R.M. Crassweller and S.J. Wallner
D.E. Smith and R.M. Crassweller
Water-sorbing polymers have been used in greenhouses and in arid and semiarid regions to improve soil water properties. Laboratory and field studies were conducted to investigate the effects of a cross-linked polyacrylamide polymer when incorporated into a silt loam. The soil treatments consisted of 0%, 0.06%, 0.12%, and 0.25% polymer by weight. The laboratory study consisted of four soil columns each containing a treatment. Water was added at a rate of 6.1 mm to the columns every 2 days. Soil moisture and volume was measured daily. The field experiment contained apple trees planted into soil amended with the different rates of polymer and covered with a polypropylene weed barrier. Tree growth and fruit yield were recorded from 1996-1998. The volume and bulk density of the soil-polymer matrix were dependent on the moisture content due to the swelling properties of the polymer. Bulk density was highest when no polymer was added and lowest for soil containing 0.25% polymer. Soil moisture measured by time delay reflectometry showed multiple wetting fronts in the soil columns after water was added. During the 1996 growing season, soil moisture was higher for field plots containing the weed barrier and amended with polymer; however, this trend was reversed in 1997. Tree growth was not effected in any of the years data was taken. Fruit yields did not differ between treatments in 1997. Fruit set and yield in 1998 was greater for trees planted without the weed barrier and polymer. The addition of polymer was not found to benefit apple tree growth or yields.
R.M. Crassweller and D.E. Smith
A peach and nectarine cultivar and training trial was planted in 1989. Training methods were open center (OC) and central leader (CL). The orchard was divided into three sections for early, mid-, and late season peaches with 10 individual-tree replications. The following characteristics were measured from 1989 to 1994: trunk cross sectional area, fruit yield, number of fruit, and fruit color. Early season peaches, those ripening with and before `Salem' in the OC system had significantly greater TCSA at the end of the fifth growing season. At the end of the sixth growing season, however, there was a significant training cultivar interaction. There were no differences between the mid- or late season cultivars. Measurable yields were obtained in 1991 through 1993. In all years, greater yields per tree were observed from trees in the CL system, although not significantly different for the late season cultivars. `Redhaven' and `Newhaven' had the highest yields for the early season cultivars, `Glohaven' for the mid-season cultivars, and `Cresthaven' and Biscoe for the late season cultivars. Trees in the CL system tended to have higher tree efficiency than trees in the OC system. Fruit color at harvest varied by year and training system.
R.M. Crassweller and G.M. Greene
Apple scab is the primary disease that drives commercial pesticide recommendations; therefore, the use of cultivars that are resistant to this disease would help in reducing chemical inputs in apple production. To date, there has been only limited information on the performance of the scab-resistant apple cultivars. In 1990 and 1991, apple cultivars that are resistant to apple scab were planted at two sites in Pennsylvania, one site was the Fruit Research and Extension Center (FREC) in south-central Pennsylvania, and the other was the Horticulture Research Farm (HRF) in central Pennsylvania. Horticultural characteristics measured were trunk cross-sectional area (TCSA), flowering characteristics, yields, and fruit maturity. Trees at the FREC produced fruit 1 year earlier than those at HRF. `Enterprise'/M.26 has been the most-productive cultivar at FREC, as measured by average total weight of fruit per tree. At HRF, `CO-OP 26'/M.26 had been the most-productive cultivar. At the end of the 1994 growing season, `CO-OP 26' and `Williams Pride', both on M.26, are the largest trees as measured by TCSA at HRF. At the FREC, `Enterprise' was the largest cultivar.
R.M. Crassweller, V. Esh and J.W. Travis
C. Morrow, P Heinemann, H. Sommer, R. Crassweller, R. Cole, Y. Tao, Z. Varghese and S. Deck
Research is described on the development of an automated inspection system which uses digital images and artificial intelligence techniques. Procedures have been developed for evaluating size, shape, and color of apples, potatoes, and mushrooms. Current emphasis is being placed on developing algorithms for detection of surface defects. A major effort will also be expended toward the development of an overall “quality” score for automated inspection of fruit and vegetables. The automated results are compared with those obtained using conventional manual inspection methods. Apples, potatoes, and mushrooms are the primary crops being inspected although the algorithms and techniques are applicable to many different fruits and vegetables. Color and monochromatic image processing components in “MS-DOS” and “Macintosh” computers are being used in this study.
