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Thomas J. Trout, Lee F. Johnson, and Jim Gartung

. By accounting for actual field conditions, this remote sensing method can provide better water requirement estimates than conventional, time-based crop coefficients ( Bausch, 1995 ; Hunsaker et al., 2005 ). Ongoing data collection programs will

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David Kohanbash, George Kantor, Todd Martin, and Lauren Crawford

that match crop water needs with greater precision. Advancing remote irrigation control to the next level allows the sensor node that is reading the data in the field to also control irrigation from an onboard solenoid control relay. This allows the

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Julie M. Tarara, Bernardo Chaves, Luis A. Sanchez, and Nick K. Dokoozlian

Vilas, 1992 ). We developed an automated system that provides a remote and dynamic indicator of annual vegetative and fruit growth, or increase in mass, and thus an opportunity to identify the onset of L analytically. Briefly, the trellis tension

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Lee Johnson and Thibaut Scholasch

Airborne multispectral image data were compared with intercepted photosynthetic photon flux (PPF) in commercial winegrape (Vitis vinifera) vineyards of Napa Valley, Calif. An empirically based calibration was applied to transform raw image pixel values to surface reflectance. Reflectance data from the red and near-infrared spectral regions were combined into a normalized difference vegetation index. Strong linear response was observed between the vegetation index and PPF interception ranging from 0.15 to 0.50. Study results suggest the possibility of using optical remote sensing to monitor and map vineyard shaded area, thus providing spatially explicit input to water budget models that invoke evapotranspiration crop coefficient based calculations.

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Jason Singhurst, D. L. Creech, and J. Williams

In Texas, 5,500 native species are distributed over an area comprised of ten regional habitat types. In the Piney Woods region of east Texas, 2,300 plant species occupy 15 million acres. In east Texas, the USFWS has identified 4 species that are federally endangered and 15 that are candidates for that listing. Interest in protecting rare plant habitats and reintroducing those species into similar and appropriate ecosystem types has led to new tools in research and development. Remote sensing is one; this technology is used to derive information about the earth's land and water areas from images acquired at a distance Multispectral and spatial techniques are applied to process and interpret remote sensing imagery for the purpose of producing conventional maps, thematic maps, reource surveys, etc., in the fields of agriculture, botany, archeology, forestry, geography, geology. and others. Remote sensing is used to classify vegetation, interpret forest photogrammetry, estimate timber production, and identify crops, individual plants and leaf structure. This specific project was initiated to determine the potential of remote sensing as a tool to locate known and new rare plant communities in east Texas. To develop benchmark data, a Daedalus scanner image of a previously surveyed and AutoCAD® mapped area, the Vista forest on the SFASU campus, was utilized to develop correlations between imagery, vegetation types and species. By inserting various scan images under the Vista forest AutoCAD® map, known tree species were analyzed through their specific spectral emission characteristics across nine bands. This pilot project has indicated that it is simple to separate pines from hardwoods and illustrate major land use features. However, separation at the species level or groups of species has not been achieved. This paper will trace the history of this project, describe problems and obstacles encountered, and make recommendations for future strategies.

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James A. Poss, Catherine M. Grieve, Walter B. Russell, and Stacy A. Bonos

Six cultivars or selections of Kentucky bluegrass (Poa pratensis L.) exposed to salinity stress were evaluated with ground-based remote sensing plant reflectance (R) measurements at wavelengths ranging from 350 nm to 2500 nm. Cultivars Baron, Brilliant, Cabernet, Eagleton, Midnight, and the selection A01-856, a Texas × Kentucky bluegrass hybrid (Poa arachnifera × P. pratensis), were grown outdoors from vegetative clones in a gravelly-sand medium from Apr. to Sept. 2005, in Riverside, Calif., at soil water salinities ranging from 2 to 22 dSm-1. Two Normalized Difference Vegetation Indicies (NDVI) were developed based on: 1) canopy reflectance in the visible domain at 695 and 670 nm and 2) an average of eight wavelengths in mid-infrared [Ravg = (R:1500, R:1680, R:1740, R:1940, R:2050, R:2170, R:2290, and R:2470 nm/8)] and the reference wavelength (670 nm). Both NDVIs were significantly sensitive to salinity-induced changes in grass canopies and were able to discriminate significantly between the salt-tolerant cultivars (`Baron', `Brilliant', and `Eagleton') and salt-sensitive cultivars (`Cabernet', `Midnight', and A01-856). Another remotely sensed index, based on the derivative of the absorbance (1/R) in the red-edge region between 600 and 800 nm, also generated a similar ranking to the NDVIs and biomass for the six cultivars. These findings indicate that remote sensing of canopy reflectance may represent an additional tool to evaluate and explain the biophysical or physiological differences among Kentucky bluegrass cultivars related to salt tolerance.

