No abstract

We used video microscopy to study the behavior of aggregating suspensions in shear flow. Suspensions consisted of 920 nm diameter silica spheres, dispersed in a methanol/bromoform solvent, to which poly(ethylene glycol) (M = 35.000 g) was added to effect weak particle aggregation. With our solvent mixture, the refractive index of the particles could be closely matched, to allow microscopic observations up to 80 microm deep into the suspension. Also the mass density is nearly equal to that of the particles, thus allowing long observation times without problems due to aggregate sedimentation. Particles were visualized via fluorescent molecules incorporated into their cores. Using a fast confocal scanning laser microscope made it possible to characterize the (flowing) aggregates via their contour-area distributions as observed in the focal plane. The aggregation process was monitored from the initial state (just after adding the polymer), until a steady state was reached. The particle volume fraction was chosen at 0.001, to obtain a characteristic aggregation time of a few hundred seconds. On variation of polymer concentration, cP (2.2-12.0 g/L), and shear rate, gamma (3-6/s), it was observed that the volume-averaged size, Dv, in the steady state became larger with polymer concentration and smaller with shear rate. This demonstrates that the aggregate size is set by a competition between cohesive forces caused by the polymer and rupture forces caused by the flow. Also aggregate size distributions were be measured (semiquantitatively). Together with a description for the internal aggregate structure they allowed a modeling of the complete aggregation curve, from t = 0 up to the steady state. A satisfactory description could be obtained by describing the aggregates as fractal objects, with Df = 2.0, as measured from CSLM images after stopping the flow. In this modeling, the fitted characteristic breakup time was found to increase with cP.

No abstract

Accurate spatial information of agricultural fields in smallholder farms is important for providing actionable information to farmers, managers, and policymakers. Very High Resolution (VHR) satellite images can capture such information. However, the automated delineation of fields in smallholder farms is a challenging task because of their small size, irregular shape and the use of mixed-cropping systems, which make their boundaries vaguely defined. Physical edges between smallholder fields are often indistinct in satellite imagery and contours need to be identified by considering the transition of the complex textural pattern between fields. In these circumstances, standard edge-detection algorithms fail to extract accurate boundaries. This article introduces a strategy to detect field boundaries using a fully convolutional network in combination with a globalisation and grouping algorithm. The convolutional network using an encoder-decoder structure is capable of learning complex spatial-contextual features from the image and accurately detects sparse field contours. A hierarchical segmentation is derived from the contours using the oriented watershed transform and by iteratively merging adjacent regions based on the average strength of their common boundary. Finally, field segments are obtained by adopting a combinatorial grouping algorithm exploiting the information of the segmentation hierarchy. An extensive experimental analysis is performed in two study areas in Nigeria and Mali using WorldView-2/3 images and comparing several state-of-the-art contour detection algorithms. The algorithms are compared based on the precision-recall accuracy assessment strategy which is tolerating small localisation errors in the detected contours. The proposed strategy shows promising results by automatically delineating field boundaries with F-scores higher than 0.7 and 0.6 on our two test areas, respectively, outperforming alternative techniques.