An artificial neural network approach to identify fungal diseases of cucumber (Cucumis sativus L.) plants using digital image processing

Nowadays, artificial intelligence solutions such as digital image processing and artificial neural networks (ANN) have become important applicable techniques in phytomonitoring and plant health detection systems. In this research, an autonomous device was designed and developed for detecting two types of fungi (Pseudoperonospora cubensis, Sphaerotheca fuliginea) that infect the cucumber (Cucumis sativus L.) plant leaves. This device was able to recognise the fungal diseases of plants by detecting their symptoms on plant leaves (downy mildew and powdery mildew). For leaves of cucumber inoculated with different spores of the fungi, it was possible to estimate the amount of hour post inoculation (HPI) by extracting leaves’ image parameters. Device included a dark chamber, a CCD digital camera, a thermal camera, a light dependent resistor lightening module and a personal computer. The proposed programme for precise disease detection was based on an image processing algorithm and ANN. Three textural features and two thermal parameters from the obtained images were measured and normalised. Performance of ANN model was tested successfully for disease recognition and detecting HPI in images using back-propagation supervised learning method and inspection data. Such this machine vision system can be used in robotic intelligent systems to achieve a modern farmer’s assistant in agricultural crop fields.     

Performance evaluation of a machine vision system for insect pests identification of field crops using artificial neural networks

Feature extraction is a crucial part of advanced image recognition systems. In this research, an autonomous detection device was designed and developed for insect pest detection to improve the ability of intelligent systems in order to annihilate harmful insect pests in agricultural crop fields. Device included a dark chamber, a CCD digital camera, a LDR lightening module and a personal computer. The proposed programme for precise insect pest detection was based on an image processing algorithm and artificial neural networks (ANNs). After image acquisition, the insect pests’ images were extracted from original images with Canny filtration. Afterwards, four morphological and three textural features from the obtained images were measured and normalised. Performance of ANN model was tested successfully for Beet armyworm (Spodoptera exigua) recognition in images using back-propagation supervised learning method and inspection data. Results showed that proposed system was able to identify S. exigua in the images from other species. Such this machine vision system can be used in autonomous field robots to achieve a modern farmer’s assistant.     

Artificial neural networks and their use in quantitative pathology

A brief general introduction to artificial neural networks is presented, examining in detail the structure and operation of a prototype net developed for the solution of a simple pattern recognition problem in quantitative pathology. The process by which a neural network learns through example and gradually embodies its knowledge as a distributed representation is discussed, using this example. The application of neurocomputer technology to problems in quantitative pathology is explored, using real-world and illustrative examples. Included are examples of the use of artificial neural networks for pattern recognition, database analysis and machine vision. In the context of these examples, characteristics of neural nets, such as their ability to tolerate ambiguous, noisy and spurious data and spontaneously generalize from known examples to handle unfamiliar cases, are examined. Finally, the strengths and deficiencies of a connectionist approach are compared to those of traditional symbolic expert system methodology. It is concluded that artificial neural networks, used in conjunction with other nonalgorithmic artificial intelligence techniques and traditional algorithmic processing, may provide useful software engineering tools for the development of systems in quantitative pathology.     

Content-based image retrieval system using neural networks

An effective image retrieval system is developed based on the use of neural networks (NNs). It takes advantages of association ability of multilayer NNs as matching engines which calculate similarities between a user's drawn sketch and the stored images. The NNs memorize pixel information of every size-reduced image (thumbnail) in the learning phase. In the retrieval phase, pixel information of a user's drawn rough sketch is inputted to the learned NNs and they estimate the candidates. Thus the system can retrieve candidates quickly and correctly by utilizing the parallelism and association ability of NNs. In addition, the system has learning capability: it can automatically extract features of a user's drawn sketch during the retrieval phase and can store them as additional information to improve the performance. The software for querying, including efficient graphical user interfaces, has been implemented and tested. The effectiveness of the proposed system has been investigated through various experimental tests.     

Robust algorithm for arrhythmia classification in ECG using extreme learning machine

Background  

Recently, extensive studies have been carried out on arrhythmia classification algorithms using artificial intelligence pattern recognition methods such as neural network. To improve practicality, many studies have focused on learning speed and the accuracy of neural networks. However, algorithms based on neural networks still have some problems concerning practical application, such as slow learning speeds and unstable performance caused by local minima.     

