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Journal articles on the topic 'Tree training'
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1
Graehl, Jonathan, Kevin Knight, and Jonathan May. "Training Tree Transducers." Computational Linguistics 34, no. 3 (September 2008): 391–427. http://dx.doi.org/10.1162/coli.2008.07-051-r2-03-57.
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Many probabilistic models for natural language are now written in terms of hierarchical tree structure. Tree-based modeling still lacks many of the standard tools taken for granted in (finite-state) string-based modeling. The theory of tree transducer automata provides a possible framework to draw on, as it has been worked out in an extensive literature. We motivate the use of tree transducers for natural language and address the training problem for probabilistic tree-to-tree and tree-to-string transducers.
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Blackburn, Bryan, and Curt R. Rom. "Early Performance of Six Peach Training Systems." HortScience 33, no. 4 (July 1998): 600a—600. http://dx.doi.org/10.21273/hortsci.33.4.600a.
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The effects of six freestanding training systems (Open Center, Untrained, 2-Scaffold V, 4-Scaffold V, Leaning V, and Central Leader at tree densities of 161, 161, 245, 375, 375, and 300 trees/acre, respectively) on yield and tree growth of `Redhaven' on Lovell rootstock were evaluated. Open-center and untrained trees were largest and had greatest yields per tree. The 2-scaffold V had the greatest production in kilograms per acre. Early productivity was related to tree density and pruning severity, not tree size. Training systems had no effect on fruit size.
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Churn, Peter. "The tree of training." Education for Primary Care 30, no. 4 (May 30, 2019): 243–45. http://dx.doi.org/10.1080/14739879.2019.1613934.
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Evans, G. E., D. E. Deyton, and J. W. High. "COMPARISONS OF PLANT DENSITIES AND TRAINING SYSTEMS OF PEACH TREES." HortScience 27, no. 6 (June 1992): 638d—638. http://dx.doi.org/10.21273/hortsci.27.6.638d.
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`Redhaven' peach tree plantings were established in 1985 to compare tree densities (299 trees/ha to 1794 trees/ha) and training systems (Open Vase, Central Leader, Y-shaped, Palmette Trellis, Tatura Trellis, and MIA Trellis). Tree trunk growth (diameter) was significantly less as the population of trees increased. Trunks of trees trained to the Open Vase were larger than Central Leader or Y-shaped trees. In 1988, yields per ha increased as tree density increased. Trees trained to the Tatura Trellis (897 trees/ha) had the highest yields (27.7 t/ha). Trees trained to the Central Leader and planted at 1794, 897, and 598 trees/ha had next highest yields of 24.5, 21.4, and 24.3 t/ha, respectively. By the 6th year, yield differences were not generally related to tree density. The top yielding systems were Open Vase (598 trees/ha) and Tatura Trellis (897 trees/ha) with yields of 32.1 and 29.0 t/ha, respectively. Trees trained to Open Vase had higher yield efficiencies (kg/cm2 limb CSA) in 1991 than trees in other systems-spacings and had yields of 23.6, 27.4, and 32.1 t/ha for plant densities of 299, 448 and 598 trees/ha, respectively.
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Blažková, J., H. Drahošová, and I. Hlušičková. "Tree vigour, cropping, and phenology of sweet cherries in two systems of tree training on dwarf rootstocks." Horticultural Science 37, No. 4 (November 3, 2010): 127–38. http://dx.doi.org/10.17221/60/2010-hortsci.
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Five cultivars and four rootstocks (Gisela 5, P-HL-A, P-HL-B, and Tabel Edabriz) were evaluated on trees in fruiting stage trained like spindle and on trellis. Tree vigour was significantly related to cultivar, rootstock and to tree training. Spindles were generally more vigorous than trees on trellis with exception of cv. Kordia. In several cases special combinations of cultivar, rootstock, and method of tree training differed significantly from mean effects of the three factors. Time of flowering was significantly dependent on the cultivar and varied annually within 15 days. Time of fruit harvest was also influenced by the rootstock and in two cases mutually contradictory to the tree training method. Yields per tree were generally dependent on the cultivar. With Burlat and cv. Kordia rootstock and tree training were also important. Higher specific yields were recorded on trellis-trained trees. Remarkable in this respect were Vanda and trees of Summit on P-HL-B and Starking Hardy Giant on Tabel Edabriz. Higher specific yields on spindle had Kordia on P-HL-A and Tabel Edabriz and Burlat on P-HL-A. Mean values of annual yields per hectare in spindle ranged between 10.0 to 17.5 t whereas in trellis between 6.7 to 12.3 t. The absolute highest annual yield (35.7 t) was recorded on spindle trees of Kordia on P-HL-A. In trellis the highest yield of 27.1 t had Kordia on Gisela 5. The advantage of spindle over trellis was greater in Burlat and Kordia but much lower in cv. Vanda. Fruit size mainly depended on the year. Only two rootstocks influenced fruit size differently in some years. Training system had no effect on fruit size.
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Ozkan, Y., K. Yıldız, E. Küçüker, Ç. Çekiç, M. Özgen, and Y. Akça. " Early performance of cv. Jonagold apple on M.9 in five tree training systems." Horticultural Science 39, No. 4 (November 19, 2012): 158–63. http://dx.doi.org/10.17221/35/2012-hortsci.
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The effects of five training systems on tree growth, fruit yield and some fruit characteristics were assessed in Jonagold apple cv. grafted on M.9 rootstock. The trees were trained in one of five ways: slender spindle (SS; 4,761 trees/ha), vertical axis (VA; 2,857 trees/ha), hytec (HT; 1,904 tree/ha) and two different tree densities of super spindle (L-Super S with 5,000 trees/ha; H-Super S with 10,000 trees/ha). Trunk cross-sectional area (TCA) was higher in HT and VA than SS, L-Super S and H-Super S in the 4<sup>th</sup> year. While HT had the highest cumulative yield/tree, the lowest cumulative yield was observed in H-Super S. Although HT had the highest yield/ tree, it ranked the last in cumulative yield efficiency (CYE) due to high TCA. The highest (CYE) was measured in trees trained as L-Super S. When cumulative yields (CY)/ha were evaluated, the yield advantage of high density planting was clearly evident for the first three cropping years. H-Super S systems (10,000 trees/ha) had the highest CY/ha and achieved a yield of 91.24 t/ha in year 4. HT (1,904 trees/ha) had the lowest CY/ha (33.46 t). Training systems had no consistent effect on average fruit diameter, weight, firmness, soluble solid and titratable acidity.
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Connell, Joseph H., Warren Micke, James Yeager, Janine Hasey, Bill Krueger, and Craig Weakley. "TRAINING PERMANENT AND TEMPORARY TREES." HortScience 25, no. 9 (September 1990): 1169c—1169. http://dx.doi.org/10.21273/hortsci.25.9.1169c.
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High orchard establishment costs require greater production early in an orchard's life. Our goal was to develop temporary trees at the least cost with the best early production. Health and longevity of permanent trees is essential. Six pruning treatments were evaluated in five-tree plots using a randomized complete block design. Each treatment was replicated four times on the `Butte' and `Mission' almond cultivars. After six years, temporary trees receiving the least pruning had the highest yields. Permanent trees had lower yields since more pruning was done in the second through fourth dormant seasons to develop branch framework for the long term. `Butte' and `Mission' responses to treatment varied due to varietal growth habits. Effects on tree development and the need for later corrective pruning were noted. After four harvests, yields were greater with less pruning.
