K-independent percolation on trees

Consider the class of k-independent bond, respectively site, percolations with parameter p on an infinite tree T. We derive tight bounds on p for both a.s. percolation and a.s. nonpercolation. The bounds are continuous functions of k and the branching number of T. This extends previous results by Lyons for the independent case (k=0) and by Bollob\`as & Balister for 1-independent bond percolations. Central to our argumentation are moment method bounds \`a la Lyons supplemented by explicit percolation models \`a la Bollob\`as & Balister. An indispensable tool is the minimality and explicit construction of Shearer's measure on the k-fuzz of Z.Comment: 28 pages, 4 figure

PRECISION AGRICULTURE, WHOLE FIELD FARMING AND IRRIGATION PRACTICES: A PRODUCTION RISK ANALYSIS

One of the potential management practices of precision agriculture (PA) is the capability of varying input application rate across a field. A potential benefit of that practice is the reduction in yield variability. Temporal reduction in yield variability can also be achieved through irrigation practices. Combining both practices should lead to a reduction of the yield risk faced by the farmer. In this study, variable rate application of nutrients will include to nitrogen, potassium and phosphate. Mathematical programming techniques will be used in a standard E-V framework to analyze the ability of PA and/or irrigation to reduce production risk.Risk and Uncertainty,

Underground Farm Petroleum Storage Tanks

Protection of farmstead water supplies is a topic which should be a prime concern of every rural landowner. Contamination of underground and surface water supplies by pesticides, fertilizers and petroleum products is a real and present hazard of modern agricultural operations. Contaminated soil and water sources can result in immediate, obvious losses such as unhealthy livestock and the need to develop alternative water sources, and long term losses such as reduced land values

Multi-Robot System Control Architecture (MRSCA) for Agricultural Production

Coordinating multiple autonomous robots for achieving an assigned collective task presents a complex engineering challenge. In this paper multi robot system control architecture (MRSCA) for the coordination of multiple agricultural robots is developed. The two important aspects of MRSCA; coordination strategy and inter-robot communication were discussed with typical agricultural tasks as examples. Classification of MRS into homogeneous and heterogeneous robots was done to identify appropriate form of cooperative behavior and inter-robot communication. The framework developed, proposes that inter-robot communication is not always required for a MRS. Three types of cooperative behaviors; No-cooperation, modest cooperation and absolute cooperation for a MRS were devised for accomplishing a variety of coordinated operations in agricultural productio

Cut Crop Edge Detection Using a Laser Sensor

An off-the-shelf low cost laser sensor was tested and evaluated both in laboratory and field conditions. The sensor identified the angular and straight edges of the laboratory test surface and replicated the straight edge profile with an error of 4%. In field conditions, the sensor identified three types of cut crop edges (wheat, alfalfa and corn) and replicated distinct shapes (triangle, curved and rectangular edges). The sensor was tested at two sensor path offset distances and three tractor/sensor speeds (3.2, 6.4 and 9.6 km/h). In all test runs the sensor detected the cut-crop edges. Standard deviations and RMSE values in determining the actual cut-crop edges for the entire field test were within 210 cm and 13 cm respectively. The sensor performed the best in the case of wheat cut-crop edge where the RMSE was 4.2 cm (sensor path offset = 1m, speed 3.2 km/h) and performed the worst in the case of alfalfa cut-crop edge where the RMSE was 16.7 cm (sensor path offset = .30 m and speed 9.6 km/h)

Guidance Directrix Generation Using Laser Sensors

A sensor array consisting of two laser sensors was utilized to determine the guidance directrix (offset distance-d, heading angle-ø) that are required as reference inputs for an automated guidance system. The sensor array was evaluated in both laboratory and field conditions. Under laboratory conditions the sensor array replicated the physical profile of the target surface with a 4% error in determining the heading angle. Field tests were conducted in two types of crops; corn and alfalfa. The sensor array identified the cut-crop edge profile ahead of the tractor and replicated distinct shapes of the cut-crop edge. RMSE values in determining the offset distances and heading angles of the cut-crop edge in corn were within 5.5 cm and 4.39°. In the case of alfalfa cut-crop edge the RMSE values were within 6.6 cm and 4.32°

Guidance Directrix Generation Using Laser Sensors

A sensor array consisting of two laser sensors was utilized to determine the guidance directrix (offset distance-d, heading angle-ø) that are required as reference inputs for an automated guidance system. The sensor array was evaluated in both laboratory and field conditions. Under laboratory conditions the sensor array replicated the physical profile of the target surface with a 4% error in determining the heading angle. Field tests were conducted in two types of crops; corn and alfalfa. The sensor array identified the cut-crop edge profile ahead of the tractor and replicated distinct shapes of the cut-crop edge. RMSE values in determining the offset distances and heading angles of the cut-crop edge in corn were within 5.5 cm and 4.390. In the case of alfalfa cut-crop edge the RMSE values were within 6.6 cm and 4.320

Sensor Ranging Technique for Determining Corn Plant Population

Mapping of corn plant population can provide useful information for making field management decisions. This research focused on using low cost infra-red sensors to count plants. The voltage output data from the sensors were processed using an algorithm developed to extract plant populations. Preliminary investigations were conducted using sensors mounted on a stationary track for laboratory testing and on a row crop tractor for field testing. Repeated measurements were taken on a manually counted corn row. Visual inspection of the data from the field test indicated the potential to identify corn stalks based on approximate physical widths of the stalks. Corn plant populations tended to be overestimated for all eight field trials, with errors ranging from +0.7% to +4.4%. Overestimation was most likely due to leaves or other objects detected by the sensors during the field trials wrongly identified as corn stalks

Automatic Guidance System Development Using Low Cost Ranging Devices

Autonomous guidance of agricultural vehicles for various field operations serves to increase productivity. Low cost infra-red ranging devices were used in this study to estimate the guidance directrix (offset distance (d) and heading error angle (Ø)). Two kinds of tracks were used for evaluating the performance of the infra-red sensors, one track was made of a cardboard in the laboratory and the other track was made of wheat crop in the field. The cardboard track consisted of straight parallel and non parallel edges whereas the wheat crop track had a series of different edges including few curves. From the results it was concluded that the sensor consistently detected the profile of the tracks with good repeatability .Errors of 6% were observed in detecting the straight edges and errors 6.6% and 13% were obtained in detecting two different angled surfaces. The reasons for the errors were detected and explained briefly in the paper

Automatic Guidance System Development Using Low Cost Ranging Devices

Autonomous guidance of agricultural vehicles for various field operations serves to increase productivity. Low cost infra-red ranging devices were used in this study to estimate the guidance directrix (offset distance (d) and heading error angle (Ø)). Two kinds of tracks were used for evaluating the performance of the infra-red sensors, one track was made of a cardboard in the laboratory and the other track was made of wheat crop in the field. The cardboard track consisted of straight parallel and non parallel edges whereas the wheat crop track had a series of different edges including few curves. From the results it was concluded that the sensor consistently detected the profile of the tracks with good repeatability .Errors of 6% were observed in detecting the straight edges and errors 6.6% and 13% were obtained in detecting two different angled surfaces. The reasons for the errors were detected and explained briefly in the paper