Automated counting of phytoplankton by pattern recognition: a comparison with a manual counting method

Computer-based image analysis and pattern recognition methodswere used to construct a system able automatically to identify,count and measure selected groups of phytoplankton. An imageanalysis algorithm was employed to isolate and measure objectsfrom digitized images of a phytoplankton sample. The measurementsobtained were used to identify selected groups of phytoplanktonby a combination of artificial neural networks and simple rule-basedprocedures. The system was trained and tested using samplesof lake water covering an annual growth cycle from Lough Neaghin Northern Ireland. Total volume estimates were obtained forthe four major phytoplankton species, using both the automatedsystem and a manual counting method. Estimates of total cellvolume obtained from the automated system were within 10% ofthose derived by manual analysis of the same cells. The automatedsystem produced total cell volume estimates close to those obtainedfrom manual analysis of different aliquots of the same watersample. Variation between successive counts of the same watersample was higher with the automated system than with the manualcounting method. Limitations and possible improvements to thetechnology are discussed.     

Volume signalling in real and robot nervous systems

Summary This paper presents recent work in computational modelling of diffusing gaseous neuromodulators in biological nervous systems. A variety of interesting and significant properties of such four dimensional neural signalling systems are demonstrated. It is shown that the morphology of the neuromodulator source plays a highly significant role in the diffusion patterns observed. The paper goes on to describe work in adaptive autonomous systems directly inspired by this: an exploration of the use of virtual diffusing modulators in robot nervous systems built from non-standard artificial neural networks. These virtual chemicals act over space and time modulating a variety of node and connection properties in the networks. A wide variety of rich dynamics are possible in such systems; in the work described here, evolutionary robotics techniques have been used to harness the dynamics to produce autonomous behaviour in mobile robots. Detailed comparative analyses of evolutionary searches, and search spaces, for robot controllers with and without the virtual gases are introduced. The virtual diffusing modulators are found to provide significant advantages.     

人工神经网络模型及其在农业和生态学研究中的应用

对于一些复杂的农业生态系统,人们对其生态过程了解较少,且这些系统的不确定性和模糊性较大,用传统的方法难以模拟这些系统的行为,神经网络模型因为能较精确地模拟这些系统的行为,而引起生态学者们的广泛兴趣。该文着重介绍了误差逆传神经网络模型的结构、算法及其在农业和生态学中的应用研究。误差逆传神经网络模型一般采用三层神经网络模型结构,三层的神经网络模型能模拟任意复杂程度的连续函数,而且因为它的结构小而不容易产生与训练数据的过度吻合。误差逆传神经网络模型算法的主要特征是:利用当前的输入误差对权值进行调整。在生态学和农业研究中,误差逆传神经网络模型通常作为非线性函数模拟器用于预测作物产量、生物生产量、生物与环境之间的关系等。已有的研究表明:误差逆传神经网络模型的模拟精度要远远高于多元线性方程,类似于非线性方程,而在样本量足够的情况下,有一定的外推能力。但是误差逆传神经网络模型需要大量的样本量来保证所求取参数的可靠性,但这在实际研究中很难做到,因而限制了误差逆传神经网络模型的应用。近年来人们提出了强制训练停止、复合模型等多种技术来提高误差逆传神经网络模型的外推能力,也提出了Garson算法、敏感性分析以及随机化检验等技术对误差逆传神经网络模型的机理进行解释。误差逆传神经网络模型的真正优势在于模拟人们了解较少或不确定性和模糊性较大系统的行为,这些是传统模型所无法实现的,因而是对传统机理模型的重要补充。     

Evidence for search image in blackbirds (Turdus merula L.): short-term learning

A ‘search image’ is acquired as a result of a change in the ability of a predator to detect cryptic familiar prey. Blackbirds were presented with artificial prey dyed either to match the colour of the background (cryptic prey) or to contrast with the background (conspicuous prey). The results of experiment 1 provide some evidence that 10 wild blackbirds had more difficulty detecting cryptic prey than conspicuous prey. Detailed analysis of experiments 2 (11 wild blackbirds) and 3 (six captive blackbirds) revealed subtle changes in the reactions of the birds to cryptic prey. At first these birds were unable to detect cryptic prey, but subsequently improved their ability to detect such food. This short-term change is interpreted as evidence for search images.     

Neural network training with global optimization techniques

This paper presents an approach of using Simulated Annealing and Tabu Search for the simultaneous optimization of neural network architectures and weights. The problem considered is the odor recognition in an artificial nose. Both methods have produced networks with high classification performance and low complexity. Generalization has been improved by using the backpropagation algorithm for fine tuning. The combination of simple and traditional search methods has shown to be very suitable for generating compact and efficient networks.     

