L*a*b*Fruits: A Rapid and Robust Outdoor Fruit Detection System Combining Bio-Inspired Features with One-Stage Deep Learning Networks
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Automation of agricultural processes requires systems that can accurately detect and classify produce in real industrial environments that include variation in fruit appearance due to illumination, occlusion, seasons, weather conditions, etc. In this paper we combine a visual processing approach inspired by colour-opponent theory in humans with recent advancements in one-stage deep learning networks to accurately, rapidly and robustly detect ripe soft fruits (strawberries) in real industrial settings and using standard (RGB) camera input. The resultant system was tested on an existent data-set captured in controlled conditions as well our new real-world data-set captured on a real strawberry farm over two months. We utilise F 1 score, the harmonic mean of precision and recall, to show our system matches the state-of-the-art detection accuracy ( F 1 : 0.793 vs. 0.799) in controlled conditions; has greater generalisation and robustness to variation of spatial parameters (camera viewpoint) in the real-world data-set ( F 1 : 0.744); and at a fraction of the computational cost allowing classification at almost 30fps. We propose that the L*a*b*Fruits system addresses some of the most pressing limitations of current fruit detection systems and is well-suited to application in areas such as yield forecasting and harvesting. Beyond the target application in agriculture this work also provides a proof-of-principle whereby increased performance is achieved through analysis of the domain data, capturing features at the input level rather than simply increasing model complexity.Keywords:
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RGB color model
Precision Agriculture
Network robustness is an essential system property to sustain functionality in the face of failures or targeted attacks. Currently, only the connectivity of the nodes unaffected by an attack is utilized to assess robustness. We propose to incorporate the properties of the emerging connectivity of the nodes affected by the attack (Idle Network), which is demonstrated to contain pertinent information about network robustness, improving its assessment accuracy. The Idle network information offers the potential to generalize models, enabling them to estimate robustness for unseen attacks.
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The precision agriculture technological system has been developed into three branches, precision farming, precision raising and precision processing. They should be developed first from DSS(Decision Support System) and achievements obtained in scientific research, and then they can be used in practice step by step. Then stretch to the two ends of precision farming system. Finally,to complete the integration of GPS、GIS and RSS and the realization of intelligent control farm machine system(ICS)until finish up all system.According to the foreign experience, the development of greenhouse precision farming and feeding may as well earlier on field precision farming.Because,“easy first hard second”it can get twice the result with half of the efforts(table 3).
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For the purpose of enhancing the robustness of model-based controllers against modelling uncertainties and external disturbances, a simple robustness enhancer is proposed in this paper. By using the given robustness enhancer, compact analytical results of uncertainty reduction and robustness enhancement can be obtained. It is shown that with such a scheme, both modelling uncertainties and disturbances can be reduced, and hence the robustness of the control system can be enhanced by a given factor through the proper design of relevant components of the robustness enhancer.< >
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A reality faced in the practical application of signal detection is the inexact statistical knowledge of the underlying random processes. Accordingly, it is often desirable for a detector to possess robustness. In this paper, we review how the concept of manifold slope can be employed to admit the measurement of robustness thus allowing the degree of robustness to be a factor in the design of the signal detector. We then present new results that show how certain nonstandard decision regions can result in what we term 'negative boundaries' which have the potential to enhance robustness. An example of this approach is provided and the results compared to the classical Huber approach for robust detection.
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Summary form only given. This paper provides an overview of the rapidly developing field of precision fanning. The main precision farming applications commercially available today, including spatial soil testing and variable rate application of fertilizers, variable rate spraying, and yield monitoring, are first introduced. The key enabling technologies - GPS, microprocessor-based control systems, electronic sensors, GIS, and decision support systems - are then discussed, followed by an outline of the precision farming system of the future. A few pointers to more information on precision farming are also provided.
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Order acceptance decisions in Engineer-To-Order (ETO) environments are often based on incomplete or uncertain information about the order specifications and the status of the production system. To quote reliable due dates and manage the production system adequately, resource loading techniques that account for uncertainty are essential. They are useful as support tools for order acceptance and thus profitable ETO production. In this paper we propose two multi-objective optimization models for Robust Resource Loading (RRL). The first model is a multi-objective MILP model with implicitly modeled precedence relations wich we solve using a branch-and-price approach. In the second approach we use a resource loading formulation with explicitly modeled precedence relations. The models generate robust plans by including robustness in the objective function. We introduce two indicators to measure robustness: resource plan robustness and activity plan robustness. Resource plan robustness measures robustness from a resource managers viewpoint. Activity plan robustness measures robustness from a customers viewpoint. Computational experiments with the models show that accounting for robustness in the objective function improves the characteristics of a plan significantly with respect to dealing with uncertainty. Furthermore, the model with explicit precedence constraints outperforms the implicit approach.
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The work deals with researching robustness of information system for measuring of microcontrollers average power consumption. Defined the ways of ensuring robustness of the proposed method. Considered hardware for robustness of the proposed method and proposed methods of study of the robustness of the proposed method.
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Network robustness is an essential system property to sustain functionality in the face of failures or targeted attacks. Currently, only the connectivity of the nodes unaffected by an attack is utilized to assess robustness. We propose to incorporate the properties of the emerging connectivity of the nodes affected by the attack (Idle Network), which is demonstrated to contain pertinent information about network robustness, improving its assessment accuracy. The Idle network information offers the potential to generalize models, enabling them to estimate robustness for unseen attacks.
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This paper presents a novel hexarotor unmanned aerial vehicle (UAV) with robustness against an arbitrary rotor-failure, called full robustness, and a design method to maximize its manipulability while ensuring the full robustness. First, the dynamical model of a hexarotor UAV and the novel structure with 2Y shape and twisted angles are presented. A hexarotor with this structure is named as 2Y hexarotor. The 2Y hexarotor has higher flight efficiency than other existing hexarotor structures with full robustness. Second, the full robustness of the 2Y hexarotor is proved, and a quantitative measure to evaluate the full robustness is introduced. Then, the quantitative measure for the full robustness is used to calculate the optimal twisted angles. Finally, the dynamic manipulability measure (DMM) is introduced to evaluate the maneuverability. A design method is defined as the maximization of the DMM under constraints regarding the quantitative measure for the full robustness and the condition to avoid overlapping rotors. The design method is applied to the 2Y hexarotor with the optimal twisted angles.
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