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W.H. Tietjen, J. Grande, P.J. Nitzsche, T. Manning, and E. Dager

Remote areas of the United States and developing nations depend on either electric grid extension or diesel power for operating crop irrigation systems. However, electric grid extension is expensive and often impractical. Diesel pumps are expensive, polluting, and require maintenance to operate. Utilizing the energy of the sun, captured by photovoltaic panels, to power irrigation systems offers a cost-effective, pollution-free, and maintenance-free alternative. Solar-powered pumping systems are capable of delivering water from rivers or wells in volumes up to 2000 gal/min. Combining solar power with drip irrigation takes advantage of the natural coincidence of peak energy from the sun and the crop's peak need for water. In 1999, cabbage was grown comparing solar and conventionally powered drip irrigation systems at the Rutgers Univ. Snyder Research and Extension Farm, Pittstown, N.J. The solar system was operated by a 1.5-horsepower motor powered by 18 solar modules.

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John LeBoeuf

The initial surge of interest in precision agriculture technologies exhibited by innovators and early adopters involved in crop production appears to have crossed over an important threshold. As valuable field experience increases and learning by doing advances, successful applications of management practices are being identified even though few are adequately documented with economic benefits. Access to accurate information pertaining to applications of site-specific management would be expected to motivate more producers to incorporate technology uses with crop production. This next group of producers has been watching technology developments as they preferred to avoid risk and wait for identifiable benefits. Waiting for detailed case studies involving high value fruits and vegetables may be the wrong approach to take. Fierce competition and strict confidentiality are expected in the fresh market industry. Thus, personal experience with technology becomes more relevant to innovative producers than published literature. This is especially true in California where 350 different crops are produced. High resolution imagery from digital aerial and satellite sensors has been used in crop production in California to identify plant stress, direct plant tissue and soil sampling efforts, and provide information for analysis and interpretation of crop growth. Examples of remote sensing imagery that have provided valuable in-season progress reports will be identified. The focus will be on practice, not theory, as seen from an industry perspective.

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Noboru Muramatsu, Naoki Sakurai, Naoki Wada, Ryoichi Yamamoto, Keiichi Tanaka, Toshikazu Asakura, Yuko Ishikawa-Takano, and Donald J. Nevins

Developmental changes in fruit texture during ripening were determined based on remote sensing of surface vibrations. The technique was evaluated with fruit having a range of firmness and textural characteristics including kiwifruit [Actinidia deliciosa (A. Chev.) Liang et Ferguson, `Hayward'] treated with ethylene, apple (Malus ×domestica Borkh. `Ourei') stored at 10 or 20 °C and persimmon (Diospyros kaki L. `Fuyu') stored at 10 °C. In each case fruit were placed on a stage capable of imparting sine wave vibrations with frequencies ranging from 5 to 2,000 Hz. The vibration transmitted through the fruit to the top surface was precisely measured without any direct contact with the Doppler laser vibrometer. The perceived fruit surface signal was corrected by subtraction of the stage vibration based on an accelerometer signal, hence the true vibrational signal of the fruit mass was determined. The phase shift at selected frequencies was based on the difference between the input and output vibration. The phase shift significantly increased in the range of 1,200 to 1,600 Hz in all three kinds of fruit analyzed as a function of maturation. The resonance frequency, peak height, and peak width of second resonance peak were also determined. The resonance frequency decreased in all fruit as a function of maturation. In apple, the peak height decreased as a function of storage duration, but in kiwifruit and persimmon the peak height fluctuated and a consistent pattern in this particular parameter was not observed. The amplitude of vibration decreased as a function of maturation when the imposed vibration exceeded 1,200 Hz. Data clearly showed that the Doppler laser vibrometer is capable of detecting the phase shift and vibration amplitude of fruit, and can be used as a versatile remote sensory tool for determining fruit firmness and for evaluations of maturity.

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Eileen M. Perry, Ian Goodwin, and David Cornwall

( Fitzgerald et al., 2010 ; Perry et al., 2012 ) shows that using remote sensing to estimate N in wheat canopies is effective, and calculation of the Canopy Chlorophyll Content Index (CCCI) from canopy spectral reflectance of wheat showed good correlation with