Vision-based force measurement using neural networks for biological cell microinjection

This paper presents a vision-based force measurement method using an artificial neural network model. The proposed model is used for measuring the applied load to a spherical biological cell during micromanipulation process. The devised vision-based method is most useful when force measurement capability is required, but it is very challenging or even infeasible to use a force sensor. Artificial neural networks in conjunction with image processing techniques have been used to estimate the applied load to a cell. A bio-micromanipulation system capable of force measurement has also been established in order to collect the training data required for the proposed neural network model. The geometric characterization of zebrafish embryos membranes has been performed during the penetration of the micropipette prior to piercing. The geometric features are extracted from images using image processing techniques. These features have been used to describe the shape and quantify the deformation of the cell at different indentation depths. The neural network is trained by taking the visual data as the input and the measured corresponding force as the output. Once the neural network is trained with sufficient number of data, it can be used as a precise sensor in bio-micromanipulation setups. However, the proposed neural network model is applicable for indentation of any other spherical elastic object. The results demonstrate the capability of the proposed method. The outcomes of this study could be useful for measuring force in biological cell micromanipulation processes such as injection of the mouse oocyte/embryo.     

Estimation of 3D rotation of femur in 2D hip radiographs

Femoral radiographs are affected by the degree of rotation of the femur with respect to the plane of projection. We aimed to determine the 3D rotation of the proximal femur in 2D radiographs. A 3D Statistical Appearance Model (SAM), which was built from CT images of cadaver proximal femurs (n=33) was randomly sampled to form a training set of 500 bones. Nineteen clinical CT images were collected for testing. All CT images were rotated to ±20° in 2° division around the shaft axis, ±10° around medial-lateral axis, and by simultaneous rotation of both axes (±16° and ±8° around shaft and medial-lateral axes). In each orientation, a 2D projection was recorded for generating a 2D SAM. The outcome parameters of the 2D SAM were used as input for a linear regression model and an artificial neural network to predict the rotation. The artificial neural network estimated the rotation more accurately than the linear regression. For artificial neural networks the mean errors were 4.0° and 2.0° around the shaft and medial-lateral axes, respectively. For an individual radiograph, the confidence interval of estimation was still relatively large. However, this method has high potential to differentiate the amount of rotations in two image sets.     

Temporal classification of Drosophila segmentation gene expression patterns by the multi-valued neural recognition method

In order to reconstruct the establishment of the body pattern over time in Drosophila embryos, we have developed automated methods for detecting the age of an embryo on the basis of knowledge about its gene expression patterns. In this paper we perform temporal classification of confocal images of expression patterns of genes controlling segmentation by means of a neural network based on multi-valued neurons (MVN). MVN are artificial neural processing elements with complex-valued weights and high functionality, which proved to be efficient for solving the image recognition problems. The results obtained by this method confirm its efficiency for image recognition and indicate that the method can detect characteristic features of expression patterns which mark their development over time.     

Homeostatic plasticity improves signal propagation in continuous-time recurrent neural networks

Continuous-time recurrent neural networks (CTRNNs) are potentially an excellent substrate for the generation of adaptive behaviour in artificial autonomous agents. However, node saturation effects in these networks can leave them insensitive to input and stop signals from propagating. Node saturation is related to the problems of hyper-excitation and quiescence in biological nervous systems, which are thought to be avoided through the existence of homeostatic plastic mechanisms. Analogous mechanisms are here implemented in a variety of CTRNN architectures and are shown to increase node sensitivity and improve signal propagation, with implications for robotics. These results lend support to the view that homeostatic plasticity may prevent quiescence and hyper-excitation in biological nervous systems.     

Comparison of photo‐matching algorithms commonly used for photographic capture–recapture studies

Photographic capture–recapture is a valuable tool for obtaining demographic information on wildlife populations due to its noninvasive nature and cost‐effectiveness. Recently, several computer‐aided photo‐matching algorithms have been developed to more efficiently match images of unique individuals in databases with thousands of images. However, the identification accuracy of these algorithms can severely bias estimates of vital rates and population size. Therefore, it is important to understand the performance and limitations of state‐of‐the‐art photo‐matching algorithms prior to implementation in capture–recapture studies involving possibly thousands of images. Here, we compared the performance of four photo‐matching algorithms; Wild‐ID, I3S Pattern+, APHIS, and AmphIdent using multiple amphibian databases of varying image quality. We measured the performance of each algorithm and evaluated the performance in relation to database size and the number of matching images in the database. We found that algorithm performance differed greatly by algorithm and image database, with recognition rates ranging from 100% to 22.6% when limiting the review to the 10 highest ranking images. We found that recognition rate degraded marginally with increased database size and could be improved considerably with a higher number of matching images in the database. In our study, the pixel‐based algorithm of AmphIdent exhibited superior recognition rates compared to the other approaches. We recommend carefully evaluating algorithm performance prior to using it to match a complete database. By choosing a suitable matching algorithm, databases of sizes that are unfeasible to match “by eye” can be easily translated to accurate individual capture histories necessary for robust demographic estimates.     