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McFadyen, Lisa, David Robertson, Stephen Morris, and Trevor Olesen. "Effects of Early Tree Training on Macadamia Production." HortTechnology 26, no. 6 (December 2016): 707–12. http://dx.doi.org/10.21273/horttech03479-16.
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The current industry recommendation for the training of young macadamia (Macadamia integrifolia, Macadamia tetraphylla, and hybrids) trees is to prune the trees to a central leader, but there is little science to support this recommendation. We planted an orchard to assess the merits of central leader training relative to a minimally pruned control. We used two cultivars, 246 and 816, representing spreading and upright growth habits, respectively. Training to a central leader reduced cumulative yields per tree over the first 3 years of production by 16% in ‘246’ and 23% in ‘816’. The reduction in yield was correlated with a reduction in the number of racemes per tree. The early training of the upright cultivar 816 appeared to improve its resistance to storm damage, but no such effect was seen in the more spreading cultivar 246. The yield penalty in training young trees to a central leader is such that industry should reconsider its early tree training recommendation.
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Campbell, JE, HI Nicol, and BR Cullis. "Effect of four different canopy shapes on apple yields." Australian Journal of Experimental Agriculture 36, no. 4 (1996): 489. http://dx.doi.org/10.1071/ea9960489.
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The cultivars, Jonathan, Delicious and Granny Smith apple (Malus domestica Borkh.) trees on MM.106, Northern Spy, Seedling and MM.102 rootstocks were trained as-vase, central leader, palmette and Hawkes Bay multi-leade; systems for 18 years. Rootstock significantly affected tree size, and there were interactions of rootstock with training systems or cultivars. There was also an interaction between training ' systems and cultivars. In the early years, while the training systems were being established, fruit yield was inversely related to the severity of the pruning; central leader- and palmette-trained trees had higher yields and tree efficiency than Hawkes Bay trees whose yields and tree efficiency were higher than vase trees. When yields reached maximum and the training systems became well established (after about 8 years of cropping), the total annual yield and tree efficiency per tree of individual training systems within a cultivar and rootstock differed only slightly. Cumulative yields of central leader, palmette and Hawkes Bay were higher than vase in the early years of all training systems and cultivars, while tree size was often smaller. In the latter years, cumulative yields of the central leader, palmette and Hawkes Bay systems remained slightly higher than vase, except with the less vigorous Jonathan and Granny Smith/ MM.102 combinations where yields were similar.
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Fallahi, Esmaeil, Bahar Fallahi, and Shahla Mahdavi. "Branch Configuration Impacts on Production, Fruit Quality, and Leaf Minerals of ‘Aztec Fuji’ Apple Trees in an Upright Single Row High-Density Orchard System Over Five Years." Journal of Agricultural Science 12, no. 4 (March 15, 2020): 53. http://dx.doi.org/10.5539/jas.v12n4p53.
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Tree architectures play a critical role in the productivity of high-density orchards, but limited information is available in this subject. We studied effects of three branch configurations on tree growth, yield components, fruit quality and leaf mineral nutrients in ‘Aztec Fuji’ apple (Malus domestica Bork.) in a single row upright high-density system under southwest Idaho, USA conditions over 2012-2016. This study revealed that trees trained into a Tall Spindle (TS) had larger trunk cross sectional area (TCSA) than those with an Overlapped Arm (OA) system. Trees trained into a TS had higher number of fruit and yield per tree, three years after planting in 2012, than those with a Tipping Arm (TA) or OA system. However, in 2013, trees with a TA system had higher yield than those with a TS or OA configuration due to trees’ biennial bearing habit and higher spur formation in trees with a TA system. Trees receiving a TA training had lower biennial bearing index between all consecutive years. Trees with an OA training had smaller fruit than those with either a TA or TS training in all years between 2012-2016. Training systems did not have any effect on fruit color, soluble solids concentration, or starch degradation pattern at harvest. However, fruit from trees with an OA training had higher firmness and lower water core than those from trees with a TS or TA training. Leaves from trees receiving a TA training had greater leaf area, fresh weight, and potassium (K) and magnesium (Mn) concentrations than those with other trainings. Leaves from trees receiving an OA training had higher leaf iron (Fe), zinc (Zn), and copper (Cu) than those with a TS training. In this study, we concluded that training trees into a TA configuration rather than an OA system is recommended if the management and operation of apple production mandate the use of an “upright wall” structure to facilitate mechanical harvesting.
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Schupp, J. R., and S. I. Koller. "Effect of Four Training Systems on Growth and Productivity of `Cortland'/M.9 EMLA Apple Trees." HortScience 33, no. 3 (June 1998): 451e—451. http://dx.doi.org/10.21273/hortsci.33.3.451e.
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`Cortland'/M.9 EMLA trees were planted in 1991 at 1.8 ×4.2-m spacing. The trees were trained to one of four systems: 1) Vertical Axis; 2) Y trellis; 3) Solen; or 4) Palmette trellis. Tree survival was 86% for Palmette trees and approached 100% for the other three systems. Annual yield and cumulative yield per tree of Vertical Axis and Y trellis was twice that of Solen or Palmette. Tree vigor was sub-optimal relative to planting distance in this study. Trunk cross-sectional area of Vertical Axis trees was larger than that of trees trained to Solen or Palmette, while trees trained to Y trellis were intermediate in trunk growth. Canopy volumes of Vertical Axis and Y trellis trees were similar, and greater than that of Solen or Palmette trees. Fruit size on Solen and Palmette trees was larger than that of Y trellis trees in 1995 and 1996, while fruit size on Vertical Axis trees was intermediate. Cumulative yield per cubic meter of canopy volume was the same for all four systems, suggesting that differences in productivity among systems were attributable to the effects of tree training practices on tree size, not to differences among systems in precocity or efficiency. The low heading cut needed to establish the lowest tier of branches on the Palmette system reduced tree vigor and in some cases, resulted in mortality. The horizontal training of the primary branches of the Solen severely reduced tree vigor. In this study, where tree vigor was sub-optimal due to rootstock selection, the additional restrictions in tree growth resulting from restrictive training methods resulted in a significant loss in productivity.
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Kohek, Štefan, Nikola Guid, Stanislav Tojnko, Tatjana Unuk, and Simon Kolmanič. "EduAPPLE: Interactive Teaching Tool for Apple Tree Crown Formation." HortTechnology 25, no. 2 (April 2015): 238–46. http://dx.doi.org/10.21273/horttech.25.2.238.
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In central Europe there are many backyard fruit growers who receive no proper education about fruit tree care. Their knowledge is mostly based on various handbooks and learning through trial and error. Such learning is slow and can even result in damage to the tree. To shorten the learning time, a new interactive teaching tool EduAPPLE has been developed based on the basic laws of apple tree (Malus ×domestica) growth and training. Pruning, weighting, tying, and spreading can be interactively practiced over and over again without any danger to the actual trees. Training responses are immediately seen and are analogous to those of real trees. They are not only predetermined by a set of rules, but also calculated based on the changes the actions cause to the light interception of the tree. EduAPPLE enables high-quality views of trees and their light interception from all angles in real time and is designed for education regarding free-standing apple tree training (spindle). It can, therefore, be used in schools, universities, and other educational organizations, as well as by tree growers, including the large number of growers having only a few fruit trees.
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Hampson, Cheryl R., Harvey A. Quamme, Frank Kappel, and Robert T. Brownlee. "Varying Density with Constant Rectangularity: I. Effects on Apple Tree Growth and Light Interception in Three Training Systems over Ten Years." HortScience 39, no. 3 (June 2004): 501–6. http://dx.doi.org/10.21273/hortsci.39.3.501.
