Relevant bibliographies by topics / Automatic insect identification systems

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Academic literature on the topic 'Automatic insect identification systems'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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Journal articles on the topic "Automatic insect identification systems"

Wu, Feng, and Yueying Li. "Lightweight Field Insect Recognition and Classification Model Based on Improved Deep Learning under Complex Background." Security and Communication Networks 2023 (April 7, 2023): 1–9. http://dx.doi.org/10.1155/2023/6560747.

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Insect monitoring in the field is an extremely important part of the agricultural production system. Recent advances in computer technology have provided the technical foundation for automatic field insect monitoring. In insect automatic monitoring, insect recognition and classification based on images is one of the most active research areas. Rapid advancements in computer vision technology based on deep learning have provided new ideas for implementing automatic field insect monitoring. Firstly, the field insect images are preprocessed and input to the lightweight algorithm for feature extraction, and the prediction networks of different sizes are output by multiscale feature fusion; then, the joint cross-merge ratio is introduced for automatic identification and classification of field insects. Compared with other algorithms, the simulation results show that the proposed algorithm has higher accuracy, less time consumption, and stronger robustness. It effectively solves the insect accumulation and background interference problems and can identify field insects online in real time.

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Zhao, Yue, Lin Zhou, Aocheng Zhou, and Yao Xiao. "Automatic insect identification system based on SE-ResNeXt." International Journal of Systems, Control and Communications 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijscc.2022.10049960.

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Xiao, Yao, Aocheng Zhou, Lin Zhou, and Yue Zhao. "Automatic insect identification system based on SE-ResNeXt." International Journal of Systems, Control and Communications 14, no. 1 (2023): 81. http://dx.doi.org/10.1504/ijscc.2023.127487.

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Cardim Ferreira Lima, Matheus, Maria Elisa Damascena de Almeida Leandro, Constantino Valero, Luis Carlos Pereira Coronel, and Clara Oliva Gonçalves Bazzo. "Automatic Detection and Monitoring of Insect Pests—A Review." Agriculture 10, no. 5 (2020): 161. http://dx.doi.org/10.3390/agriculture10050161.

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Many species of insect pests can be detected and monitored automatically. Several systems have been designed in order to improve integrated pest management (IPM) in the context of precision agriculture. Automatic detection traps have been developed for many important pests. These techniques and new technologies are very promising for the early detection and monitoring of aggressive and quarantine pests. The aim of the present paper is to review the techniques and scientific state of the art of the use of sensors for automatic detection and monitoring of insect pests. The paper focuses on the methods for identification of pests based in infrared sensors, audio sensors and image-based classification, presenting the different systems available, examples of applications and recent developments, including machine learning and Internet of Things. Future trends of automatic traps and decision support systems are also discussed.

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Wang, Jing, Minglin Hong, Xia Hu, et al. "Camouflaged Insect Segmentation Using a Progressive Refinement Network." Electronics 12, no. 4 (2023): 804. http://dx.doi.org/10.3390/electronics12040804.

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Accurately segmenting an insect from its original ecological image is the core technology restricting the accuracy and efficiency of automatic recognition. However, the performance of existing segmentation methods is unsatisfactory in insect images shot in wild backgrounds on account of challenges: various sizes, similar colors or textures to the surroundings, transparent body parts and vague outlines. These challenges of image segmentation are accentuated when dealing with camouflaged insects. Here, we developed an insect image segmentation method based on deep learning termed the progressive refinement network (PRNet), especially for camouflaged insects. Unlike existing insect segmentation methods, PRNet captures the possible scale and location of insects by extracting the contextual information of the image, and fuses comprehensive features to suppress distractors, thereby clearly segmenting insect outlines. Experimental results based on 1900 camouflaged insect images demonstrated that PRNet could effectively segment the camouflaged insects and achieved superior detection performance, with a mean absolute error of 3.2%, pixel-matching degree of 89.7%, structural similarity of 83.6%, and precision and recall error of 72%, which achieved improvements of 8.1%, 25.9%, 19.5%, and 35.8%, respectively, when compared to the recent salient object detection methods. As a foundational technology for insect detection, PRNet provides new opportunities for understanding insect camouflage, and also has the potential to lead to a step progress in the accuracy of the intelligent identification of general insects, and even being an ultimate insect detector.

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Suto, Jozsef. "Codling Moth Monitoring with Camera-Equipped Automated Traps: A Review." Agriculture 12, no. 10 (2022): 1721. http://dx.doi.org/10.3390/agriculture12101721.

