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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.

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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.

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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.

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T. Auxt Baugher, J. Schupp, K. Ellis, J. Remcheck, E. Winzeler, R. Duncan, S. Johnson, K. Lewis, G. Reighard, G. Henderson, M. Norton, A. Dhaddey and P. Heinemann

Hand thinning is a necessary and costly management practice in peach (Prunus persica) production. Stone fruit producers are finding it increasingly difficult to find a workforce to manually thin fruit crops, and the cost of farm labor is increasing. A new “hybrid” string thinner prototype designed to adjust crop load in vase or angled tree canopies was evaluated in processing and fresh fruit plantings in varying production systems in four U.S. growing regions in 2009. Data were uniformly collected across regions to determine blossom removal rate, fruit set, labor required for follow-up green fruit hand thinning, fruit size distribution at harvest, yield, and economic impact. String thinner trials with the variable tree forms demonstrated reduced labor costs compared with hand-thinned controls and increased crop value due to a larger distribution of fruit in marketable and higher market value sizes. Blossom removal ranged from 17% to 56%, hand thinning requirement was reduced by 19% to 100%, and fruit yield and size distribution improved in at least one string-thinning treatment per experiment. Net economic impact at optimum tractor and spindle speeds was $462 to $1490 and $264 to $934 per acre for processing and fresh market peaches, respectively. Case study interviews of growers who thinned a total of 154 acres indicated that commercial adoption of string-thinning technology would likely have positive impacts on the work place environment.