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The effect of increasing planting density at constant rectangularity on the vegetative growth and light interception of apple [Malus ×sylvestris (L) var. domestica (Borkh.) Mansf.] trees in three training systems (slender spindle, tall spindle, and Geneva Y trellis) was assessed for 10 years. Five tree densities (from 1125 to 3226 trees/ha) and two cultivars (Royal Gala and Summerland McIntosh) were tested in a fully guarded split-split plot design. Planting density was the most influential factor. As tree density increased, tree size decreased, and leaf area index and light interception increased. A planting density between 1800 and 2200 trees/ha (depending on training system) was needed to achieve at least 50% light interception under the conditions of this trial. Training system altered tree height and canopy diameter, but not total scion weight. Training system began to influence light interception in the sixth leaf, when the Y trellis system intercepted more light than either spindle form. Trees trained to the Y trellis tended to have more spurs and a lower proportion of total leaf area in shoot leaves than the other two systems. The slender and tall spindles were similar in most aspects of performance. Tall spindles did not intercept more light than slender spindles. `Royal Gala' and `Summerland McIntosh' trees intercepted about the same amount of light. `Royal Gala' had greater spur leaf area per tree than `Summerland McIntosh', but the cultivars were similar in shoot leaf area per tree and spur density.
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Stančić, Ivica, Jelica Živić, Saša Petrović, and Desimir Knežević. "THE EFECTS OF A CULTIVATION METHOD ON TOMATO YIELD SOLANUM LYCOPERSICUM L." International Conference on Technics, Technologies and Education, ICTTE 2019 (2019): 434–37. http://dx.doi.org/10.15547/ictte.2019.07.008.
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Investigated the effect of different forms of training system the yield of tomatoes in greenhouses. The study involved the genetic forms of tomatoes with one, two and three trees. The influence of training system on the number of fruits per plant, fruit weight, fruit yield per plant and fruit yield per m2. The highest average yield per plant was achieved by growing tomatoes in the three trees, planting a tree in the form given by the average large fruit, and the highest number of fruits per plant was obtained with the training system with three trees, and the lowest in the training system on a tree. When growing on a tree reaches maturity at the earliest, and larger fruits, and the growing maturation of the two trees is a little later, the fruits are slightly smaller, but higher total yield of tomatoes. Growth on three trees achieved the highest yield, but the fruit is considerably smaller, which reduces their market value.
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Kułaga, Rafał, and Marek Gorgoń. "FPGA Implementation of Decision Trees and Tree Ensembles for Character Recognition in Vivado Hls." Image Processing & Communications 19, no. 2-3 (September 1, 2014): 71–82. http://dx.doi.org/10.1515/ipc-2015-0012.
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Abstract Decision trees and decision tree ensembles are popular machine learning methods, used for classification and regression. In this paper, an FPGA implementation of decision trees and tree ensembles for letter and digit recognition in Vivado High-Level Synthesis is presented. Two publicly available datasets were used at both training and testing stages. Different optimizations for tree code and tree node layout in memory are considered. Classification accuracy, throughput and resource usage for different training algorithms, tree depths and ensemble sizes are discussed. The correctness of the module’s operation was verified using C/RTL cosimulation and on a Zynq-7000 SoC device, using Xillybus IP core for data transfer between the processing system and the programmable logic.
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Whiting, Matthew D., Gregory Lang, and David Ophardt. "Rootstock and Training System Affect Sweet Cherry Growth, Yield, and Fruit Quality." HortScience 40, no. 3 (June 2005): 582–86. http://dx.doi.org/10.21273/hortsci.40.3.582.
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Traditional sweet cherry (Prunus avium L.) training systems in the United States are based upon vigorous rootstocks and multiple leader vase canopy architectures. The sweet cherry research lab at Washington State University has been investigating the potential of new rootstocks and training systems to improve production efficiency and produce high quality fruit. This paper describes the effects of three rootstocks—Mazzard (P. avium), `Gisela 6', and `Gisela 5' (P. cerasus × P. canescens)—and four training systems—central leader, multiple-leader bush, palmette, and y-trellis—on `Bing' sweet cherry tree vigor, fruit yield and quality over a seven year period. Compared to trees on Mazzard, trees on `Gisela 5' and `Gisela 6' had 45% and 20% lower trunk cross-sectional areas after 7 seasons, respectively. Trees on `Gisela 6' were the most productive, yielding between 13% and 31% more than those on `Gisela 5' and 657% to 212% more than trees on Mazzard, depending on year. Both Gisela rootstocks significantly improved precocity compared to Mazzard, bearing fruit in year 3 in the orchard. Canopy architecture had only moderate effects on tree vigor and fruit yield. Across rootstocks, bush-trained trees were about 25% less productive compared to the other systems, which exhibited similar cumulative yields (102 kg/tree). Fruit weight was negatively and closely (r2 = 0.84) related to tree yield efficiency (kg·cm–2). Crop value was related positively to fruit yield.
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Layne, Desmond R., and Eric J. Hitzler. "(310) Peach Orchard Systems Management Trial: The First Six Years." HortScience 40, no. 4 (July 2005): 1026A—1026. http://dx.doi.org/10.21273/hortsci.40.4.1026a.
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In 1999, a trial was established at the Clemson University Musser Fruit Research Farm to investigate the effects of training system/tree density, rootstock, and irrigation/fertilization on tree growth, productivity and profitability. The replicated trial comprised three training systems/tree densities (open center–332 trees/ha; quad V–664 trees/ha; and perpendicular V–996 trees/ha). The two rootstocks used were Lovell and Guardian. Three irrigation/fertilization treatments included: natural rainfall only plus granular program; supplemental irrigation plus granular; and supplemental irrigation plus fertigation at a reduced nitrogen rate from the granular program. The scion was the popular midseason cultivar Redglobe. There were a total of 18 treatment combinations replicated 4 times with 5 trees per treatment plot. Soil moisture was determined by TDR and pan evaporation monitored by weather station. Seventy-two minirhizotron tubes were installed in 2002 to monitor fine root growth. During 1999–2001, drought conditions limited rainfall to 35% below the annual average. Spring freeze events in 2001 and 2004 reduced crop load disproportionately in shorter, open-center trees. As tree density within the tree row increased, trunk cross sectional area (TCA) decreased. Trees on Guardian rootstock had significantly greater TCA than Lovell each year. Trees that received supplemental irrigation had greater TCA than nonirrigated trees each year. By reducing N rate through fertigation, TCA was reduced. Cumulative yield was not affected by training system/tree density. Cumulative yield was not affected by rootstock. Cumulative yield was 7% greater by supplemental irrigation. Pruning, thinning and harvesting was easier in V-systems than for open center trees.
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Njoroge, J. M., and J. K. Kimemia. "Influence of Tree Training and Plant Density on Yields of an Improved Cultivar of Coffea arabica." Experimental Agriculture 30, no. 1 (January 1994): 89–94. http://dx.doi.org/10.1017/s0014479700023887.
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SummaryThe effect on Coffea arabica cv. Ruiru 11 of training the trees on a single or two stem system at planting densities of between 1600 and 4800 trees ha−1 during the first production cycle after establishment was studied at three contrasting sites in Kenya. Training tree growth in the two stem system significantly depressed yields from the first two crops at all sites, irrespective of plant density, but yields caught up with the production levels of the single stem system faster in the wetter than in the drier zone. Yields of clean coffee increased with tree density up to 4000 trees ha−1 at all sites, irrespective of tree training method, but increased more slowly at populations of more than 4000 trees ha−1, especially in the drier zone. The proportion of large, grade A, coffee beans was not significantly influenced by the treatments studied.