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The codling moth (Cydia pomonella) is probably the most harmful pest in apple and pear orchards. The crop loss due to the high harmfulness of the insect can be extremely expensive; therefore, sophisticated pest management is necessary to protect the crop. The conventional monitoring approach for insect swarming has been based on traps that are periodically checked by human operators. However, this workflow can be automatized. To achieve this goal, a dedicated image capture device and an accurate insect counter algorithm are necessary which make online insect swarm prediction possible. From the hardware side, more camera-equipped embedded systems have been designed to remotely capture and upload pest trap images. From the software side, with the aid of machine vision and machine learning methods, traditional (manual) identification and counting can be solved by algorithm. With the appropriate combination of the hardware and software components, spraying can be accurately scheduled, and the crop-defending cost will be significantly reduced. Although automatic traps have been developed for more pest species and there are a large number of papers which investigate insect detection, a limited number of articles focus on the C. pomonella. The aim of this paper is to review the state of the art of C. pomonella monitoring with camera-equipped traps. The paper presents the advantages and disadvantages of automated traps’ hardware and software components and examines their practical applicability.

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Gong, He, Tonghe Liu, Tianye Luo, et al. "Based on FCN and DenseNet Framework for the Research of Rice Pest Identification Methods." Agronomy 13, no. 2 (2023): 410. http://dx.doi.org/10.3390/agronomy13020410.

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One of the most important food crops is rice. For this reason, the accurate identification of rice pests is a critical foundation for rice pest control. In this study, we propose an algorithm for automatic rice pest identification and classification based on fully convolutional networks (FCNs) and select 10 rice pests for experiments. First, we introduce a new encoder–decoder in the FCN and a series of sub-networks connected by jump paths that combine long jumps and shortcut connections for accurate and fine-grained insect boundary detection. Secondly, the network also integrates a conditional random field (CRF) module for insect contour refinement and boundary localization, and finally, a novel DenseNet framework that introduces an attention mechanism (ECA) is proposed to focus on extracting insect edge features for effective rice pest classification. The proposed model was tested on the data set collected in this paper, and the final recognition accuracy was 98.28%. Compared with the other four models in the paper, the proposed model in this paper is more accurate, faster, and has good robustness; meanwhile, it can be demonstrated from our results that effective segmentation of insect images before classification can improve the detection performance of deep-learning-based classification systems.

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Dr., Priyanka Solanki, Varma Nalla Anil, Satwika Maharaj, Bhattacharjya Swastik, Sonu Kumar, and Rajput Rajat. "Smart Pest Management in Precision Farming: A Comprehensive Review." International Journal of Environmental & Agriculture Research (IJOEAR) 10, no. 7 (2024): 23–35. https://doi.org/10.5281/zenodo.13133427.

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Precision agriculture has transformed current agricultural techniques by incorporating cutting-edge technology to maximize resource usage, increase crop output, and reduce insect threats. This article delves into the nexus of precision agriculture and pest management, explaining how precise approaches are designed to address pest concerns effectively and sustainably. Precision agriculture takes a multifaceted approach to pest control, using technology and tactics from throughout the agricultural environment. Remote sensing technologies are essential for early pest identification, sensor technologies for real-time field monitoring, and GPS/GIS applications for accurate mapping and focused control methods. The integration of entomological data is critical in precision pest management because it allows for precise pest identification, behaviour monitoring, and predictive modelling, which helps to successfully forecast and prevent pest outbreaks. Automated insect identification technologies, DNA barcoding, and decision support systems allow for proactive pest control tactics adapted to individual pest species and environmental circumstances. Precision pest management's economic and environmental consequences highlight its importance in contemporary agriculture, with cost-benefit analyses demonstrating increased efficiency and a lower environmental imprint. To encourage wider use and maximum benefits, specific solutions must address implementation challenges such as technology hurdles and farmer acceptance. 

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Swain, Deepak Kumar, Ved Vrat Verma, Manni Sruthi, Anvesha Garg, and Vaibhav Kaushik. "3D modeling and automated identification of insect species using AI-based systems." Journal of Entomological Research 48, suppl (2024): 673–79. https://doi.org/10.5958/0974-4576.2024.00131.3.

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Chesmore, E. D., and E. Ohya. "Automated identification of field-recorded songs of four British grasshoppers using bioacoustic signal recognition." Bulletin of Entomological Research 94, no. 4 (2004): 319–30. http://dx.doi.org/10.1079/ber2004306.

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AbstractRecognition of Orthoptera species by means of their song is widely used in field work but requires expertise. It is now possible to develop computer-based systems to achieve the same task with a number of advantages including continuous long term unattended operation and automatic species logging. The system described here achieves automated discrimination between different species by utilizing a novel time domain signal coding technique and an artificial neural network. The system has previously been shown to recognize 25 species of British Orthoptera with 99% accuracy for good quality sounds. This paper tests the system on field recordings of four species of grasshopper in northern England in 2002 and shows that it is capable of not only correctly recognizing the target species under a range of acoustic conditions but also of recognizing other sounds such as birds and man-made sounds. Recognition accuracies for the four species of typically 70–100% are obtained for field recordings with varying sound intensities and background signals.