R.M. Crassweller, J.W. Travis, P.H. Heinemann and E.G. Rajotte
Decreasing resources and increasing complexity of horticultural crop production necessitate that new technologies be developed to transfer information to commercial producers. Expert systems (ES) have been cited as potential tools that can facilitate knowledge transfer. The definitions of an expert system, however, technically only indicates a computer program that simulates the thought processes of a human expert and, as such, does not supply all the facets necessary to assist commercial producers. The combination of databases, graphic capabilities, and textual information into a comprehensive program would provide a more complete package. To differentiate the two, we use the term decision support systems (DSS). The development, testing, and release of DSS, however, require greater commitment and interdisciplinary cooperation. Developing DSS fosters interstate, interregional, and international cooperation among researchers and extension personnel. Using systems developed in fruit production as examples, we outline the value of DSS to promote cooperation, the resources necessary to develop these systems; and the attitudinal change necessary to build the systems.
R. M. Crassweller, J. W. Travis, P. H. Heinemann and E. G. Rajotte
Apple orchards are highly diversified and complex ecological and economic systems. Production is affected by a wide range of insects, diseases, weeds, and mammalian pests. The incidence of these pests is often dependant upon climatological effects; and the microclimate within orchards. An expert system, a form of artificial intelligence, has been developed and commercially released to apple growers that utilizes weather data to make recommendations regarding production decisions. Users of the system are instructed on how to establish a weather station, and to collect, and input weather data from the farm. The information is utilized to calculate disease infection periods and pesticide residues to arrive at a control recommendation. Other weather dependant modules include the scheduling of trickle irrigation as well as water application rates during a frost. An interactive demonstration of the system will be presented to the group.
Richard P. Marini, James R. Schupp, Tara Auxt Baugher and Robert Crassweller
Early-season fruit diameter measurements for ‘Gala’, ‘Fuji’, and ‘Honeycrisp’ apples in three orchards for 3 years were used to develop regression models to estimate fruit weight at harvest. Fruit weight at harvest was linearly related to fruit diameter 60 days after bloom, but intercepts and slopes were not homogeneous for all nine combinations of orchards and years for any of the cultivars. When the entire data set for a cultivar was used to develop a single predictive model, the model was biased and underpredicted fruit weight for small fruit and overpredicted fruit weight for large fruit. Adding the ratio of (fruit weight/fruit diameter) at 60 days after bloom to the model with fruit diameter at 60 days after bloom produced a less-biased model with improved coefficients of determination, and predicted values were more similar to the observed values. The (fruit weight/fruit diameter) ratio was positively related to cumulative growing degree days for the 60 days before the fruit were measured and tended to be lower in years when fruits were exposed to frosts. These multiple regression models can be used to develop tables with predicted fruit weights at harvest for varying combinations of fruit diameter and (fruit weight/fruit diameter) ratio 60 days after bloom.
Richard P. Marini, James R. Schupp, Tara Auxt Baugher and Robert Crassweller
Canopies of ‘Gala’ and ‘Fuji’ trees, trained to the vertical axis, were divided into eight vertical sections, each representing 12.5% of the tree canopy. The diameter of all ‘Gala’ fruit and fruit weight for all ‘Fuji’ fruit were recorded for each canopy section. Fruit size from most canopy sections was normally distributed and distributions were similar for most sections. Therefore, fruit size distribution for a tree can be estimated by harvesting fruit from two sections of a tree, representing 25% of the canopy. For small trees in intensive plantings, with canopy diameters less than 2.0 m, average fruit diameter or fruit weight estimated from all fruit collected from 25% of the canopy may provide estimates within 7% of the true value.