Chia-Tseng Chen

RFID-integrated Multi-Functional Remote Sensing System for Seedling Production Management

A multi-functional remote sensing system (MFRSS) integrating Radio Frequency Identification (RFID) technology with remote spectral imaging and environmental sensing was developed to enhance seedling production and management in greenhouse. Consisted of a management traceability system (also known as management resume) and an environment traceability system (also known as environment resume), a traceable production management system is highly desirable for automated operations in greenhouse. This study is the first application of RFID to an automatic greenhouse seedling production system. The advantages of the MFRSS system are twofold: First, the production management information can be made traceable by establishing passive RFID on the multi-functional remote sensing system during variable boom cruising in the greenhouse. The spectral images were acquired using a color camera and a monochrome camera with a 780 nm optical filter, with the exposure time and signal gain controlled through an IEEE-1394 interface. Secondly, an automatic exposure algorithm eliminating interferences caused by sunlight variation was developed using Matlab 6.5 and LabVIEW 7.1. The tray position data were transferred to the look up table and delivered to the water management module through a DataSocket server and wireless network. The environment-sensing sub-system, including temperature, relative humidity, and photo-quantum measurements, was developed with a PCI-6023 interface to analyze their spatial distribution in the greenhouse. Not only does the mechanism established in the present study provide a basis for developing an automatic seedling production system, the environmental factors and facility status captured by the RFID-integrated MFRSS also enable a traceable seedling production management database, which is a crucial constituent for practical applications.

Estimation of Leaf Nitrogen Content using Artificial Neural Network with Cross-Learning Scheme and Significant Wavelengths

Reflectance from crops provides spectral information for non-destructive monitoring of their nutrition status. In order to develop a multi-spectral imaging system for remote sensing of the nitrogen content of crops, the significant wavelengths and calibration models were carefully evaluated in this study. The significant wavelengths in full band (400-2500 nm) and a selected band (450-950 nm), which is suitable for silicon CCD cameras, were investigated. In this article, significant wavelengths for estimating nitrogen content of cabbage seedling leaves were first determined by SMLR (stepwise multi-linear regression) analysis. A proposed ANN (artificial neural network) model with cross-learning scheme (ANN-CL) was further developed to increase the prediction accuracy. To comply with the design of a practical multi-spectral imaging system using silicon CCD cameras and commercially available bandpass filters, an ANN-CL model with four inputs of spectral absorbance at 490, 570, 600, and 680 nm was developed. The calibration results (rc = 0.93, SEC = 0.873%, and SEV = 0.960%) reduced the SEV about 15% when compared with the SMLR method with four wavelengths (SEV = 1.099%). In addition, the results were comparable to that of SMLR with seven wavelengths (rc = 0.94, SEC = 0.806%, and SEV = 0.993%) in the full band. These results indicated that the ANN model with cross-learning using spectral information at 490, 570, 600, and 680 nm could be used to develop a practical remote sensing system to predict nitrogen content of cabbage seedlings.

Development of multi-functional remote sensing system for greenhouse production

This study is aimed to develop a multi-functional remote sensing system based on spectral imaging and environmental sensing for seedling production in the greenhouses. The spectral images were grabbed with exposure time and signal gain controls through IEEE-1394 interface; and a color camera and a B/W camera with optical filter at 780 nm were used. A control program was developed to grab the good quality images using the automatic exposure algorithm with a developed software using Matlab and LabVIEW. To obtain necessary spectral information regarding tray locations and seedling growth status on greenhouse benches, a serial image processing procedures, including spatial calibration, image stitching, gray-level calibration and image segmentation were developed. The data of tray positions and growth status were transferred to the look up table (LUT) and delivered to the water management module through the DataSocket server and wireless network. Besides, the environmental sensing sub-system, including temperature, relative humidity, and lighting measurements, was also developed with the PCI-6023 interface to analyze the spatial distribution of these parameters in the greenhouse. The information of environmental status will provide a better management for seedling growth in greenhouses.

Study of deacetylation in chitinous materials using near infrared spectroscopy

Chitinous materials are important sources for bio-medical applications, and the process monitoring is one of key factors for the quality control of products. In this study, chitin and chitosan in suspension form were analyzed using near infrared (NIR) spectroscopy. Two models including multiple linear regression (MLR), modified partial least square regression (MPLSR) were adopted for studying the degree of deacetylation (DD) of chitinous materials in order to assure a better quality monitoring and control for chitosan production. During the process of the deacetylation, the real-time measurements of suspension were conducted. The MPLSR model with second derivative spectra in the range of 600-1000 and 1400-1500 nm yielded the best results, which were rc=0.991, SEC=0.019, RESC=1.4%, rp=0.990, SEP=0.022, RSEP=3.4%, RPD=9.4. The NIR measurements of DD status of chitinous suspension could be achieved by using the MLR and MPLSR models developed in this study. It provides great application potentials to the real-time and on-line quality monitoring of deacetylation process for the production of chitosan.

A DYNAMIC SIMULATION MODEL FOR SEEDLING GROWTH

This study presents a dynamic growth model applicable to automated seedling cultivation. Experiments on the influence of environmental conditions on cabbage seedling quality during three growth stages were conducted in a phytotron, and a growth database was established. An error back propagation neural network was used to analyze experimental data and develop strategies for a dynamic growth model to simulate the relationship between environmental factors (temperature, water supply and daily radiation) and cabbage seedling quality (cumulative dry matter of seedlings). A feedback algorithm and dynamic strategies were integrated into the neural network to reflect the strong importance of daily historical memory in seedling growth. The dynamic model was thus successfully developed with a coefficient of determination of 0.996 and error of 1.68%, and was verified using the data from nurseries. The dynamic model performed excellently in determining seedling growth, achieving superior results to static models. The error in predicting the cumulative dry matter resulting from seedling growth was reduced by about 80% (from 18.2% to 3.75% prediction error) when the dynamic growth model was used in place of the static model. This model not only gave a clear view of production management toward seedling growth, but also provided a basis for better environmental and quality control strategies.

Deacetylation of Chitinous Materials Using Near Infrared Spectroscopy

Abstract Chitinous materials are important sources for many bio-medical applications; and the process monitoring is a key factor for better quality control of chitosan production. In this study, chitin and chitosan in suspension form were analyzed using near infrared (NIR) spectroscopy. Two models including multiple linear regression (MLR) and modified partial least square regression (MPLSR) were adopted for studying the degree of deacetylation (DD) of chitinous materials in order to assure a better quality monitoring and control for chitosan production. During the process of the deacetylation, the real-time measurements of suspension were conducted. The MPLSR model with second derivative spectra in the range of 600–1000 and 1400–1500 nm yielded the best results, which were rc = 0.991, SEC = 0.019, RESC = 1.4%, rp = 0.990, SEP = 0.022, RSEP = 3.4%, RPD = 9.4. The NIR measurements of DD status of chitinous suspension could be achieved by using either MLR or MPLSR model developed in this study. It provides great application potentials to the real-time and on-line inspection for the quality monitoring and control of the chitosan production during deacetylation process.

Study on Water Stress Evaluation of Cabbage Seedlings by Chlorophyll Fluorescence using Multi-Spectral Imaging System

Plants would suffer many different stresses during the growth process, and water stress would be the most common one. Fluorescence of the plant would be detected when the plant is