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Elfying, D. C. "EFFECTS OF TREE SUPPORT AND TRAINING SYSTEM ON APPLE TREE GROWTH AND PRODUCTIVITY." HortScience 27, no. 6 (June 1992): 620a—620. http://dx.doi.org/10.21273/hortsci.27.6.620a.
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'Empire' (E) and 'Marshall McIntosh' (MM)/'Mark' trees planted in 1986 were trained to the freestanding central leader (CL), central leader with annual extension-shoot heading (HCL), slender spindle (SS) or vertical axis (VA). Support with a full tree stake (SS & VA) had little effect on shoot growth. HCL increased shoot number and mean length. Fewer pruning cuts were made on supported trees, while more were made on HCL trees. Dry weight of prunings 1989-91 was the same for all MM trees, while in E trees, CL and SS had lower pruning weights than HCL and VA. Bloom density was uninfluenced by support or training. Fruit set was greater in 1990 and 1991 on supported E trees, and in 1990 on supported MM trees. Yield was greater on supported systems in 3 out of 4 production years. Total yield after 6 years of age was 26-38% greater for supported trees of both cultivars. Bienniality was reduced about 15% by support in MM trees but unaffected by support or training in E trees. Net total crop value (estimated annual crop value minus annual harvest cost and support cost, if applicable, annual 10% discount rate) in 1991 was approximately $1600 per ha greater for supported E trees and $270 per ha greater for supported MM trees.
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Scorza, Ralph. "GENETIC MANIPULATION OF TREE FRUIT ARCHITECTURE." HortScience 25, no. 9 (September 1990): 1177d—1177. http://dx.doi.org/10.21273/hortsci.25.9.1177d.
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The genetically available range in tree fruit architecture has not been fully utilized for tree fruit breeding or production. Higher planting densities, new training systems, high coats of pruning, the need to eliminate ladders in the orchard, and mechanized harvesting require a re-evaluation of tree architecture. Dwarf, semidwarf, columnar, and spur-type trees may be more efficient than standard tree forms, especially when combined with specific production systems. Studies of the growth of novel tree types and elucidation of the inheritance of growth habit components may allow breeders to combine canopy growth characteristics to produce trees tailored to evolving production systems.
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Ferree, David C. "Early Performance of Two Apple Cultivars in Three Training Systems." HortScience 29, no. 9 (September 1994): 1004–7. http://dx.doi.org/10.21273/hortsci.29.9.1004.
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In 1987, `Smoothee Golden Delicious' (`Smoothee') and `Lawspur Rome Beauty' (`Lawspur') apple (Malus domestica Borkh,) trees were planted and trained as central leaders or palmette leaders on M.7 and Mark rootstocks or were planted as slender spindles on Mark rootstocks. `Smoothee' trees were larger and had consistently greater yields and production per unit trunk cross-sectional area (TCA) than `Lawspur' trees. Slender spindle trees had lower early yields per tree and TCA but had greater cumulative yields per hectare than trees in the other training systems. In the fifth and sixth growing seasons, `Smoothee' trained as palmette leaders tended to have higher yields per hectare then central leader trees. Training system had little influence on `Lawspur' tree yields. Limb bending in 1989 increased flower density in 1989 and 1990. Cumulative yield per hectare increased 11% as a result of limb bending of trees on Mark rootstock, but bending had no influence on trees on M.7 rootstock. `Smoothee' on Mark had higher cumulative yields per hectare with the palmette leader and central leader than either `Smoothee' on M.7 in either training system or any combination with `Lawspur'.
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AGLAR, Erdal, Kenan YILDIZ, and Lynn Edwards LONG. "The Effects of Rootstocks and Training Systems on the Early Performance of ‘0900 Ziraat’ Sweet Cherry." Notulae Botanicae Horti Agrobotanici Cluj-Napoca 44, no. 2 (December 14, 2016): 573–78. http://dx.doi.org/10.15835/nbha44210401.
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The effects of three rootstocks (‘Gisela 5’, ‘Gisela 6’ and ‘MaxMa 14’) and three training systems (Spanish bush, Steep leader and Vogel central leader) on early performance of ‘0900 Ziraat’ sweet cherry were compared. There have been significant differences among both rootstocks and training systems in terms of tree heights. At the end of the fourth year, while the height of the trees grafted on ‘Gisela 5’ was 238.3 cm, those grafted on ‘MaxMa 14’ reached 266.4 cm in height. While the shortest tree height was obtained from Spanish bush system, heights of the trees in Steep leader and Vogel central leader training systems were found to be at similar levels. ‘Gisela’ 5 had lower trunk cross section area (TCSA) than ‘Gisela 6’ and ‘MaxMa 14’ rootstocks. Among three systems, trees trained to Steep leader had the highest TCSA, followed by Spanish bush and Vogel central leader. Interactions were found between rootstock and training system for yield and yield efficiency. On ‘Gisela 6’, cumulative yield of Vogel central leader system (17.0 g/tree) was significantly higher than Spanish bush (14.8 g/tree) and Steep leader (12.6 g/tree). On the other hand, on ‘MaxMa 14’, there were not significant differences in cumulative yield per tree among training systems. On ‘Gisela 5’ and ‘Gisela 6’, the highest yield efficiency were observed in trees trained as Vogel central leader. Yield efficiency of Vogel central leader (0.49 kg cm-²) was two time higher than those of Spanish bush (0.29 kg cm-²) and Steep leader (0.26 kg cm-²) on ‘Gisela 6’. The weight of fruits from trees grafted on ‘Gisela 5’ was lower than those from trees on ‘Gisela 6’ and ‘MaxMa 14’. In the fourth year, while the average fruit weight was 5.86 g on ‘Gisela 5’, it was 6.00 and 6.25 g on ‘Gisela 6’ and ‘MaxMa 14’ rootstocks respectively.
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Safarov, Nakhchivan Yusub ogly. "New Look on Training the General Physics." International Journal of Life Sciences 9, no. 6 (September 26, 2015): 83–90. http://dx.doi.org/10.3126/ijls.v9i6.13428.
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In this paper the System of training the General Physics with a unified approach is construed. The technique of creation of this pedagogical system is explained. Having established analogy between elements of system and elements of a natural tree, training process is designed in the form of the natural tree (“Training tree”).The analogy of evolutions of the "Training Tree” and a natural tree have been established. A synergism of the developed training system is shown, it is established that it adequately meets the criteria of the training technology.
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Povhan, Igor. "LOGICAL CLASSIFICATION TREES IN RECOGNITION PROBLEMS." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 10, no. 2 (June 30, 2020): 12–15. http://dx.doi.org/10.35784/iapgos.927.
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The paper is dedicated to algorithms for constructing a logical tree of classification. Nowadays, there exist many algorithms for constructing logical classification trees. However, all of them, as a rule, are reduced to the construction of a single classification tree based on the data of a fixed training sample. There are very few algorithms for constructing recognition trees that are designed for large data sets. It is obvious that such sets have objective factors associated with the peculiarities of the generation of such complex structures, methods of working with them and storage. In this paper, we focus on the description of the algorithm for constructing classification trees for a large training set and show the way to the possibility of a uniform description of a fixed class of recognition trees. A simple, effective, economical method of constructing a logical classification tree of the training sample allows you to provide the necessary speed, the level of complexity of the recognition scheme, which guarantees a simple and complete recognition of discrete objects.
