Effect of row orientation on soil water content and vine water status on a Cabernet franc vineyard in Madrid, spain

The aim of this study was to evaluate the effects of row orien¬tation on vine and soil water status in an irrigated vineyard. The trial was developed during 2006, 2007 and 2008, in the South East region of Madrid (Spain) on 5-year old Cabernet franc grapevines (Vitis vinifera L.) grafted onto 140Ru. Plant spacing was 2.5 m x 1.5 m and vines were trained to a VSP. Four orientations were stu¬died: North-South (N-S), East-West (E-W), Northeast-Southwest (N+45) and North-South +20o (N+20). Irrigation (0.4•ET0) started when shoot growth stopped. Soil water availability was measured using a TDR technique with forty buried probes. Row orientation did not have any effect on water consumption in the vineyard. At maturity, leaf water potential was measured at predawn, early mor¬ning, midday and 14:00 solar time, on both canopy sides - sun and shade – ; the early morning measurement was the one that better differentiated treatments. Leaf water potential was a good indica¬tor of plant water status. Differences between (N-S and E-W) and (N+20 and N+45) treatments were obtained both on sun and shade canopy sides, N+20 and N+45 having lower leaf water potentials then drier leaves. The water stress integral shows that N-S and E-W reach the end of maturation with a greater level of hydration than N+45 and N+20. As a whole, N+45 and N+20 orientations, without affecting too much the soil available water content, induce regularly more water stress to the vine at some periods, probably due to an higher sunlight interception in early morning which makes water limitation for the vine more early and thus more severe during the day.

Mapping wide row crops with video sequences acquired from a tractor moving at treatment speed

This paper presents a mapping method for wide row crop fields. The resulting map shows the crop rows and weeds present in the inter-row spacing. Because field videos are acquired with a camera mounted on top of an agricultural vehicle, a method for image sequence stabilization was needed and consequently designed and developed. The proposed stabilization method uses the centers of some crop rows in the image sequence as features to be tracked, which compensates for the lateral movement (sway) of the camera and leaves the pitch unchanged. A region of interest is selected using the tracked features, and an inverse perspective technique transforms the selected region into a bird’s-eye view that is centered on the image and that enables map generation. The algorithm developed has been tested on several video sequences of different fields recorded at different times and under different lighting conditions, with good initial results. Indeed, lateral displacements of up to 66% of the inter-row spacing were suppressed through the stabilization process, and crop rows in the resulting maps appear straight

Crop row detection in maize fields inspired on the human visual perception

This paper proposes a new method, oriented to image real-time processing, for identifying crop rows in maize fields in the images. The vision system is designed to be installed onboard a mobile agricultural vehicle, that is, submitted to gyros, vibrations, and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of two main processes: image segmentation and crop row detection. The first one applies a threshold to separate green plants or pixels (crops and weeds) from the rest (soil, stones, and others). It is based on a fuzzy clustering process, which allows obtaining the threshold to be applied during the normal operation process. The crop row detection applies a method based on image perspective projection that searches for maximum accumulation of segmented green pixels along straight alignments. They determine the expected crop lines in the images. The method is robust enough to work under the above-mentioned undesired effects. It is favorably compared against the well-tested Hough transformation for line detection.

Direct and Inverse Results on Row Sequences of Hermite-Pad, Approximants

We give necessary and sufficient conditions for the convergence with geometric rate of the common denominators of simultaneous rational interpolants with a bounded number of poles. The conditions are expressed in terms of intrinsic properties of the system of functions used to build the approximants. Exact rates of convergence for these denominators and the simultaneous rational approximants are provided.

Automatic expert system based on images for accuracy crop row detection in maize fields

This paper proposes an automatic expert system for accuracy crop row detection in maize fields based on images acquired from a vision system. Different applications in maize, particularly those based on site specific treatments, require the identification of the crop rows. The vision system is designed with a defined geometry and installed onboard a mobile agricultural vehicle, i.e. submitted to vibrations, gyros or uncontrolled movements. Crop rows can be estimated by applying geometrical parameters under image perspective projection. Because of the above undesired effects, most often, the estimation results inaccurate as compared to the real crop rows. The proposed expert system exploits the human knowledge which is mapped into two modules based on image processing techniques. The first one is intended for separating green plants (crops and weeds) from the rest (soil, stones and others). The second one is based on the system geometry where the expected crop lines are mapped onto the image and then a correction is applied through the well-tested and robust Theil–Sen estimator in order to adjust them to the real ones. Its performance is favorably compared against the classical Pearson product–moment correlation coefficient.