Prediction of biometric variables through multispectral images obtained from UAV in beans (Phaseolus vulgaris L.) during ripening stage

Here, we report the prediction of vegetative stages variables of canary bean crop by means of RGB and multispectral images obtained from UAV during the ripening stage, correlating the vegetation indices with biometric variables measured manually in the field. Results indicated a highly significant correlation of plant height with eight RGB image vegetation indices for the canary bean crop, which were used for predictive models, obtaining a maximum correlation of R2 = 0.79. On the other hand, the estimated indices of multispectral images did not show significant correlations

Environmentally relevant concentration of caffeine-effect on activity and circadian rhythm in wild perch

We studied the ecological consequences of widespread caffeine contamination by conducting an experiment focused on changes in the behavioral traits of wild perch (Perca fluviatilis) after waterborne exposure to 10 mu g L-1 of caffeine. We monitored fish swimming performance during both light and dark conditions to study the effect of caffeine on fish activity and circadian rhythm, using a novel three-dimensional tracking system that enabled positioning even in complete darkness. All individuals underwent three behavioral trials-before exposure, after 24 h of exposure, and after 5 days of exposure. We did not observe any effect of the given caffeine concentration on fish activity under light or dark conditions. Regardless of caffeine exposure, fish swimming performance was significantly affected by both the light-dark conditions and repeating of behavioral trials. Individuals in both treatments swam significantly more during the light condition and their activity increased with time as follows: before exposure < after 24 h of exposure < after 5 days of exposure. We confirmed that the three-dimensional automated tracking system based on infrared sensors was highly effective for conducting behavioral experiments under completely dark conditions

High-speed surface reconstruction of a flying bird using structured light

Birds fly effectively and maneuver nimbly by dynamically changing the shape of their wings during each wingbeat. These shape changes have yet to be quantified automatically at high temporal and spatial resolution. Therefore, we developed a custom 3D surface reconstruction method, which uses a high-speed camera to identify spatially encoded binary striped patterns that are projected on a flying bird. This non-invasive structured-light method allows automated 3D reconstruction of each stand-alone frame and can be extended to multiple views. We demonstrate this new technique by automatically reconstructing the dorsal surface of a parrotlet wing at 3200 fps during flapping flight. From this shape we analyze key parameters such as wing twist and angle of attack distribution. While our binary ‘single-shot’ algorithm is demonstrated by quantifying dynamic shape changes of a flying bird, it is generally applicable to moving animals, plants and deforming objects.Organismic and Evolutionary Biolog

Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals

Human activity recognition and neural activity analysis are the basis for human computational neureoethology research dealing with the simultaneous analysis of behavioral ethogram descriptions and neural activity measurements. Wireless electroencephalography (EEG) and wireless inertial measurement units (IMU) allow the realization of experimental data recording with improved ecological validity where the subjects can be carrying out natural activities while data recording is minimally invasive. Specifically, we aim to show that EEG and IMU data fusion allows improved human activity recognition in a natural setting. We have defined an experimental protocol composed of natural sitting, standing and walking activities, and we have recruited subjects in two sites: in-house (N = 4) and out-house (N = 12) populations with different demographics. Experimental protocol data capture was carried out with validated commercial systems. Classifier model training and validation were carried out with scikit-learn open source machine learning python package. EEG features consist of the amplitude of the standard EEG frequency bands. Inertial features were the instantaneous position of the body tracked points after a moving average smoothing to remove noise. We carry out three validation processes: a 10-fold cross-validation process per experimental protocol repetition, (b) the inference of the ethograms, and (c) the transfer learning from each experimental protocol repetition to the remaining repetitions. The in-house accuracy results were lower and much more variable than the out-house sessions results. In general, random forest was the best performing classifier model. Best cross-validation results, ethogram accuracy, and transfer learning were achieved from the fusion of EEG and IMUs data. Transfer learning behaved poorly compared to classification on the same protocol repetition, but it has accuracy still greater than 0.75 on average for the out-house data sessions. Transfer leaning accuracy among repetitions of the same subject was above 0.88 on average. Ethogram prediction accuracy was above 0.96 on average. Therefore, we conclude that wireless EEG and IMUs allow for the definition of natural experimental designs with high ecological validity toward human computational neuroethology research. The fusion of both EEG and IMUs signals improves activity and ethogram recognitionThis work has been partially supported by FEDER funds through MINECO Project TIN2017-85827-P. Special thanks to Naiara Vidal from IMH who conducted the recruitment process in the framework of Langileok project funded by the Elkartek program. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 777720

A review on the application of computer vision and machine learning in the tea industry