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Abspoel, Mark, Daniel Escudero, and Nikolaj Volgushev. "Secure training of decision trees with continuous attributes." Proceedings on Privacy Enhancing Technologies 2021, no. 1 (January 1, 2021): 167–87. http://dx.doi.org/10.2478/popets-2021-0010.
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AbstractWe apply multiparty computation (MPC) techniques to show, given a database that is secret-shared among multiple mutually distrustful parties, how the parties may obliviously construct a decision tree based on the secret data. We consider data with continuous attributes (i.e., coming from a large domain), and develop a secure version of a learning algorithm similar to the C4.5 or CART algorithms. Previous MPC-based work only focused on decision tree learning with discrete attributes (De Hoogh et al. 2014). Our starting point is to apply an existing generic MPC protocol to a standard decision tree learning algorithm, which we then optimize in several ways. We exploit the fact that even if we allow the data to have continuous values, which a priori might require fixed or floating point representations, the output of the tree learning algorithm only depends on the relative ordering of the data. By obliviously sorting the data we reduce the number of comparisons needed per node to O(N log2N) from the naive O(N2), where N is the number of training records in the dataset, thus making the algorithm feasible for larger datasets. This does however introduce a problem when duplicate values occur in the dataset, but we manage to overcome this problem with a relatively cheap subprotocol. We show a procedure to convert a sorting network into a permutation network of smaller complexity, resulting in a round complexity of O(log N) per layer in the tree. We implement our algorithm in the MP-SPDZ framework and benchmark our implementation for both passive and active three-party computation using arithmetic modulo 264. We apply our implementation to a large scale medical dataset of ≈ 290 000 rows using random forests, and thus demonstrate practical feasibility of using MPC for privacy-preserving machine learning based on decision trees for large datasets.
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McGee, Charles E. "Estimating Tree Ages in Uneven-Aged Hardwood Stands." Southern Journal of Applied Forestry 13, no. 1 (February 1, 1989): 40–42. http://dx.doi.org/10.1093/sjaf/13.1.40.
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Abstract Two groups of upland hard-wood trees from the same general area were studied by 44 volunteer observers to determine the ability of the observers to visually estimate tree ages. The error of estimate for tree ages varied by the professional category of the observer, and all categoriesof observer improved with training. The error of age estimate over all categories of observer was 40.2% on the age estimation for the first group of trees. On the second group of trees, after being supplied with the correct ages for trees in the first group, the volunteer observers reducedthe overall error to 22.1%. The best individual effort was achieved by a forester whose error on the first attempt was 16.5% with a 10.7% error on his second attempt. However, for most workers, on-the-ground training is essential for reasonable accuracy in age estimation. South. J. Appl. For.13(1):40-42.
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Choudhury, Rituparna, Shaik Rafi Ahamed, and Prithwijit Guha. "Training Accelerator for Two Means Decision Tree." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 29, no. 7 (July 2021): 1465–69. http://dx.doi.org/10.1109/tvlsi.2021.3076081.
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Hampson, Cheryl R., Harvey A. Quamme, and Robert T. Brownlee. "Canopy Growth, Yield, and Fruit Quality of 'Royal Gala' Apple Trees Grown for Eight Years in Five Tree Training Systems." HortScience 37, no. 4 (July 2002): 627–31. http://dx.doi.org/10.21273/hortsci.37.4.627.
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In 1993, a planting of virus-free 'Royal Gala' apple (Malu×domestica Borkh.) on 'M.9' rootstock was established at Summerland, B.C., Canada, to determine whether angled-canopy training systems could improve orchard tree performance relative to slender spindles. The trees were trained in one of five ways: slender spindle (SS), Geneva Y-trellis (GY), a modified Solen training we called 'Solen Y-trellis' (SY), or V-trellis (LDV), all at the same spacing (1.2 m × 2.8 m), giving a planting density of 2976 trees/ha. In addition, a higher density (7143 trees/ha) version of the V-trellis (HDV) was planted to gauge the performance of this system at densities approaching those of local super spindle orchards. The plots were drip-irrigated and hand-thinned. No summer pruning was done. After 8 years, differences among training systems at the same density and spacing were small and few. The two Y-shaped training systems had 11% to 14% greater cumulative yield/ha than the SS, but did not intercept significantly more light at maturity. No consistent differences occurred in fruit size or the percentage of fruit with delayed color development among the four training systems at the same density. Relative to the LDV, the HDV yielded less per tree, but far more per hectare, particularly in the first 3 years. After 8 years, the cumulative yield/ha was still 65% greater than with LDV. Yield efficiency was unaffected by tree density. Fruit size on HDV ranked lowest among the systems nearly every year, but was still commercially acceptable. The HDV intercepted more light (73%) than SS (53%). The percentage of fruit with delayed color development in HDV was not significantly different from that for LDV in most years. The trees in HDV were difficult to contain within their allotted space without summer pruning. The substantially similar performance of all the training systems (at a given density, and with minimal pruning) suggests that cost and ease of management should be the decisive factors when choosing a tree training method.
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Marini, Richard P., and Donald S. Sowers. "Peach Tree Growth, Yield, and Profitability as Influenced by Tree Form and Tree Density." HortScience 35, no. 5 (August 2000): 837–42. http://dx.doi.org/10.21273/hortsci.35.5.837.
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`Norman' peach [Prunus persica (L.) Batsch] trees were trained to the central-leader or open-vase form and were planted at high (740 trees/ha), or low (370 trees/ha) density. A third density treatment was a HIGH → LOW density, where alternate trees in high-density plots were removed after 6 years to produce a low-density treatment. From 3 to 5 years after planting, trunk cross-sectional areas (TCA) increased most for low-density trees. After 9 years, TCA was greatest for low-density and least for high-density trees. Because of differences in tree training, central-leader trees were taller than open-vase trees and tree spread was greater for low-density than for high-density trees. Annual yield per hectare was 15% to 40% greater for high-density treatments than for low-density treatments, but tree form had little influence on yield. Average fruit weight tended to be greater for low-density than for high-density treatments, but cumulative marketable yield was greatest for high-density and lowest for HIGH → LOW treatments. Income minus costs for 9 years was nearly $4200/ha higher, and net present value was about $2200/ha higher, for open-vase than for central-leader trees (P = 0.08). Cumulative net present value for the 9 years was about $2660/ha higher for high-than for low-density trees (P = 0.36).
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Applegate, Jason R., and Jim Steinman. "A Comparison of Tree Health Among Forest Types and Conditions at Fort A.P. Hill, Virginia." Southern Journal of Applied Forestry 29, no. 3 (August 1, 2005): 143–47. http://dx.doi.org/10.1093/sjaf/29.3.143.
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Abstract Fort A.P. Hill's Range and Training Land Assessments (RTLA) program initiated long-term monitoring of installation forests to assess forest health and ensure optimal sustainability of forest resources for military training activities. A subset of forest health indicators developed by the USDA Forest Service Forest Health Monitoring (FHM) and Forest Inventory and Analysis programs were used to assess forest health on Army training lands at Fort A.P. Hill, Virginia. Indicators of tree crown condition and tree damage condition were taken in forested areas where military training occurs, “tactical concealment areas (TCAs),” and on continuous forest monitoring (CFM) plots established in control stands where military training is absent. A higher percent of trees with high crown dieback, low crown density, and multiple types of stem damage were observed within TCAs than on CFM plots. The results are indicative of possible long-term changes to forest health from military training activities. The FHM forest health indicators proved to be an effective and useful approach to assess tree conditions. South. J. Appl. For. 29(3):143–147.
