regarding crop distribution, crop growth conditions, crop yield in various forms such as monitoring, forecasting, estimation or analysis. This paper presents the design and construction of a crop information system based on data collected in Korea, USA, and China. Therein, climate data including temperature, precipitation,solar radiation are used to evaluate the impact on crop growth, NDVI (Normalized Difference Vegetation Index) data is used in crop monitoring, and crop map data is utilized for the management of crop distribution. The system has achieved three prominent results: 1) Providing information with high frequency, 2) Automatically creating the report through the analysis of the data, 3) The users to easily approach the system and retrieve the information.

• Weed & Turfgrass Science
• /
• v.7 no.1
• /
• pp.22-34
• /
• 2018

Miscanthus has been considered as the most promising bioenergy crop for lignocellulosic biomass production. In Korea, M. sacchariflorus and M. sinensis can be found easily in all regions. It is a great advantage to utilize as important species with respect to genetic and cross-breeding programs materials for creation of novel hybrids. For successful breeding programs, it is important to precisely understand the variability of morphological and growth characteristics among Miscanthus species as breeding parent materials. In this study, morphological and growth characteristics were observed in 960 germplasms of two Miscanthus species (M. sacchariflorus and M. sinensis) for growing seasons over three years. Due to the inherent characteristics of these species, the germplasm of M. sacchariflorus among the collected germplasm were reduced in plant height than in the collection area. In M. sinensis, the plant height of germplasm collected mainly from Jeju-do increased more than those collected from collection area. Sixty-one of the collected 960 germplasms were selected and investigated to the morphological characteristics. Based on the investigated morphological data, the phylogenic tree was developed. As the results, it was confirmed that there exist germplasm in which the characteristics of M. sacchariflorus and M. sinensis are mixed. This study of Miscanthus may provide an important information in order to expedite the introduction as breeding materials for creation of new hybrid.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2017.04a
• /
• pp.45-45
• /
• 2017

On-site monitoring of vegetable growth parameters, such as leaf length, leaf area, and fresh weight, in an agricultural field can provide useful information for farmers to establish farm management strategies suitable for optimum production of vegetables. Unmanned Aerial Vehicles (UAVs) are currently gaining a growing interest for agricultural applications. This study reports on validation testing of previously developed vegetable growth estimation models based on UAV-based RGB images for white radish and Chinese cabbage. Specific objective was to investigate the potential of the UAV-based RGB camera system for effectively quantifying temporal and spatial variability in the growth status of white radish and Chinese cabbage in a field. RGB images were acquired based on an automated flight mission with a multi-rotor UAV equipped with a low-cost RGB camera while automatically tracking on a predefined path. The acquired images were initially geo-located based on the log data of flight information saved into the UAV, and then mosaicked using a commerical image processing software. Otsu threshold-based crop coverage and DSM-based crop height were used as two predictor variables of the previously developed multiple linear regression models to estimate growth parameters of vegetables. The predictive capabilities of the UAV sensing system for estimating the growth parameters of the two vegetables were evaluated quantitatively by comparing to ground truth data. There were highly linear relationships between the actual and estimated leaf lengths, widths, and fresh weights, showing coefficients of determination up to 0.7. However, there were differences in slope between the ground truth and estimated values lower than 0.5, thereby requiring the use of a site-specific normalization method.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2016.07a
• /
• pp.271-272
• /
• 2016

Owing to the development of information communication technology(ICT), there are many changes taking place in the agriculture sector. As an example, an effective agricultural activities with ICT have brought an increase in agricultural production. On the other hand, these technology investment activities are very costly. Until recently, the technology development has been made in agricultural technology development perspective. This view, however, has been changed to production increase in accordance with the crop growth conditions. In this paper, we propose a device which can maintain an optimal crop growing environment. It is economic by utilizing the existing facility of farmhouse and can reduce the cost of crop growth environment improvement.