Tea is rich in polyphenols, vitamins, and protein, which is good for health and tastes great. As a result, tea is very popular and has become the second most popular beverage in the world after water. For this reason, it is essential to improve the yield and quality of tea. In this paper, we review the application of computer vision and machine learning in the tea industry in the last decade, covering three crucial stages: cultivation, harvesting, and processing of tea. We found that many advanced artificial intelligence algorithms and sensor technologies have been used in tea, resulting in some vision-based tea harvesting equipment and disease detection methods. However, these applications focus on the identification of tea buds, the detection of several common diseases, and the classification of tea products. Clearly, the current applications have limitations and are insufficient for the intelligent and sustainable development of the tea field. The current fruitful developments in technologies related to UAVs, vision navigation, soft robotics, and sensors have the potential to provide new opportunities for vision-based tea harvesting machines, intelligent tea garden management, and multimodal-based tea processing monitoring. Therefore, research and development combining computer vision and machine learning is undoubtedly a future trend in the tea industry

Applications of deep learning in fish habitat monitoring: A tutorial and survey

Marine ecosystems and their fish habitats are becoming increasingly important due to their integral role in providing a valuable food source and conservation outcomes. Due to their remote and difficult to access nature, marine environments and fish habitats are often monitored using underwater cameras to record videos and images for understanding fish life and ecology, as well as for preserve the environment. There are currently many permanent underwater camera systems deployed at different places around the globe. In addition, there exists numerous studies that use temporary cameras to survey fish habitats. These cameras generate a massive volume of digital data, which cannot be efficiently analysed by current manual processing methods, which involve a human observer. Deep Learning (DL) is a cutting-edge Artificial Intelligence (AI) technology that has demonstrated unprecedented performance in analysing visual data. Despite its application to a myriad of domains, its use in underwater fish habitat monitoring remains under explored. In this paper, we provide a tutorial that covers the key concepts of DL, which help the reader grasp a high-level understanding of how DL works. The tutorial also explains a step-by-step procedure on how DL algorithms should be developed for challenging applications such as underwater fish monitoring. In addition, we provide a comprehensive survey of key deep learning techniques for fish habitat monitoring including classification, counting, localisation, and segmentation. Furthermore, we survey publicly available underwater fish datasets, and compare various DL techniques in the underwater fish monitoring domains. We also discuss some challenges and opportunities in the emerging field of deep learning for fish habitat processing. This paper is written to serve as a tutorial for marine scientists who would like to grasp a high-level understanding of DL, develop it for their applications by following our step-by-step tutorial, and see how it is evolving to facilitate their research efforts. At the same time, it is suitable for computer scientists who would like to survey state-of-the-art DL-based methodologies for fish habitat monitoring

A review on the use of computer vision and artificial intelligence for fish recognition, monitoring, and management.

Abstract: Computer vision has been applied to fish recognition for at least three decades. With the inception of deep learning techniques in the early 2010s, the use of digital images grew strongly, and this trend is likely to continue. As the number of articles published grows, it becomes harder to keep track of the current state of the art and to determine the best course of action for new studies. In this context, this article characterizes the current state of the art by identifying the main studies on the subject and briefly describing their approach. In contrast with most previous reviews related to technology applied to fish recognition, monitoring, and management, rather than providing a detailed overview of the techniques being proposed, this work focuses heavily on the main challenges and research gaps that still remain. Emphasis is given to prevalent weaknesses that prevent more widespread use of this type of technology in practical operations under real-world conditions. Some possible solutions and potential directions for future research are suggested, as an effort to bring the techniques developed in the academy closer to meeting the requirements found in practice

ABS-FishCount: An Agent-Based Simulator of Underwater Sensors for Measuring the Amount of Fish