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Crassweller, R. M., and D. E. Smith. "Influence of Training Systems on Tree Size, Yield, and Fruit Quality of 15 Peach Cultivars." HortScience 31, no. 4 (August 1996): 666e—666. http://dx.doi.org/10.21273/hortsci.31.4.666e.
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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.
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Murphy, P. M., and M. J. Pazzani. "Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction." Journal of Artificial Intelligence Research 1 (March 1, 1994): 257–75. http://dx.doi.org/10.1613/jair.41.
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We report on a series of experiments in which all decision trees consistent with the training data are constructed. These experiments were run to gain an understanding of the properties of the set of consistent decision trees and the factors that affect the accuracy of individual trees. In particular, we investigated the relationship between the size of a decision tree consistent with some training data and the accuracy of the tree on test data. The experiments were performed on a massively parallel Maspar computer. The results of the experiments on several artificial and two real world problems indicate that, for many of the problems investigated, smaller consistent decision trees are on average less accurate than the average accuracy of slightly larger trees.
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Fricker, Geoffrey A., Jonathan D. Ventura, Jeffrey A. Wolf, Malcolm P. North, Frank W. Davis, and Janet Franklin. "A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery." Remote Sensing 11, no. 19 (October 6, 2019): 2326. http://dx.doi.org/10.3390/rs11192326.
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In this study, we automate tree species classification and mapping using field-based training data, high spatial resolution airborne hyperspectral imagery, and a convolutional neural network classifier (CNN). We tested our methods by identifying seven dominant trees species as well as dead standing trees in a mixed-conifer forest in the Southern Sierra Nevada Mountains, CA (USA) using training, validation, and testing datasets composed of spatially-explicit transects and plots sampled across a single strip of imaging spectroscopy. We also used a three-band ‘Red-Green-Blue’ pseudo true-color subset of the hyperspectral imagery strip to test the classification accuracy of a CNN model without the additional non-visible spectral data provided in the hyperspectral imagery. Our classifier is pixel-based rather than object based, although we use three-dimensional structural information from airborne Light Detection and Ranging (LiDAR) to identify trees (points > 5 m above the ground) and the classifier was applied to image pixels that were thus identified as tree crowns. By training a CNN classifier using field data and hyperspectral imagery, we were able to accurately identify tree species and predict their distribution, as well as the distribution of tree mortality, across the landscape. Using a window size of 15 pixels and eight hidden convolutional layers, a CNN model classified the correct species of 713 individual trees from hyperspectral imagery with an average F-score of 0.87 and F-scores ranging from 0.67–0.95 depending on species. The CNN classification model performance increased from a combined F-score of 0.64 for the Red-Green-Blue model to a combined F-score of 0.87 for the hyperspectral model. The hyperspectral CNN model captures the species composition changes across ~700 meters (1935 to 2630 m) of elevation from a lower-elevation mixed oak conifer forest to a higher-elevation fir-dominated coniferous forest. High resolution tree species maps can support forest ecosystem monitoring and management, and identifying dead trees aids landscape assessment of forest mortality resulting from drought, insects and pathogens. We publicly provide our code to apply deep learning classifiers to tree species identification from geospatial imagery and field training data.
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Dobashi, Nao, Shota Saito, Yuta Nakahara, and Toshiyasu Matsushima. "Meta-Tree Random Forest: Probabilistic Data-Generative Model and Bayes Optimal Prediction." Entropy 23, no. 6 (June 18, 2021): 768. http://dx.doi.org/10.3390/e23060768.
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This paper deals with a prediction problem of a new targeting variable corresponding to a new explanatory variable given a training dataset. To predict the targeting variable, we consider a model tree, which is used to represent a conditional probabilistic structure of a targeting variable given an explanatory variable, and discuss statistical optimality for prediction based on the Bayes decision theory. The optimal prediction based on the Bayes decision theory is given by weighting all the model trees in the model tree candidate set, where the model tree candidate set is a set of model trees in which the true model tree is assumed to be included. Because the number of all the model trees in the model tree candidate set increases exponentially according to the maximum depth of model trees, the computational complexity of weighting them increases exponentially according to the maximum depth of model trees. To solve this issue, we introduce a notion of meta-tree and propose an algorithm called MTRF (Meta-Tree Random Forest) by using multiple meta-trees. Theoretical and experimental analyses of the MTRF show the superiority of the MTRF to previous decision tree-based algorithms.
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Yosefian, Iman, Ehsan Mosa Farkhani, and Mohammad Reza Baneshi. "Application of Random Forest Survival Models to Increase Generalizability of Decision Trees: A Case Study in Acute Myocardial Infarction." Computational and Mathematical Methods in Medicine 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/576413.
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Background. Tree models provide easily interpretable prognostic tool, but instable results. Two approaches to enhance the generalizability of the results are pruning and random survival forest (RSF). The aim of this study is to assess the generalizability of saturated tree (ST), pruned tree (PT), and RSF.Methods. Data of 607 patients was randomly divided into training and test set applying 10-fold cross-validation. Using training sets, all three models were applied. Using Log-Rank test, ST was constructed by searching for optimal cutoffs. PT was selected plotting error rate versus minimum sample size in terminal nodes. In construction of RSF, 1000 bootstrap samples were drawn from the training set.C-index and integrated Brier score (IBS) statistic were used to compare models.Results. ST provides the most overoptimized statistics. Mean difference betweenC-index in training and test set was 0.237. Corresponding figure in PT and RSF was 0.054 and 0.007. In terms of IBS, the difference was 0.136 in ST, 0.021 in PT, and 0.0003 in RSF.Conclusion. Pruning of tree and assessment of its performance of a test set partially improve the generalizability of decision trees. RSF provides results that are highly generalizable.
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Ampatzidis, Yiannis G., and Matthew D. Whiting. "Training System Affects Sweet Cherry Harvest Efficiency." HortScience 48, no. 5 (May 2013): 547–55. http://dx.doi.org/10.21273/hortsci.48.5.547.
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Intuitively, tree architecture will affect harvest efficiency of tree fruit crops, yet there are no empirical studies that document this. The objective of the current research was to investigate the role of training system on harvest rate of individual pickers in commercial sweet cherry (Prunus avium L.) orchards. We used a real-time labor monitoring system (LMS) with the ability to track and record individual picker efficiency in 11 orchards throughout the Pacific Northwest. Trees were trained to one of five different architectures: 1) upright fruiting offshoots (UFO), a planar architecture comprised of unbranched vertical fruiting wood; 2) Y-trellised, an angled dual planar architecture; 3) Kym Green Bush (KGB), a multileader bush; 4) central leader (CL); and 5) traditional multileader open center (MLOC), trees comprised of three to five main leaders. A consistent picking crew was used to facilitate comparisons among systems and eliminate variability in skill among pickers. The LMS calculated harvest rate, picking cost, weight of harvested fruit, number of harvested buckets, range in fruit weight per bucket/bin, and mean fruit weight per bucket/bin for individual pickers. Tests revealed a significant effect of canopy architecture on labor efficiency. The highest mean (± se) harvest rates (0.94 ± 0.02 kg·min−1 and 0.78 ± 0.03 kg·min−1) were recorded in ‘Cowiche’/‘Gisela®5’ and ‘Tieton’/‘Gisela®5’ orchards trained to the UFO system, respectively. High harvest efficiency in these orchards was likely the result of the planar, simplified architecture and that most fruit were accessible from the ground. The third highest picking rate was recorded in the KGB system (0.73 ± 0.04 kg·min−1), a fully pedestrian orchard. Interestingly, harvest rate of slower pickers was improved to a greater extent (+132%) than skilled pickers (+83%) when comparing pedestrian and planar systems (e.g., UFO and KGB) with traditional architecture (MLOC). Furthermore, picking rate of individual pickers varied within 1 day by more than 100%, likely as a result of variability in fruit density within trees, tree size as well as fruit accessibility. We documented variability of more than 35 kg in final bin weight across all orchards and a range in bucket weight between ≈7 and 13 kg. These results suggest that architecture has a major effect on harvest efficiency and that current systems of piece-rate picker reimbursement are beset with inaccuracy.
