• Journal of Microbiology and Biotechnology
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• v.19 no.10
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• pp.1250-1258
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• 2009

Bacillus spp., as a type of plant growth-promoting rhizobacteria (PGPR), were studied with regards promoting plant growth and inducing plant systemic resistance. The results of greenhouse experiments with tobacco plants demonstrated that treatment with the Bacillus spp. significantly enhanced the plant height and fresh weight, while clearly lowering the disease severity rating of the tobacco mosaic virus (TMV) at 28 days post-inoculation (dpi). The TMV accumulation in the young non-inoculated leaves was remarkably lower for all the plants treated with the Bacillus spp. An RT-PCR analysis of the signaling regulatory genes Coil and NPR1, and defense genes PR-1a and PR-1b, in the tobacco treated with the Bacillus spp. revealed an association with enhancing the systemic resistance of tobacco to TMV. A further analysis of two expansin genes that regulate plant cell growth, NtEXP2 and NtEXP6, also verified a concomitant growth promotion in the roots and leaves of the tobacco responding to the Bacillus spp.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

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

• Journal of Biosystems Engineering
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• v.18 no.1
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• pp.37-47
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• 1993

This study was conducted to develop the on-line monitoring system for plant growth responses. The system consisted of two parts. One system was the measuring system and the other was its controlling system. The established measuring systems were the ultrasonic wave sensor driver for height of plant, the potentiometer for diameter of plant stem, and the weighing system with strain gage application for plant weight. Also, computer program for measurement and controlling was developed, and the whole system was tested by the fabricated plant, and the actual plant growth responses were monitored by the system. When monitoring the actual plant growth responses, even the small amount of plant growth resposes could be measured by the system within tolerable error ranges.

• Journal of Microbiology and Biotechnology
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• v.19 no.2
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• pp.194-203
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• 2009

$HpaG_{Xooc}$, from rice pathogenic bacterium Xanthomonas oryzae pv. oryzicola, is a member of the harpin group of proteins, eliciting hypersensitive cell death in non-host plants, inducing disease and insect resistance in plants, and enhancing plant growth. To express and secret the $HpaG_{Xooc}$ protein in Bacillus subtilis, we constructed a recombinant expression vector pM43HF with stronger promoter P43 and signal peptide element nprB. The SDS-PAGE and Western blot analysis demonstrated the expression of the protein $HpaG_{Xooc}$ in B. subtilis. The ELISA analysis determined the optimum condition for $HpaG_{Xooc}$ expression in B. subtilis WBHF. The biological function analysis indicated that the protein $HpaG_{Xooc}$ from B. subtilis WBHF elicits hypersensitive response(HR) and enhances the growth of tobacco. The results of RT-PCR analysis revealed that $HpaG_{Xooc}$ induces expression of the pathogenesis-related genes PR-1a and PR-1b in plant defense response.

• Journal of the Korean Society of Radiology
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• v.5 no.6
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• pp.351-355
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• 2011

In this paper, monitoring system and platform of plant growth are suggested which are required by safe crop management about disaster and radiation pollution. In addition, by monitoring plant growth, the growth of plants that can measure the size of the efficient system was developed. The expected effect of this study, first, through natural disasters and radioactive contamination monitors produce fast and accurate response function can result in improved quality and productivity. Second, the size of the plant required to maintain the measurement data can save time and expense savings. Finally, plant managers can improve work efficiency.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.4
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• pp.306-317
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• 2017

