An automatic bird identification system is required for offshore wind farms in Finland. Indubitably, a radar is the obvious choice to detect flying birds, but external information is required for actual identification. We applied visual camera images as external data. The proposed system for automatic bird identification consists of a radar, a motorized video head and a single-lens reflex camera with a telephoto lens. A convolutional neural network trained with a deep learning algorithm is applied to the image classification. We also propose a data augmentation method in which images are rotated and converted in accordance with the desired color temperatures. The final identification is based on a fusion of parameters provided by the radar and the predictions of the image classifier. The sensitivity of this proposed system, on a dataset containing 9312 manually taken original images resulting in 2.44 × 106 augmented data set, is 0.9463 as an image classifier. The area under receiver operating characteristic curve for two key bird species is 0.9993 (the White-tailed Eagle) and 0.9496 (The Lesser Black-backed Gull), respectively. We proposed a novel system for automatic bird identification as a real world application. We demonstrated that our data augmentation method is suitable for image classification problem and it significantly increases the performance of the classifier.

Practical deterrent methods are needed to prevent collisions between birds and wind turbine blades for offshore wind farms. It is improbable that a single deterrent method would work for all bird species in a given area. An automatic bird identification system is required in order to develop bird species-level deterrent methods. This system is the first and necessary part of the entirety that is eventually able to automatically monitor bird movements, identify bird species, and launch deterrent measures. A prototype system has been built on Finnish west coast. In the proposed system, a separate radar system detects birds and provides WGS84 coordinates to a steering system of a camera. The steering system consists of a motorized video head and our software to control it. The steering system tracks flying birds in order to capture series of images by a digital single-lens reflex camera. Classification is based on these images, and it is implemented by convolutional neural network trained with a deep learning algorithm.We applied to the images our data augmentation method in which images are rotated and converted into different color temperatures. The results indicate that the proposed system has good performance to identify bird species in the test area. Aiming accuracy for the video head was 88.91 %. Image classification performance as true positive rate was 0.8688. KEYWORDSconvolutional neural networks, deep learning, image classification, intelligent surveillance systems, machine learning, wind farms 1 Identification of bird species is mainly based on morphology and vocalization of which vocalization is not a feasible method in the offshore environment because of long ranges and background noise. Images taken of birds in the test area (the wind farm) are used as a feasible method to study morphology. This turns the identification problem to an image classification problem. Thus, the problem of automatic bird identification in real-time is two-fold: How to successfully aim the camera to a target bird in order to collect images and how to classify (identify) the images? Wind Energy. 2020;23:1394-1407. wileyonlinelibrary.com/journal/we

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Light quality response is a vital environmental cue regulating plant development. Conifers, like angiosperms, respond to the changes in light quality including the level of red (R) and far-red (FR) light, which follows a latitudinal cline. R and FR wavelengths form a significant component of the entire plant life cycle, including the initial developmental stages such as seed germination, cotyledon expansion and hypocotyl elongation. With an aim to identify differentially expressed candidate genes, which would provide a clue regarding genes involved in the local adaptive response in Scots pine (Pinus sylvestris) with reference to red/far-red light; we performed a global expression analysis of Scots pine hypocotyls grown under two light treatments, continuous R (cR) and continuous FR (cFR) light; using Pinus taeda cDNA microarrays on bulked hypocotyl tissues from different individuals, which represented different genotypes. This experiment was performed with the seeds collected from northern part of Sweden (Ylinen, 68˚N). Interestingly, gene expression pattern with reference to cryptochrome1, a blue light photoreceptor, was relatively high under cFR as compared to cR light treatment. Additionally, the microarray data analysis also revealed expression of 405 genes which was enhanced under cR light treatment; while the expression of 239 genes was enhanced under the cFR light treatment. Differentially expressed genes were re-annotated using Blast2GO tool. These results indicated that cR light acts as promoting factor whereas cFR antagonises the effect in most of the processes like C/N metabolism, photosynthesis and cell wall metabolism which is in accordance with former findings in Arabidopsis. We propose cryptochrome1 as a strong candidate gene to study the adaptive cline response under R and FR light in Scots pine as it shows a differential expression under the two light conditions.

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