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LAST, MARK, ODED MAIMON, and EINAT MINKOV. "IMPROVING STABILITY OF DECISION TREES." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 02 (March 2002): 145–59. http://dx.doi.org/10.1142/s0218001402001599.
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Decision-tree algorithms are known to be unstable: small variations in the training set can result in different trees and different predictions for the same validation examples. Both accuracy and stability can be improved by learning multiple models from bootstrap samples of training data, but the "meta-learner" approach makes the extracted knowledge hardly interpretable. In the following paper, we present the Info-Fuzzy Network (IFN), a novel information-theoretic method for building stable and comprehensible decision-tree models. The stability of the IFN algorithm is ensured by restricting the tree structure to using the same feature for all nodes of the same tree level and by the built-in statistical significance tests. The IFN method is shown empirically to produce more compact and stable models than the "meta-learner" techniques, while preserving a reasonable level of predictive accuracy.
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Weinstein, Ben G., Sergio Marconi, Stephanie Bohlman, Alina Zare, and Ethan White. "Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks." Remote Sensing 11, no. 11 (June 1, 2019): 1309. http://dx.doi.org/10.3390/rs11111309.
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Remote sensing can transform the speed, scale, and cost of biodiversity and forestry surveys. Data acquisition currently outpaces the ability to identify individual organisms in high resolution imagery. We outline an approach for identifying tree-crowns in RGB imagery while using a semi-supervised deep learning detection network. Individual crown delineation has been a long-standing challenge in remote sensing and available algorithms produce mixed results. We show that deep learning models can leverage existing Light Detection and Ranging (LIDAR)-based unsupervised delineation to generate trees that are used for training an initial RGB crown detection model. Despite limitations in the original unsupervised detection approach, this noisy training data may contain information from which the neural network can learn initial tree features. We then refine the initial model using a small number of higher-quality hand-annotated RGB images. We validate our proposed approach while using an open-canopy site in the National Ecological Observation Network. Our results show that a model using 434,551 self-generated trees with the addition of 2848 hand-annotated trees yields accurate predictions in natural landscapes. Using an intersection-over-union threshold of 0.5, the full model had an average tree crown recall of 0.69, with a precision of 0.61 for the visually-annotated data. The model had an average tree detection rate of 0.82 for the field collected stems. The addition of a small number of hand-annotated trees improved the performance over the initial self-supervised model. This semi-supervised deep learning approach demonstrates that remote sensing can overcome a lack of labeled training data by generating noisy data for initial training using unsupervised methods and retraining the resulting models with high quality labeled data.
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Triadiati, Triadiati, and Miftahudin Miftahudin. "Pemberdayaan Masyarakat pada Budi Daya dan Pengembangan Produk Pohon Gaharu (Aquilaria sp.) di Kabupaten Tolitoli, Sulawesi Tengah." Agrokreatif: Jurnal Ilmiah Pengabdian kepada Masyarakat 7, no. 2 (June 7, 2021): 174–84. http://dx.doi.org/10.29244/agrokreatif.7.2.174-184.
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Tolitoli District has a potential for agarwood trees in the forests, but it has not been optimally utilized and preserved by the local communities. Therefore, efforts are needed to increase understanding and community involvement in cultivation and product development of agarwood trees. The objectives of this activity are: to explore agarwood tree species in the forest and sources of local inoculums using a purposive sampling method for agarwood production, to assess the suitability of agro-climates for agarwood tree cultivation, to conduct face-to-face training and practice for agarwood tree breeding and bio-induction by injection, and to identify socio-economic conditions to support agarwood tree cultivation for community empowerment through product development and cultivation in Kabupaten Tolitoli. The project was implementated by exploring and identifying the existence of natural agarwood trees and agarwood farmers, training, and mentoring. The results of exploration and identification showed that Kabupaten Tolitoli has natural resources of agarwood trees in the forest and local inoculums for the bio-induction of agarwood formation. Also, the local community, including the local government, practitioners, farmers, and educational institutions, have great interest and desire for product development and cultivation of agarwood trees. Thus, it can be concluded that community empowerment through product development and cultivation of agarwood trees in Kabupaten Tolitoli can be implemented.
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ZANTEMA, HANS, and HANS L. BODLAENDER. "FINDING SMALL EQUIVALENT DECISION TREES IS HARD." International Journal of Foundations of Computer Science 11, no. 02 (June 2000): 343–54. http://dx.doi.org/10.1142/s0129054100000193.
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Two decision trees are called decision equivalent if they represent the same function, i.e., they yield the same result for every possible input. We prove that given a decision tree and a number, to decide if there is a decision equivalent decision tree of size at most that number is NP-complete. As a consequence, finding a decision tree of minimal size that is decision equivalent to a given decision tree is an NP-hard problem. This result differs from the well-known result of NP-hardness of finding a decision tree of minimal size that is consistent with a given training set. Instead our result is a basic result for decision trees, apart from the setting of inductive inference. On the other hand, this result differs from similar results for BDDs and OBDDs: since in decision trees no sharing is allowed, the notion of decision tree size is essentially different from BDD size.
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Campbell, R. J., and J. Wasielewski. "MANGO TREE TRAINING TECHNIQUES FOR THE HOT TROPICS." Acta Horticulturae, no. 509 (February 2000): 641–52. http://dx.doi.org/10.17660/actahortic.2000.509.73.
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Parton, Diana, Keith Huffman, Patty Pridgen, Kent Norman, and Ben Shneiderman. "Learning a menu selection tree: training methods compared." Behaviour & Information Technology 4, no. 2 (April 1985): 81–91. http://dx.doi.org/10.1080/01449298508901790.
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Boucher, Alexandre. "Considering complex training images with search tree partitioning." Computers & Geosciences 35, no. 6 (June 2009): 1151–58. http://dx.doi.org/10.1016/j.cageo.2008.03.011.
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Tanha, Jafar, Maarten van Someren, and Hamideh Afsarmanesh. "Semi-supervised self-training for decision tree classifiers." International Journal of Machine Learning and Cybernetics 8, no. 1 (January 24, 2015): 355–70. http://dx.doi.org/10.1007/s13042-015-0328-7.
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Caruso, T., P. Inglese, F. Sottile, and F. P. Marra. "Effect of Planting System on Productivity, Dry-matter Partitioning and Carbohydrate Content in Above-ground Components of `Flordaprince' Peach Trees." Journal of the American Society for Horticultural Science 124, no. 1 (January 1999): 39–45. http://dx.doi.org/10.21273/jashs.124.1.39.