[EN] Underwater sensors provide one of the possibilities to explore oceans, seas, rivers, fish farms and dams, which all together cover most of our planet's area. Simulators can be helpful to test and discover some possible strategies before implementing these in real underwater sensors. This speeds up the development of research theories so that these can be implemented later. In this context, the current work presents an agent-based simulator for defining and testing strategies for measuring the amount of fish by means of underwater sensors. The current approach is illustrated with the definition and assessment of two strategies for measuring fish. One of these two corresponds to a simple control mechanism, while the other is an experimental strategy and includes an implicit coordination mechanism. The experimental strategy showed a statistically significant improvement over the control one in the reduction of errors with a large Cohen's d effect size of 2.55.This work acknowledges the research project Desarrollo Colaborativo de Soluciones AAL with reference TIN2014-57028-R funded by the Spanish Ministry of Economy and Competitiveness. This work has been supported by the program Estancias de movilidad en el extranjero José Castillejo para jóvenes doctores funded by the Spanish Ministry of Education, Culture and Sport with reference CAS17/00005. We also acknowledge support from Universidad de Zaragoza , Fundación Bancaria Ibercaja and Fundación CAI in the Programa Ibercaja-CAI de Estancias de Investigación with reference IT24/16. We acknowledge the research project Construcción de un framework para agilizar el desarrollo de aplicaciones móviles en el ámbito de la salud funded by University of Zaragoza and Foundation Ibercaja with grant reference JIUZ-2017-TEC-03. It has also been supported by Organismo Autónomo Programas Educativos Europeos with reference 2013-1-CZ1-GRU06-14277. We also aknowledge support from project Sensores vestibles y tecnología móvil como apoyo en la formación y práctica de mindfulness: prototipo previo aplicado a bienestar funded by University of Zaragoza with grant number UZ2017-TEC-02. Furthermore, we acknowledge the Fondo Social Europeo and the Departamento de Tecnología y Universidad del Gobierno de Aragón for their joint support with grant number Ref-T81.García-Magariño, I.; Lacuesta Gilabert, R.; Lloret, J. (2017). ABS-FishCount: An Agent-Based Simulator of Underwater Sensors for Measuring the Amount of Fish. Sensors. 17(11):1-19. https://doi.org/10.3390/s17112606S1191711Lloret, J. (2013). Underwater Sensor Nodes and Networks. Sensors, 13(9), 11782-11796. doi:10.3390/s130911782Akyildiz, I. F., Pompili, D., & Melodia, T. (2005). Underwater acoustic sensor networks: research challenges. 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水産養殖における給餌支援を目的とした養殖魚活動量推定センサネットワーク

With the expansion of aquaculture production to meet the growing demand for food fish worldwide, there is an increasing need for its sustainable management not only to mitigate any threat to the aquatic environment but also to produce more high-quality fish that meet the market standards for seafood. Digital transformation (DX) holds an important role in achieving this need, enabling fish farmers make better decisions in using their resources as well as in reducing their costs of production through knowledge transfer and data. One such decision-making where DX can assist is in feeding, which generally has the largest share in production costs. Conventionally, farmers control the feeding from judging the fishes’ behavior. They learn this practice through their subjective experiences, leading to substantial differences in results between expert and novice farmers. The latter tends to feed the fishes inefficiently, producing uneaten feeds, which do not only increase financial burden in culture operations but also contribute to the pollution of the aquatic environment, which affect the growth and quality of the fish stocks and ultimately the sustainability of their operations. Applying DX to estimate the fish behavior therefore becomes important. While several intelligent feeding control methods using various technologies have been developed for applying such DX, many of are either easily affected by changes or noise from external sources or are technically difficult to implement in larger scales. An alternative approach is by measurement of outward flow from the cage, which has been observed to be fish induced. If we assume that fishes tend to swim upward when they sense feeds coming from the surface and swim back down when satiated, and that they tend to move in circles, fish activity at different depths can be visualized with this measurement to help farmers make feeding decisions. An off-grid modular sensor network was thus designed and developed to collect flow speed measurements and underwater video recordings from at least two depths and from multiple sides of a fish cage. This was realized by organizing sensors into sensor modules, which are connected to each other and are organized into sensor units. The sensor units were designed wirelessly relay data from all modules to a hub unit. Flow sensors were modified to measure flow speeds underwater. The network’s operation was also designed to be scheduled to manage its offshore power supply to enable long-term observation by the system. To demonstrate its functionality, the sensor network was deployed in fish cages and collected data, especially during feeding. The flow measurements and underwater videos were analyzed together to estimate the fish activity. Although there were various patterns, it could be observed that surface flow increased significantly at the beginning of feeding and declined toward the end. Vigorous surface activity was observed at most cages, validating the observed flow speeds. Offset between speeds at opposite sides was also observed, suggesting cancellation of global currents. In some experiments, increase of flow below the surface was also observed at the beginning and towards the end of feeding, indicating fishes to climbing and descending. There are many factors that contribute to the speed of flow coming out of the cage. However, the fishes’ locomotion and depth distribution have a large contribution to the changes in flow speed. These parameters depend on their hunger level and on the availability of feeds in water, as fishes may tend to swim up fast when they sense feed in water and swim less vigorously when they start to become full. A simplified model of the fish activity as a response to feeding was developed for simulation. Its output could be compared with the collected flow data for the farmers to use in improving their feeding decisions. Some observed flow patterns such as the decline of surface flow and the increase of flow at lower depths could be used for deciding on when to stop feeding. With these insights, a DX system was envisioned to collect flow speed and other measurements from multiple fish cages, assisting fish farmers in feeding. This research contributes to the development of DX application in cage aquaculture by introducing a flexible self-correcting system that could help farmers visualize underwater fish activity to help them improve their feeding decisions.九州工業大学博士学位論文 学位記番号：生工博甲第447号 学位授与年月日：令和4年9月26日1. Introduction|2. Flow speed sensor network|3. Fish activity estimation|4. Discussion on feeding decision|5. Conclusion九州工業大学令和4年