Mobile Real-Time Grasshopper Detection and Data Aggregation Framework

nsects of the family Orthoptera: Acrididae including grasshoppers and locust devastate crops and eco-systems around the globe. The effective control of these insects requires large numbers of trained extension agents who try to spot concentrations of the insects on the ground so that they can be destroyed before they take flight. This is a challenging and difficult task. No automatic detection system is yet available to increase scouting productivity, data scale and fidelity. Here we demonstrate MAESTRO, a novel grasshopper detection framework that deploys deep learning within RBG images to detect insects. MAeStRo uses a state-of-the-art two-stage training deep learning approach. the framework can be deployed not only on desktop computers but also on edge devices without internet connection such as smartphones. MAeStRo can gather data using cloud storage for further research and in-depth analysis. In addition, we provide a challenging new open dataset (GHCID) of highly variable grasshopper populations imaged in inner Mongolia. the detection performance of the stationary method and the mobile App are 78 and 49 percent respectively; the stationary method requires around 1000 ms to analyze a single image, whereas the mobile app uses only around 400 ms per image. The algorithms are purely data-driven and can be used for other detection tasks in agriculture (e.g. plant disease detection) and beyond. This system can play a crucial role in the collection and analysis of data to enable more effective control of this critical global pest

Neural Circuit Dynamics and Ensemble Coding in the Locust and Fruit Fly Olfactory System

Raw sensory information is usually processed and reformatted by an organism’s brain to carry out tasks like identification, discrimination, tracking and storage. The work presented in this dissertation focuses on the processing strategies of neural circuits in the early olfactory system in two insects, the locust and the fruit fly. Projection neurons (PNs) in the antennal lobe (AL) respond to an odor presented to the locust’s antennae by firing in slow information-carrying temporal patterns, consistent across trials. Their downstream targets, the Kenyon cells (KCs) of the mushroom body (MB), receive input from large ensembles of transiently synchronous PNs at a time. The information arrives in slices of time corresponding to cycles of oscillatory activity originating in the AL. In the first part of the thesis, ensemble-level analysis techniques are used to understand how the AL-MB system deals with the problem of identifying odors across different concentrations. Individual PN odor responses can vary dramatically with concentration, but invariant patterns in PN ensemble responses are shown to allow odor identity to be extracted across a wide range of intensities by the KCs. Second, the sensitivity of the early olfactory system to stimulus history is examined. The PN ensemble and the KCs are found capable of tracking an odor in most conditions where it is pulsed or overlapping with another, but they occasionally fail (are masked) or reach intermediate states distinct from those seen for the odors presented alone or in a static mixture. The last part of the thesis focuses on the development of new recording techniques in the fruit fly, an organism with well-studied genetics and behavior. Genetically expressed fluorescent sensors of calcium offer the best available option to study ensemble activity in the fly. Here, simultaneous electrophysiology and two-photon imaging are used to estimate the correlation between G-CaMP, a popular genetically expressible calcium sensor, and electrical activity in PNs. The sensor is found to have poor temporal resolution and to miss significant spiking activity. More generally, this combination of electrophysiology and imaging enables explorations of functional connectivity and calibrated imaging of ensemble activity in the fruit fly.</p

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects

The ornamental crop industry is an important contributor to the economy in the United States. The industry has been facing challenges due to continuously increasing labor and agricultural input costs. Sensing and automation technologies have been introduced to reduce labor requirements and to ensure efficient management operations. This article reviews current sensing and automation technologies used for ornamental nursery crop production and highlights prospective technologies that can be applied for future applications. Applications of sensors, computer vision, artificial intelligence (AI), machine learning (ML), Internet-of-Things (IoT), and robotic technologies are reviewed. Some advanced technologies, including 3D cameras, enhanced deep learning models, edge computing, radio-frequency identification (RFID), and integrated robotics used for other cropping systems, are also discussed as potential prospects. This review concludes that advanced sensing, AI and robotic technologies are critically needed for the nursery crop industry. Adapting these current and future innovative technologies will benefit growers working towards sustainable ornamental nursery crop production

