The Kocac°ay Basin (KC°B) is a key area in western Anatolia^a well-known extended terrane where regional segmentation has received limited attention^for investigating strike-slip faults kinematically linked to detachment faults. In this paper, we present results of an integrated sedimentologic, stratigraphic, and structural study of Miocene alluvial fan/fan-delta/lacustrine deposits that accumulated in the KC°B, a NE-trending basin with connections to the Menderes Metamorphic Core Complex (MCC).We mapped and evaluated most of the key faults in the KC°B, many for the ¢rst time, and recognised di¡erent deformation events in the study area near the E margin of the MCC.We also present ¢eld evidence for kinematic connections between low-angle normal and strike-slip faults which were developed in an intermittently active basement-involved transfer zone in western Anatolia.We ¢nd that the KC°B contains a detailed record of Miocene transtensional sedimentation and volcanism that accompanied exhumation of the MCC. Structural data reveal that the basin was initially formed by transtension (D1 phase) and subsequently uplifted and deformed, probably as a result of early Pliocene wrench-to extension-dominated deformation (D2 phase) overprinted by Plio -Quaternary extensional tectonics (D3 phase).These results are consistent with progressive deformation wherein the axis of maximum extension remained in the horizontal plane but the intermediate and maximum shortening axes switched position in the vertical plane. Combining our results with published studies, we propose a new working hypothesis that the KC°B was a transtensional supradetachment basin during the Miocene.The hypothesis could provide new insights into intermittently active extension-parallel zone of weakness in western Anatolia.These results also suggest that the termination of low-angle normal fault systems within an extension parallel transfer zone may have resulted in a transtensional depressions which are di¡erent from classical supradetachment basins with respect to the sedimentation and deformational pattern of the basin in¢lls. Fig. 2. Simpli¢ed geologic map and cross section of NE-trending Cumaovası Basin and Kocac°ay Basin.The basins were separated by basement uplifts (e.g. the Nifdag ï ı High) during deposition of basin-¢ll units. MFZ, Mahmutdag ï ı Fault Zone; SFZ, Spildag ï ı Fault Zone; KF, KemalpaSa Fault; KTF, Kalkancatepe Fault; OFZ, Orhanlı Fault Zone.

The stratigraphy of the uppermost levels of the Menderes Massif is controversial and within its details lie vital constraints to the tectonic evolution of south-western Turkey. Our primary study was carried out in four reference areas along the southern and eastern Menderes Massif. These areas lie in the upper part of the Menderes metamorphic cover and have a clear stratigraphic relationship and contain datable fossils. The first one, in the Akbük±Milas area, is located south-east of Bafa Lake where the Milas, then Kõzõlag AE aç and Kazõklõ formations are well exposed. There, the Milas formation grades upwards into the Kõzõlag AE aç formation. The contact between the Kõzõla-g AE aç and the overlying Kazõklõ formation is not clearly seen but is interpreted as an unconformity. The Milas and Kõzõlag AE aç formations are also found north of Mug AE la, in the region of Yatag AE an and Kavaklõdere. In these areas, the Milas formation consists of schists and conformably overlying platform-type, emery and rudist-bearing marbles. Rudists form the main palaeontological data from which a Santonian±Campa-nian age is indicated. The Kõzõlag AE aç formation is characterized by reddish±greyish pelagic marbles with marly-pelitic interlayers and coarsening up debris flow deposits. Pelagic marbles within the formation contain planktonic foraminifera and nanoplankton of late Campanian to late Maastrichtian age. The Kazõklõ formation is of flysch type and includes carbonate blocks.

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The Upper Cretaceous Korkuteli (Antalya) carbonate sequence of the Bey Da¤lar› autochthonous unit (western Taurides) comprises two formations. The Cenomanian-Santonian Bey Da¤lar› formation lies at the base of this sequence and can be divided into two parts. Neritic part is characterized by platform-type, peritidal limestones and comprises an approximately 600-m-thick sequence that contains two main rudistid horizons corresponding to Cenomanian and early Santonian. The neritic limestones pass gradually upward into the 15-mthick, middle-upper Santonian massive hemipelagic limestones that form the upper part. The upper Campanianmiddle Maastrichtian Akda¤ formation consists totally of pelagic limestones that indicate basinal conditions and disconformably overlies different stratigraphic levels of the Bey Da¤lar› formation. Palaeogene pelagic marls form the base of the Tertiary sequence and disconformably overlie different stratigraphic levels of the Upper Cretaceous sequence. The presence of two erosional phases in the Upper Cretaceous sequence is obvious. The autochthonous unit was subaerially exposed after post-Santonian and middle Maastrichtian regressions.

Knowing before harvesting how many plants have emerged and how they are growing is key in optimizing labour and efficient use of resources. Unmanned aerial vehicles (UAV) are a useful tool for fast and cost efficient data acquisition. However, imagery need to be converted into operational spatial products that can be further used by crop producers to have insight in the spatial distribution of the number of plants in the field. In this research, an automated method for counting plants from very high-resolution UAV imagery is addressed. The proposed method uses machine vision—Excess Green Index and Otsu’s method—and transfer learning using convolutional neural networks to identify and count plants. The integrated methods have been implemented to count 10 weeks old spinach plants in an experimental field with a surface area of 3.2 ha. Validation data of plant counts were available for 1/8 of the surface area. The results showed that the proposed methodology can count plants with an accuracy of 95% for a spatial resolution of 8 mm/pixel in an area up to 172 m2. Moreover, when the spatial resolution decreases with 50%, the maximum additional counting error achieved is 0.7%. Finally, a total amount of 170 000 plants in an area of 3.5 ha with an error of 42.5% was computed. The study shows that it is feasible to count individual plants using UAV-based off-the-shelf products and that via machine vision/learning algorithms it is possible to translate image data in non-expert practical information.