Hitching horses to get the most work done

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Improving of the wear resistance of working parts agricultural machinery by the implementation of the effect of self-sharpening

The failure of the cutting elements of farm machinery is due to the blunting of cutting edges (increase of their radius) till the limit values. The most effective method for increasing the wear resistance of farm machinery is the realization of self-sharpening effect of the cutting elements. The testings took place in laboratory and field at the State Technical University of Kirovograd (Ukraine) in 2015. The technical equipment consists of the consolidated farmer plowshares by different methods as well as their samples, devicesfor measuring the wear resistance and thumbprint plowshares. It was determined the resistance to wear, the radius of curvature and the changing coefficient of the blades shape. The self-sharpening process was examined throughout the experiment.The results showed that the consolidated plowshares by the proposed technology (laser welding of the mixture (PS-14-60 + 6% В4С) compared to the traditional technology (volumetric heat treatment) have a blade radius 2.5 times lower, a wear 2.2 to 2.78 times lower and the self-sharpening process of the plowshares has been observed since the beginning of the wear until the time limit operation. The changing shape coefficient was respectively of 0.98 for the consolidated plowshares with alloy PS-14-60 + 6% B4C and 0.82 for those consolidated by volumetric heat treatment

Tillage Research in Ohio A Guide to the Selection of Profitable Tillage Systems

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CQESTR Simulation of Management Practice Effects on Long-Term Soil Organic Carbon

Management of soil organic matter (SOM) is important for soil productivity and responsible utilization of crop residues for additional uses. CQESTR, pronounced “sequester,” a contraction of “C sequestration” (meaning C storage), is a C balance model that relates organic residue additions, crop management, and soil tillage to SOM accretion or loss. Our objective was to simulate SOM changes in agricultural soils under a range of climate and management systems using the CQESTR model. Four long-term experiments (Champaign, IL, \u3e100 yr; Columbia, MO, \u3e100 yr; Lincoln, NE, 20 yr; Sidney, NE, 20 yr) in the United States under various crop rotations, tillage practices, organic amendments, and crop residue removal treatments were selected for their documented history of the long-term effects of management practice on SOM dynamics. CQESTR successfully simulated a substantial decline in SOM with 50 yr of crop residue removal under various rotations at Columbia and Champaign. The increase in SOM following addition of manure was simulated well; however, the model underestimated SOM for a fertilized treatment at Columbia. Predicted and observed values from the four sites were signifi cantly related (r2 = 0.94, n = 113, P \u3c 0.001), with slope not signifi cantly different from 1. Given the high correlation of simulated and observed SOM changes, CQESTR can be used as a reliable tool to predict SOM changes from management practices and offers the potential for estimating soil C storage required for C credits. It can also be an important tool to estimate the impacts of crop residue removal for bioenergy production on SOM level and soil production capacity

Weed Control for Reduced Tillage Systems

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TILLAGE SYSTEMS FOR MINNESOTA FARMS: ALTERNATIVE TECHNOLOGIES, ECONOMIC CONSIDERATIONS, AND DATA NEEDS

Crop Production/Industries,

Unsupervised Learning with Self-Organizing Spiking Neural Networks

We present a system comprising a hybridization of self-organized map (SOM) properties with spiking neural networks (SNNs) that retain many of the features of SOMs. Networks are trained in an unsupervised manner to learn a self-organized lattice of filters via excitatory-inhibitory interactions among populations of neurons. We develop and test various inhibition strategies, such as growing with inter-neuron distance and two distinct levels of inhibition. The quality of the unsupervised learning algorithm is evaluated using examples with known labels. Several biologically-inspired classification tools are proposed and compared, including population-level confidence rating, and n-grams using spike motif algorithm. Using the optimal choice of parameters, our approach produces improvements over state-of-art spiking neural networks

From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration

In this paper, we propose a novel approach to the rank minimization problem, termed rank residual constraint (RRC) model. Different from existing low-rank based approaches, such as the well-known nuclear norm minimization (NNM) and the weighted nuclear norm minimization (WNNM), which estimate the underlying low-rank matrix directly from the corrupted observations, we progressively approximate the underlying low-rank matrix via minimizing the rank residual. Through integrating the image nonlocal self-similarity (NSS) prior with the proposed RRC model, we apply it to image restoration tasks, including image denoising and image compression artifacts reduction. Towards this end, we first obtain a good reference of the original image groups by using the image NSS prior, and then the rank residual of the image groups between this reference and the degraded image is minimized to achieve a better estimate to the desired image. In this manner, both the reference and the estimated image are updated gradually and jointly in each iteration. Based on the group-based sparse representation model, we further provide a theoretical analysis on the feasibility of the proposed RRC model. Experimental results demonstrate that the proposed RRC model outperforms many state-of-the-art schemes in both the objective and perceptual quality