Information processing in neural systems: oscillations, network topologies and optimal representations

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid. Escuela Politécnica Superior, Departamento de Ingeniería informática. Fecha de lectura: 1-07-200

Investigation of visual pathways in honeybees (Apis mellifera) and desert locusts (Schistocerca gregaria): anatomical, ultrastructural, and physiological approaches

Many insect species demonstrate sophisticated abilities regarding spatial orientation and navigation, despite their small brain size. The behaviors that are based on spatial orientation differ dramatically between individual insect species according to their lifestyle and habitat. Central place foragers like bees and ants, for example, orient themselves in their surrounding and navigate back to the nest after foraging for food or water. Insects like some locust and butterfly species, on the other hand, use spatial orientation during migratory phases to keep a stable heading into a certain direction over a long period of time. In both scenarios, homing and long-distance migration, vision is the primary source for orientation cues even though additional features like wind direction, the earth’s magnetic field, and olfactory cues can be taken into account as well. Visual cues that are used for orientational purposes range from landmarks and the panorama to celestial cues. The latter consists in diurnal insects of the position of the sun itself, the sun-based polarization pattern and intensity and spectral gradient, and is summarized as sky-compass system. For a reliable sky-compass orientation, the animal needs, in addition to the perception of celestial cues, to compensate for the daily movement of the sun across the sky. It is likely that a connection from the circadian pacemaker system to the sky-compass network could provide the necessary circuitry for this time compensation. The present thesis focuses on the sky-compass system of honeybees and locusts. There is a large body of work on the navigational abilities of honeybees from a behavioral perspective but the underlying neuronal anatomy and physiology has received less attention so far. Therefore, the first two chapters of this thesis reveals a large part of the anatomy of the anterior sky-compass pathway in the bee brain. To this end, dye injections, immunohistochemical stainings, and ultrastructural examinations were conducted. The third chapter describes a novel methodical protocol for physiological investigations of neurons involved in the sky-compass system using calcium imaging in behaving animals. The fourth chapter of this thesis deals with the anatomical basis of time compensation in the sky-compass system of locusts. Therefore, the ultrastructure of synaptic connections in a brain region of the desert locust where the contact of both systems could be feasible has been investigated

Short-term memory and olfactory signal processing

Modern neural recording methodologies, including multi-electrode and optical recordings, allow us to monitor the large population of neurons with high temporal resolution. Such recordings provide rich datasets that are expected to understand better how information about the external world is internally represented and how these representations are altered over time. Achieving this goal requires the development of novel pattern recognition methods and/or the application of existing statistical methods in novel ways to gain insights into basic neural computational principles. In this dissertation, I will take this data-driven approach to dissect the role of short-term memory in olfactory signal processing in two relatively simple models of the olfactory system: fruit fly (Drosophila melanogaster) and locust (Schistocerca americana). First, I will focus on understanding how odor representations within a single stimulus exposure are refined across different populations of neurons (faster dynamics; on the order seconds) in the early olfactory circuits. Using light-sheet imaging datasets from transgenic flies expressing calcium indicators in select populations of neurons, I will reveal how odor representations are decorrelated over time in different neural populations. Further, I will examine how this computation is altered by short-term memory in this neural circuitry. Next, I will examine how neural representations for odorants at an ensemble level are altered across different exposures (slower dynamics; on the order of tens of seconds to minutes). I will examine the role of this short-term adaptation in altering neural representations for odor identity and intensity. Lastly, I will present approaches to help achieve robustness against both extrinsic and intrinsic perturbations of odor-evoked neural responses. I will conclude with a Boolean neural network inspired by the insect olfactory system and compare its performance against other state-of-the-art methods on standard machine learning benchmark datasets. In sum, this work will provide deeper insights into how short-term plasticity alters sensory neural representations and their computational significance