Using deep neural networks to model similarity between visual patterns: Application to fish sexual signals

The evolution of visual patterns is a frontier in the theory of sexual selection as we seek to understand the function of complex visual patterning in courtship. Recently, the sensory drive and sensory bias models of sexual selection have been applied to higher-level visual processing. One prediction of this application is that animals' sexual signals will mimic the visual statistics of their habitats. An enduring difficulty of testing predictions of visual pattern evolution is in developing quantitative methods for comparing patterns. Advances in artificial neural networks address this challenge by allowing for the direct comparison of images using both simple and complex features. Here, we use VGG19, an industry‑leading image classification network to test predictions of sensory drive, by comparing visual patterns in darter fish (Etheostoma spp.) to images of their habitats. We find that images of female darters are significantly more similar to images of their habitat than are images of males, supporting a role of camouflage in female patterning. We do not find direct evidence for sensory drive shaping the design of male patterns; however, this work demonstrates the utility of network methods for pattern analysis and suggests future directions for visual pattern research.     

Motor-related signals support localization invariance for stable visual perception

Our ability to perceive a stable visual world in the presence of continuous movements of the body, head, and eyes has puzzled researchers in the neuroscience field for a long time. We reformulated this problem in the context of hierarchical convolutional neural networks (CNNs)—whose architectures have been inspired by the hierarchical signal processing of the mammalian visual system—and examined perceptual stability as an optimization process that identifies image-defining features for accurate image classification in the presence of movements. Movement signals, multiplexed with visual inputs along overlapping convolutional layers, aided classification invariance of shifted images by making the classification faster to learn and more robust relative to input noise. Classification invariance was reflected in activity manifolds associated with image categories emerging in late CNN layers and with network units acquiring movement-associated activity modulations as observed experimentally during saccadic eye movements. Our findings provide a computational framework that unifies a multitude of biological observations on perceptual stability under optimality principles for image classification in artificial neural networks.     

Performance of deep learning restoration methods for the extraction of particle dynamics in noisy microscopy image sequences

Particle tracking in living systems requires low light exposure and short exposure times to avoid phototoxicity and photobleaching and to fully capture particle motion with high-speed imaging. Low-excitation light comes at the expense of tracking accuracy. Image restoration methods based on deep learning dramatically improve the signal-to-noise ratio in low-exposure data sets, qualitatively improving the images. However, it is not clear whether images generated by these methods yield accurate quantitative measurements such as diffusion parameters in (single) particle tracking experiments. Here, we evaluate the performance of two popular deep learning denoising software packages for particle tracking, using synthetic data sets and movies of diffusing chromatin as biological examples. With synthetic data, both supervised and unsupervised deep learning restored particle motions with high accuracy in two-dimensional data sets, whereas artifacts were introduced by the denoisers in three-dimensional data sets. Experimentally, we found that, while both supervised and unsupervised approaches improved tracking results compared with the original noisy images, supervised learning generally outperformed the unsupervised approach. We find that nicer-looking image sequences are not synonymous with more precise tracking results and highlight that deep learning algorithms can produce deceiving artifacts with extremely noisy images. Finally, we address the challenge of selecting parameters to train convolutional neural networks by implementing a frugal Bayesian optimizer that rapidly explores multidimensional parameter spaces, identifying networks yielding optimal particle tracking accuracy. Our study provides quantitative outcome measures of image restoration using deep learning. We anticipate broad application of this approach to critically evaluate artificial intelligence solutions for quantitative microscopy.     

A template matching model for pattern recognition: Self-organization of templates and template matching by a disinhibitory neural network

A template matching model for pattern recognition is proposed. By following a previouslyproposed algorithm for synaptic modification (Hirai, 1980), the template of a stimulus pattern is selforganized as a spatial distribution pattern of matured synapses on the cells receiving modifiable synapses. Template matching is performed by the disinhibitory neural network cascaded beyond the neural layer composed of the cells receiving the modifiable synapses. The performance of the model has been simulated on a digital computer. After repetitive presentations of a stimulus pattern, a cell receiving the modifiable synapses comes to have the template of that pattern. And the cell in the latter layer of the disinhibitory bitory neural network that receives the disinhibitory input from that cell becomes electively sensitive to that pattern. Learning patterns are not restricted by previously learned ones. They can be subset or superset patterns of the ones previously learned. If an unknown pattern is presented to the model, no cell beyond the disinhibitory neural network will respond. However, if previously learned patterns are embedded in that pattern, the cells which have the templates of those patterns respond and are assumed to transmit the information to higher center. The computer simulation also shows that the model can organize a clean template under a noisy environment.