Detection of Cattle Using Drones and Convolutional Neural Networks

[EN] Multirotor drones have been one of the most important technological advances of the last decade. Their mechanics are simple compared to other types of drones and their possibilities in flight are greater. For example, they can take-off vertically. Their capabilities have therefore brought progress to many professional activities. Moreover, advances in computing and telecommunications have also broadened the range of activities in which drones may be used. Currently, artificial intelligence and information analysis are the main areas of research in the field of computing. The case study presented in this article employed artificial intelligence techniques in the analysis of information captured by drones. More specifically, the camera installed in the drone took images which were later analyzed using Convolutional Neural Networks (CNNs) to identify the objects captured in the images. In this research, a CNN was trained to detect cattle, however the same training process could be followed to develop a CNN for the detection of any other object. This article describes the design of the platform for real-time analysis of information and its performance in the detection of cattle

Machine Learning for Informed Representation Learning

The way we view reality and reason about the processes surrounding us is intimately connected to our perception and the representations we form about our observations and experiences. The popularity of machine learning and deep learning techniques in that regard stems from their ability to form useful representations by learning from large sets of observations. Typical application examples include image recognition or language processing for which artificial neural networks are powerful tools to extract regularity patterns or relevant statistics. In this thesis, we leverage and further develop this representation learning capability to address relevant but challenging real-world problems in geoscience and chemistry, to learn representations in an informed manner relevant to the task at hand, and reason about representation learning in neural networks, in general. Firstly, we develop an approach for efficient and scalable semantic segmentation of degraded soil in alpine grasslands in remotely-sensed images based on convolutional neural networks. To this end, we consider different grassland erosion phenomena in several Swiss valleys. We find that we are able to monitor soil degradation consistent with state-of-the-art methods in geoscience and can improve detection of affected areas. Furthermore, our approach provides a scalable method for large-scale analysis which is infeasible with established methods. Secondly, we address the question of how to identify suitable latent representations to enable generation of novel objects with selected properties. For this, we introduce a new deep generative model in the context of manifold learning and disentanglement. Our model improves targeted generation of novel objects by making use of property cycle consistency in property-relevant and property-invariant latent subspaces. We demonstrate the improvements on the generation of molecules with desired physical or chemical properties. Furthermore, we show that our model facilitates interpretability and exploration of the latent representation. Thirdly, in the context of recent advances in deep learning theory and the neural tangent kernel, we empirically investigate the learning of feature representations in standard convolutional neural networks and corresponding random feature models given by the linearisation of the neural networks. We find that performance differences between standard and linearised networks generally increase with the difficulty of the task but decrease with the considered width or over-parametrisation of these networks. Our results indicate interesting implications for feature learning and random feature models as well as the generalisation performance of highly over-parametrised neural networks. In summary, we employ and study feature learning in neural networks and review how we may use informed representation learning for challenging tasks

Auto-Encoder Learning-Based UAV Communications for Livestock Management

The advancement in computing and telecommunication has broadened the applications of drones beyond military surveillance to other fields, such as agriculture. Livestock farming using unmanned aerial vehicle (UAV) systems requires surveillance and monitoring of animals on relatively large farmland. A reliable communication system between UAVs and the ground control station (GCS) is necessary to achieve this. This paper describes learning-based communication strategies and techniques that enable interaction and data exchange between UAVs and a GCS. We propose a deep auto-encoder UAV design framework for end-to-end communications. Simulation results show that the auto-encoder learns joint transmitter (UAV) and receiver (GCS) mapping functions for various communication strategies, such as QPSK, 8PSK, 16PSK and 16QAM, without prior knowledge

Computer vision for plant and animal inventory

The population, composition, and spatial distribution of the plants and animals in certain regions are always important data for natural resource management, conservation and farming. The traditional ways to acquire such data require human participation. The procedure of data processing by human is usually cumbersome, expensive and time-consuming. Hence the algorithms for automatic animal and plant inventory show their worth and become a hot topic. We propose a series of computer vision methods for automated plant and animal inventory, to recognize, localize, categorize, track and count different objects of interest, including vegetation, trees, fishes and livestock animals. We make use of different sensors, hardware platforms, neural network architectures and pipelines to deal with the varied properties and challenges of these objects. (1) For vegetation analysis, we propose a fast multistage method to estimate the coverage. The reference board is localized based on its edge and texture features. And then a K-means color model of the board is generated. Finally, the vegetation is segmented at pixel level using the color model. The proposed method is robust to lighting condition changes. (2) For tree counting in aerial images, we propose a novel method called density transformer, or DENT, to learn and predict the density of the trees at different positions. DENT uses an efficient multi-receptive field network to extract visual features from different positions. A transformer encoder is applied to filter and transfer useful contextual information across different spatial positions. DENT significantly outperformed the existing state-of-art CNN detectors and regressors on both the dataset built by ourselves and an existing cross-site dataset. (3) We propose a framework of fish classification system using boat cameras. The framework contains two branches. A branch extracts the contextual information from the whole image. The other branch localizes all the individual fish and normalizes their poses. The classification results from the two branches are weighted based on the clearness of the image and the familiarness of the context. Our system achieved the top 1 percent rank in the competition of The Nature Conservancy Fisheries Monitoring. (4) We also propose a video-based pig counting algorithm using an inspection robot. We adopt a novel bottom-up keypoint tracking method and a novel spatial-aware temporal response filtering method to count the pigs. The proposed approach outperformed the other methods and even human competitors in the experiments.Includes bibliographical references

Object detection for single tree species identification with high resolution aerial images

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesObject recognition is one of the computer vision tasks developing rapidly with the invention of Region-based Convolutional Neural Network (RCNN). This thesis contains a study conducted using RCNN base object detection technique to identify palm trees in three datasets having RGB images taken by Unnamed Aerial Vehicles (UAVs). The method was entirely implemented using TensorFlow object detection API to compare the performance of pre-trained faster RCNN object detection models. According to the results, best performance was recorded with the highest overall accuracy of 93.1 ± 4.5 % and the highest speed of 9m 57s from faster RCNN model which was having inceptionv2 as feature extractor. The poorest performance was recorded with the lowest overall accuracy of 65.2 ± 10.9% and the lowest speed of 5h 39m 15s from faster RCNN model which was having inception_resnetv2 as feature extractor

The Application of Advanced Technologies for Agriculture and Rangeland Management

This project demonstrates two applications of remote sensing in agricultural and rangeland environments. In the first, an unmanned aerial system (UAS) equipped with a multi-spectral sensor was used to estimate canopy cover across four different cover crop trials at four time periods. In the second, a local database of stationary camera trap images of wildlife was used to train a convolutional neural network to automatically catalogue images by identifying the animal in those images. Both projects aimed to provide an example of how remote sensing platforms and machine learning techniques can facilitate the rapid collection and processing of large-scale field data. In both projects, methods were developed that confirm the utility of advanced remote sensing and computer vision technologies

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

This work was supported by the projects: "VIRTUOUS" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2019. Ref. 872181, "SUSTAINABLE" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2020. Ref. 101007702 and the "Project of Excellence" from Junta de Andalucia 2020. Ref. P18-H0-4700. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.European Commission 101007702 872181Junta de Andalucia P18-H0-470