A cricket Gene Index: a genomic resource for studying neurobiology, speciation, and molecular evolution

<p>Abstract</p> <p>Background</p> <p>As the developmental costs of genomic tools decline, genomic approaches to non-model systems are becoming more feasible. Many of these systems may lack advanced genetic tools but are extremely valuable models in other biological fields. Here we report the development of expressed sequence tags (EST's) in an orthopteroid insect, a model for the study of neurobiology, speciation, and evolution.</p> <p>Results</p> <p>We report the sequencing of 14,502 EST's from clones derived from a nerve cord cDNA library, and the subsequent construction of a Gene Index from these sequences, from the Hawaiian trigonidiine cricket <it>Laupala kohalensis</it>. The Gene Index contains 8607 unique sequences comprised of 2575 tentative consensus (TC) sequences and 6032 singletons. For each of the unique sequences, an attempt was made to assign a provisional annotation and to categorize its function using a Gene Ontology-based classification through a sequence-based comparison to known proteins. In addition, a set of unique 70 base pair oligomers that can be used for DNA microarrays was developed. All Gene Index information is posted at the DFCI Gene Indices web page</p> <p>Conclusion</p> <p>Orthopterans are models used to understand the neurophysiological basis of complex motor patterns such as flight and stridulation. The sequences presented in the cricket Gene Index will provide neurophysiologists with many genetic tools that have been largely absent in this field. The cricket Gene Index is one of only two gene indices to be developed in an evolutionary model system. Species within the genus <it>Laupala </it>have speciated recently, rapidly, and extensively. Therefore, the genes identified in the cricket Gene Index can be used to study the genomics of speciation. Furthermore, this gene index represents a significant EST resources for basal insects. As such, this resource is a valuable comparative tool for the understanding of invertebrate molecular evolution. The sequences presented here will provide much needed genomic resources for three distinct but overlapping fields of inquiry: neurobiology, speciation, and molecular evolution.</p

Shape Representation in Primate Visual Area 4 and Inferotemporal Cortex

The representation of contour shape is an essential component of object recognition, but the cortical mechanisms underlying it are incompletely understood, leaving it a fundamental open question in neuroscience. Such an understanding would be useful theoretically as well as in developing computer vision and Brain-Computer Interface applications. We ask two fundamental questions: “How is contour shape represented in cortex and how can neural models and computer vision algorithms more closely approximate this?” We begin by analyzing the statistics of contour curvature variation and develop a measure of salience based upon the arc length over which it remains within a constrained range. We create a population of V4-like cells – responsive to a particular local contour conformation located at a specific position on an object’s boundary – and demonstrate high recognition accuracies classifying handwritten digits in the MNIST database and objects in the MPEG-7 Shape Silhouette database. We compare the performance of the cells to the “shape-context” representation (Belongie et al., 2002) and achieve roughly comparable recognition accuracies using a small test set. We analyze the relative contributions of various feature sensitivities to recognition accuracy and robustness to noise. Local curvature appears to be the most informative for shape recognition. We create a population of IT-like cells, which integrate specific information about the 2-D boundary shapes of multiple contour fragments, and evaluate its performance on a set of real images as a function of the V4 cell inputs. We determine the sub-population of cells that are most effective at identifying a particular category. We classify based upon cell population response and obtain very good results. We use the Morris-Lecar neuronal model to more realistically illustrate the previously explored shape representation pathway in V4 – IT. We demonstrate recognition using spatiotemporal patterns within a winnerless competition network with FitzHugh-Nagumo model neurons. Finally, we use the Izhikevich neuronal model to produce an enhanced response in IT, correlated with recognition, via gamma synchronization in V4. Our results support the hypothesis that the response properties of V4 and IT cells, as well as our computer models of them, function as robust shape descriptors in the object recognition process