Advanced Life Support Systems(ALSS) are being studied to support human life during long-duration space missions. ALSS can be categorized into four subsystems: Crew, Biomass Production, Food Processing and Nutrition, Waste Processing and Resource Recovery. The System Studies and Modeling (SSM) team of New Jersey-NASA Specialized Center of Research and Training (NJ-NSCORT) has facilitated and conducted analyses of ALSS to address systems level issues. The underlying concept of the SSM work is to enable the effective utilization of information to aid in planning, analysis, design, management, and operation of ALSS and their components. Analytical tools and computer models for ALSS analyses have been developed and implemented for value-added information processing. The results of analyses heave been delivered through the internet for effective communication within the advanced life support (ALS) community. Several modeling paradigms have been explored by developing tools for use in systems analysis. they include objected-oriented approach for top-level models, procedureal approach for process-level models, and application of commercially available modeling tools such as $MATLAB^{R}$/$Simulink^{R}$. Every paradigm has its particular applicability for the purpose of modeling work. an overview is presented of the systems studies and modeling work conducted by the NJ-NSCORT SSM team in its efforts to provide systems analysis capabilities to the ALS community. The experience gained and the analytical tools developed from this work can be extended to solving problems encountered in general agriculture.

Chemical application is very hazardous in confined spaces under the canopy ceiling in Korean vineyard. For a small orchard sprayer adaptable to such a working condition, a low-cost autonomous steering control system was developed using two ultrasonic sensors, two electrically-operated cylinders and 80196kc microprocessor. A distance ranging system timed the round-trip for each ultrasonic wave to travel against parallel targets, placed every 1.5m spacing along both sides of a desired path. A steering control algorithm of the autonomous operation began with ranging left and right targets and the heading was decided using difference between the distances. Electrically-operated cylinders actuated steering clutches to guide the sprayer. Evaluation tests showed that the orchard sprayer could travel within RMS value of 5cm along the desired path. Ground speed did not affect the performance of the autonomous guidance system at the speed ranges of 0.29~0.52m/sec.

The starch content of apples, which is related to the maturity and ripeness, was quantified by using a computer vision system. The stained area was measured by image analysis when an apple slice was stained with KI/I$_2$ solution. The ratio of stained area of the apple slice to its whole area, so called the starch index, was defined as an indicator of the starch content. When Tsugaru apples were manually sorted into immature, turing and mature groups, their starch indices were 0.374, 0.312 and 0.129, respectively. The starch index had relationships to various physicochemical properties of Tsugaru apples. At the statistically significant level of 0.1%, it was correlated with the pH value, bio-yield force, rupture force and color of intact and skin-removed apples. At the 1% significant level, it had the correlation with the density and moisture content.

The objective of this study was to develop a real-time soil moisture meter using RF impedance. The impedance suchas capacitance and resistance (or conductance) was analyzed using parallel cylinder type capacitance probe(C-probe) and Q-meter (HP4342). The capacitance and conductance of soil increased as volumetric water content increased. The 5 MHz of modified Colpitts type crystal oscillator was designed to detect the capacitance change of the C-probe with moist soil. A third order polynomial regression model was proposed to describe the relationship between RF impedance and volumetric water content. The prototype real time moisture meter consisted of the C-probe, sample container, oscillator, frequency counter and related signal processing units. The calibration equation for measurement of volumetric moisture content of soil was developed and validated. The correlation coefficient and root mean square error between measured volumetric water content by oven method and predicted values by prototype moisture meter for unknown soil samples were 0.984 and 0.032$cm^3$$cm\^3$, respectively.

Detection of operation lines in farm works, object recognition and obstacle avoidance are essential pre-requisite technologies for unmanned agricultural machinery. A CCD camera, which has been largely used for these functions, is expensive and has difficulty in real-time signal processing. In this study, a laser range sensor was selected as the guiding vision for unmanned agricultural machinery such as a tractor. To achieve this capability, algorithms for distance measurement, signal filtering, object recognition, and obstacle avoidance were developed. Computer simulations were carried out to evaluate performance of the algorithms. Experiments were also conducted with various materials and shapes, Laser beam lost its intensity for poor reflective materials, resulting in less range value than actual, so a compensation technique was considered to be necessary. Object detection system was fabricated on an agricultural tractor and the performance was evaluated. As test result for obstacle detection and avoidance in field, to detect and avoid obstacle for path finding with guiding system for unmanned agricultural machinery was enable.

This paper describes method to diagnose abiotic stresses such ad low root temperature, low light intensity and high salinity in cucumber plants with several physiological instruments. The stresses could be detected by measuring and analyzing the differences in chlorophyll content, temperature difference between leaf and atmosphere and light absorptance at wavelengths of 480, 560, 710, 1420 and 1650nm. It was concluded that the stresses could be first diagnosed from the 3rd to 10th day after treatment and the overall accuracy of diagnosis was estimated between 25 and 75%. near-infrared spectrometer showed better and earlier detection than the other instruments investigated.