Unmanned aerial vehicles (UAVs) became popular platforms for the collection of remotely sensed data in the last years. This study deals with the monitoring of multi-temporal onion growth with very high resolution by means of low-cost equipment. The concept of the monitoring was estimation of multi-temporal onion growth using normalized difference vegetation index (NDVI) and meteorological factors. For this study, UAV imagery was taken on the Changnyeong, Hapcheon and Muan regions eight times from early February to late June during the onion growing season. In precision agriculture frequent remote sensing on such scales during the vegetation period provided important spatial information on the crop status. Meanwhile, four plant growth parameters, plant height (P.H.), leaf number (L.N.), plant diameter (P.D.) and fresh weight (F.W.) were measured for about three hundred plants (twenty plants per plot) for each field campaign. Three meteorological factors included average temperature, rainfall and irradiation over an entire onion growth period. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, $NDVI_{UAV}$ and rainfall in the model explain 88% and 68% of the P.H. and F.W. with a root mean square error (RMSE) of 7.29 cm and 59.47 g, respectively. And $NDVI_{UAV}$ in the model explain 43% of the L.N. with a RMSE of 0.96. These lead to the result that the characteristics of variations in onion growth according to $NDVI_{UAV}$ and other meteorological factors were well reflected in the model.

• Journal of Biosystems Engineering
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• v.25 no.4
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• pp.293-300
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• 2000

Automated greenhouse production system often require crop growth monitoring involving accurate quantification of plant physiological properties. Conventional methods are usually burdensome, inaccurate, and harmful to crops. A thermal image analysis system can accomplish rapid and accurate measurements of physiological-property changes of stressed crops. In this research a thermal imaging system was used to measure the leaf-temperature changes of several crops according to nutrient stresses. Thermal images were obtained from lettuce, cucumber, and pepper plants. Plants were placed in growth chamber to provide relatively constant growth environment. Results showed that there were significant differences in the temperature of stressed plants and non-stressed plants. In a case of the both N deficiency and excess, the leaf temperatures of cucumber were $2^{\circ}C$ lower than controlled temperature. The leaf temperature of cucumber was $2^{\circ}C$ lower than controlled temperature only when it was under N excess stress. For the potassium deficiency or excess stress, the leaf temperaures of cucumber and hot pepper were $2^{\circ}C$ lower than controls, respectively. The phosphorous deficiency stress dropped the leaf temperatures of cucumber and hot pepper $2^{\circ}C$ and $1.5^{\circ}C$ below than controls. However, the leaf temperature of lettuce did not change. It was possible to detect the changes in leaf temperature by infrared thermography when subjected to nutrition stress. Since the changes in leaf temperatures were different each other for plants and kinds of stresses, however, it is necessary to add a nutrient measurement system to a plant-growth monitoring system using thermography.

• Journal of Biosystems Engineering
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• v.21 no.3
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• pp.315-324
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• 1996

This study was one of a series of studies on application of machine vision and image processing to extract the geometrical features of plants and to analyze plant growth. Several algorithms were developed to measure morphological properties of plants and describing the growth development of in-situ lettuce(Lactuca sativa L.). Canopy, centroid, leaf density and fractal dimension of plant were measured from a top viewed binary image. It was capable of identifying plants by a thinning top viewed image. Overlapping the thinning side viewed image with a side viewed binary image of plant was very effective to auto-detect meaningful nodes associated with canopy components such as stem, branch, petiole and leaf. And, plant height, stem diameter, number and angle of branches, and internode length and so on were analyzed by using meaningful nodes extracted from overlapped side viewed images. Canopy, leaf density and fractal dimension showed high relation with fresh weight or growth pattern of in-situ lettuces. It was concluded that machine vision system and image processing techniques are very useful in extracting geometrical features and monitoring plant growth, although interactive methods, for some applications, were required.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.6
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• pp.2702-2706
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• 2012

In plants, factory automation systems, although most of the growth of plants by the state workforce is the restaurant to check manually. In this paper, we use two cameras to measure the plant's developmental state has been studied. Plant measurement algorithm, the camera only affordable, reliable and simple system to get the data you can build a system. In this paper, the size of plants that plant growth in the plant to measure the efficient monitoring system has been developed. By utilizing this system, the size of the plant measured data required to maintain and manage accordingly, saving time and reducing costs and improving operational efficiency of plants, plant managers, the effect could be obtained by building the actual system the performance of the proposed system was confirmed.