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Miles, Gaines E.

Publications and source records attributed to Miles, Gaines E..

Robotics in a controlled, ecological life support system

Controlled, Ecological Life Support Systems (CELSS) that utilize plants to provide food, water and oxygen could consume considerable amounts of labor unless crop production, recovery and processing are automated. Robotic manipulators equipped with special end-effectors and programmed to perform the sensing and materials handling tasks would minimize the amount of astronaut labor required. The Human Rated Test Facility (HRTF) planned for Johnson Space Center could discover and demonstrate techniques of crop production which can be reliably integrated with machinery to minimize labor requirements. Before the physical components (shelves, lighting fixtures, etc.) can be selected, a systems analysis must be performed to determine which alternative processes should be followed and how the materials handling tasks should be automated. Given that the current procedures used to grow crops in a CELSS may not be the best methods to automate, then what are the alternatives? How may plants be grown, harvested, processed for food, and the inedible components recycled? What commercial technologies current exist? What research efforts are underway to develop new technologies which might satisfy the need for automation in a CELSS? The answers to these questions should prove enlightening and provide some of the information necessary to perform the systems analysis. The planting, culturing, gathering, threshing and separation, food processing, and recovery of inedible portions of wheat were studied. The basic biological and materials handling processes of each task are defined and discussed. Current practices at Johnson Space Center and other NASA centers are described and compared to common production practices in the plant production industry. Technologies currently being researched which might be applicable are identified and illustrated. Finally, based on this knowledge, several scenarios are proposed for automating the tasks for wheat.

Miles, Gaines E.

Multispectral Image Processing for Plants

The development of a machine vision system to monitor plant growth and health is one of three essential steps towards establishing an intelligent system capable of accurately assessing the state of a controlled ecological life support system for long-term space travel. Besides a network of sensors, simulators are needed to predict plant features, and artificial intelligence algorithms are needed to determine the state of a plant based life support system. Multispectral machine vision and image processing can be used to sense plant features, including health and nutritional status.

Miles, Gaines E.

Plant features measurements for robotics

Initial studies of the technical feasibility of using machine vision and color image processing to measure plant health were performed. Wheat plants were grown in nutrient solutions deficient in nitrogen, potassium, and iron. An additional treatment imposed water stress on wheat plants which received a full complement of nutrients. The results for juvenile (less than 2 weeks old) wheat plants show that imaging technology can be used to detect nutrient deficiencies. The relative amount of green color in a leaf declined with increased water stress. The absolute amount of green was higher for nitrogen deficient leaves compared to the control plants. Relative greenness was lower for iron deficient leaves, but the absolute green values were higher. The data showed patterns across the leaf consistent with visual symptons. The development of additional color image processing routines to recognize these patterns would improve the performance of this sensor of plant health.

Miles, Gaines E.