disk plow


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Related to disk plow: disk harrow

disk plow

disk plow or disk, farm implement employing a row or rows of concave circular steel disks that cut and pitch the soil in a way somewhat similar to a moldboard plow. It can be used in many situations where the latter will not work, such as dry crusty soil, rough ground, or sticky gumbo. The one-way disk has essentially replaced other plows in areas where wind erosion of the soil is a problem as it can work the soil without turning it completely over, thus leaving the stubble on top for protection. This type of disk can also be fitted with attachments for seeding small grains.

Bibliography

See H. P. Smith and L. H. Wilkes, Farm Machinery and Equipment (6th ed. 1976); C. Culpin, Farm Machinery (11th ed. 1986).

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disk plow

[′disk ‚plau̇]
(agriculture)
A plow consisting of a number of disk blades attached to one axle or gang bolt; used for rapid, shallow plowing.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
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Key words: Artificial neural network, disk plow, draft, unit draft, energy requirement.
The disk plow has always been the basic tillage implement on the farm.
The factors affecting forces on disk plow include type of soil, the bearing of disk, scraper type, tilt angle, disk angle, forward speed, depth, width of cut, soil density and others as reported by many studies (Sommer et al., 1983; Godwin et al., 1987; Panigrahi et al., 1990; Bukhari et al., 1992; Morad, 1992; Shirin et al.,1993; Manian et al, 2000; Ismail, 2002; Abu-Hamdeh and Reeder, 2003; Mamkagh, 2009; Osman et al.,2011).
The literature studies have shown that the relationship between the dependent and independent variables affecting performance parameters of a disk plow was modeled by regression analysis as reported by Ismail (2002) or by dimensional analysis as reported by Olatunji (2011).
Therefore, the objective of this study was to develop, evaluate and validate a new ANN model to predict draft, unit draft and energy requirements of a disk plow. Several common and readily available factors were used as inputs to the ANN.
The purpose of the field experiment was to determine draft force for disk plow to acquire data for developing and testing the ANN model.
Disk plow (Nardi, mounted category II, weight 362 kg, Italy), model MF 38, serial No.
Raw data of the field tests during plowing with disk plow.
e) Disk plow performance modeling with ANN: In the present work, the ANN model with standard back- propagation algorithm was developed using a commercially available software Qnet 2000 (Vesta Services, 2000).
Using the weights obtained from the trained ANN model, new formulations were presented in Appendix (A) for the calculation of draft, unit draft and energy requirements of a disk plow. These formulations can be employed with any programming language or spreadsheet program (Ahmed et al., 2012), thus facilitating their use in routine analysis.
f) Determination of errors in disk plow performance: The accuracy of ANN predictions was evaluated using different error statistics as follows:
Conclusion: ANN model was developed for the prediction of the performance parameters (draft, unit draft and required energy) of the disk plow. The appropriate architecture of the neural network was 10-8-3.
ARTIFICIAL NEURAL NETWORK MODEL FOR PREDICTING DRAFT AND ENERGY REQUIREMENTS OF A DISK PLOW

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