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Vegetative growth, fruit yields, and dry matter partitioning within above-ground components were assessed during three growing seasons for trees of an early ripening peach (Prunus persica L. Batsch `Flordaprince' on GF 677 rootstock) trained either to a free standing central leader (930 trees/ha) or to Y shape (1850 trees/ha). Individual trees trained to central leader gave higher fruit yield, had a significantly greater leaf area and accumulated more dry mass in above-ground components per tree than Y shape trees. The training systems did not differ in terms of yield efficiency (yield per trunk cross-sectional area) and leaf area index (LAI), but Y shape trees had a higher harvest index and fruit dry mass per ground area than central leader. Four years after planting, Y shape had 35% higher yield per hectare than central leader. The relative contribution of 1-year-old wood, shoot and leaf to the dry mass of the tree decreased with tree age. Four years after planting the dry matter partitioned to the >1-year-old wood components represented 60% of the total tree mass (excluding fruit) in both the training systems. Central leader trees had the highest relative vegetative growth rate during stage III of fruit development. Most starch depletion occurred from dormancy to pit hardening from the canopy main storage pools (>1-year-old wood), and was higher for central leader than Y shape trees. For the ease of management and the high crop efficiency, the Y shape can be successfully used for peach high density planting systems.
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Chae, Deok-Jin, Ye-Ho Sin, Tae-Yeong Cheon, Heung-Seon Go, Geun-Ho Ryu, and Bu-Hyeon Hwang. "The Training Data Generation and a Technique of Phylogenetic Tree Generation using Decision Tree." KIPS Transactions:PartD 10D, no. 6 (January 1, 2003): 897–906. http://dx.doi.org/10.3745/kipstd.2003.10d.6.897.
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Edstrom, J. P., J. Connell, W. Krueger, W. Reil, J. Hasey, and J. Yeager. "600 Maintaining Yields In Hedgerow Almond Production." HortScience 35, no. 3 (June 2000): 500C—500. http://dx.doi.org/10.21273/hortsci.35.3.500c.
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Four tree training methods have been evaluated since 1979 in California for their affect on yield of “Nonpareil” ctv. almond [Prunus dulcis (Mill.) D.A.Webb] in a tightly spaced “Nonpareil”/”Price” ctvs 1:1 hedgerow planting. Four variations of open center training began at the first dormant pruning in a 2.2 × 6.7-m spacing (667 trees/ha): 1) Temporary Hedge—trees trained to three primary scaffolds, standard pruned with alternate trees gradually whisked back to allow space for permanent trees and then removed at 8th year leaving 4.4 × 6.7-m spacing(333 trees/ha); 2) Permanent Hedge—trees trained to three scaffolds, standard pruned at 2.2-m spacing; 3) Two-Scaffold Hedge—Trees trained into “perpendicular V” two scaffold configuration, standard pruned at 2.2-m spacing; 4) Unpruned Hedge—Trained to three scaffolds then left essentially unpruned at 2.2-m spacing. Replicated yield data accumulated over 15 years shows no difference in production between the three permanent 2.2-m hedgerow methods. Yield for the Temporary Hedge, however, declined 30% the year following alternate tree removal. Adequate canopy expansion resulted in some regained nut production, but yields never recovered and remain 20% below the permanent hedge treatments 13 years post-removal. Observations indicate considerable loss of fruitwood has occurred in the lower canopy of all three 2.2-m hedge treatments, especially in the Unpruned but good commercial production has been maintained at 2400 to 3000 kg/ha The size of almond kernels was not affected by training method. Trunk circumference was affected by treatment. Trees in Temporary Hedge plots grew sustantially larger after alternate tree removal than trees in all 2.2-m hedge treatments that were equal in size.
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Corelli-Grappadelli, Luca, Gianfranco Ravaglia, and Eugenio Magnanini. "160 Light Conversion Efficiency in Peach Training Systems." HortScience 35, no. 3 (June 2000): 417D—417. http://dx.doi.org/10.21273/hortsci.35.3.417d.
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Training system efficiency may be defined as the ratio of fruit produced to the amount of light intercepted by the canopy. In apple, a positive, linear relationship between yield and light intercepted is generally found, but in peach similar data are hard to come by. This paper reports data from an ongoing training systems trial now in the 7th year, with trees trained as Y, palmette, and delayed vase. During the life of the orchard, light interception has been measured for the different tree shapes, the yields have been recorded, and, in some years, whole-canopy gas exchanges of cropping trees have been measured. In general, the trees have been intercepting light in amounts proportional to canopy shape and tree density, with the Y (planted at higher density) intercepting more light than the other two systems, which appear more comparable to each other, despite the fact that they intercept light during the day in different ways, with the delayed vase exposing more or less the same leaves to incoming light during most of the day. Cropping has followed the amounts of light intercepted, with higher yields for the Y, without appreciable differences in fruit quality traits. The data accumulated so far indicate furthermore that the palmette and the delayed vase, despite slightly different light interception potentials (lower for the palmette), have similar yields. This might depend in part on the fact that these two systems intercept light according to different patterns during the day, with the palmette—which distributes the light intercepted in a more even fashion between the two sides—perhaps at an advantage over the vase in terms of managing the stress of excessive light (heat) loads during the central hours of the day. Whole canopy Carbon exchange data have been found to be in agreement with the patterns of light interception.
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RAHMANI, MOHSEN, SATTAR HASHEMI, ALI HAMZEH, and ASHKAN SAMI. "AGENT BASED DECISION TREE LEARNING: A NOVEL APPROACH." International Journal of Software Engineering and Knowledge Engineering 19, no. 07 (November 2009): 1015–22. http://dx.doi.org/10.1142/s0218194009004477.
Full textAbstract:
Decision trees are one of the most effective and widely used induction methods that have received a great deal of attention over the past twenty years. When decision tree induction algorithms were used with uncertain rather than deterministic data, the result is a complete tree, which can classify most of the unseen samples correctly. This tree would be pruned in order to reduce its classification error and over-fitting. Recently, multi agent researchers concentrated on learning from large databases. In this paper we present a novel multi agent learning method that is able to induce a decision tree from distributed training sets. Our method is based on combination of separate decision trees each provided by one agent. Hence an agent is provided to aggregate results of the other agents and induces the final tree. Our empirical results suggest that the proposed method can provide significant benefits to distributed data classification.
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Southwick, Stephen M., and James T. Yeager. "TREE TRAINING PROCEDURES FOR HIGH-DENSITY SWEET CHERRY PRODUCTION ON VIGOROUS ROOTSTOCKS." HortScience 25, no. 9 (September 1990): 1122d—1122. http://dx.doi.org/10.21273/hortsci.25.9.1122d.
Full textAbstract:
Sweet cherries produce vigorous upright growth from Apr.-Sept. and are slow to bear in California. Our tree training objectives include earlier bearing, easier harvesting, high productivity of good quality fruit. `Bing' cherry on mazzard and mahaleb rootstock were planted in 7 blocks and trained 6 ways. One group was headed 12-18 inches above the bud union and 4 branches were retained at the 1st dormant pruning. Lateral buds were treated with promalin at bud-break to induce lateral shoot formation. Trees were spring-summer pruned to reduce terminal growth. At the second dormant pruning, strong shoots were removed and lateral shoots were treated with promalin to induce spur formation. Trees were treated likewise through the 3rd dormant season and produced a fair crop in the 4th season. Central leader trees were created by tying/weighting limbs, dormant and summer pruning, and retaining less vigorous limbs as well as utilizing promalin. Slow growing trees tended to bear fruit more rapidly. Both training methods yielded fruit in the 4th season while traditional pruning procedures produced few fruit. Data and procedures will be presented to document these practices.