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Dissertations / Theses on the topic "Automatic insect identification systems"

De, Marco Luisa <1984&gt. "Optical Design for Automatic Identification and Portable Systems." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amsdottorato.unibo.it/4600/1/DeMarco_Luisa_Tesi.pdf.

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This thesis proposes design methods and test tools, for optical systems, which may be used in an industrial environment, where not only precision and reliability but also ease of use is important. The approach to the problem has been conceived to be as general as possible, although in the present work, the design of a portable device for automatic identification applications has been studied, because this doctorate has been funded by Datalogic Scanning Group s.r.l., a world-class producer of barcode readers. The main functional components of the complete device are: electro-optical imaging, illumination and pattern generator systems. For what concerns the electro-optical imaging system, a characterization tool and an analysis one has been developed to check if the desired performance of the system has been achieved. Moreover, two design tools for optimizing the imaging system have been implemented. The first optimizes just the core of the system, the optical part, improving its performance ignoring all other contributions and generating a good starting point for the optimization of the whole complex system. The second tool optimizes the system taking into account its behavior with a model as near as possible to reality including optics, electronics and detection. For what concerns the illumination and the pattern generator systems, two tools have been implemented. The first allows the design of free-form lenses described by an arbitrary analytical function exited by an incoherent source and is able to provide custom illumination conditions for all kind of applications. The second tool consists of a new method to design Diffractive Optical Elements excited by a coherent source for large pattern angles using the Iterative Fourier Transform Algorithm. Validation of the design tools has been obtained, whenever possible, comparing the performance of the designed systems with those of fabricated prototypes. In other cases simulations have been used.

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De, Marco Luisa <1984&gt. "Optical Design for Automatic Identification and Portable Systems." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amsdottorato.unibo.it/4600/.

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Kim, Jijoong. "Automatic aircraft recognition and identification." Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20060808.161115/index.html.

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Cabell, Randolph H. "The automatic identification of aerospace acoustic sources." Thesis, Virginia Tech, 1989. http://hdl.handle.net/10919/45932.

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This work describes the design of an intelligent recognition system used to distinguish noise signatures of five different acoustic sources. The system uses pattern recognition techniques to identify the information obtained from a single microphone. A training phase is used in which the system learns to distinguish the sources and automatically selects features for optimal performance. Results were obtained by training the system to distinguish jet planes, propeller planes, a helicopter, train, and wind turbine from one another, then presenting similar sources to the system and recording the number of errors. These results indicate the system can successfully identify the trained sources based on acoustic information. Classification errors highlight the impact of the training sources on the system's ability to recognize different sources. Master of Science

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Hidayat, Egi. "On Identification of Biological Systems." Doctoral thesis, Uppsala universitet, Avdelningen för systemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-215699.

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System identification finds nowadays application in various areas of biological research as a tool of empiric mathematical modeling and model individualization. A fundamental challenge of system identification in biology awaits in the form of response variability. Furthermore, biological systems tend to exhibit high degree of nonlinearity as well as significant time delays. This thesis covers system identification approaches developed for the applications within two particular biomedical fields: neuroscience and endocrinology. The first topic of the thesis is parameter estimation of the classical Elementary Motion Detector (EMD) model in insect vision. There are two important aspects to be taken care of in the identification approach, namely the nonlinear dynamics of the individual EMD and the spatially distributed structure of multiple detectors producing a measurable neural response. Hence, the suggested identification method is comprised of two consecutive stages addressing each of the above aspects. Furthermore, visual stimulus design for high spatial excitation order has been investigated. The second topic is parameter estimation of mathematical model for testosterone regulation in the human male. The main challenges of this application are in the unavailability of input signal measurements and the presence of an unknown pulsatile feedback in the system resulting in a highly nonlinear closed-loop dynamics. Semi-blind identification method has been developed based on a recently proposed pulse-modulated model of pulsatile endocrine regulation. The two system identification problems treated in the thesis bear some resemblance in the sense that both involve measured signals that can be seen as square-integrable functions of time. This property is handled by transforming the signals into the Laguerre domain, i.e. by equivalently representing the functions with their infinite Laguerre series.

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Qin, Tiantian. "Identification of Reliable Cues for an Automatic Deception Detection System." Diss., The University of Arizona, 2007. http://hdl.handle.net/10150/194385.

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An automatic deception detection system (ADDS) is to detect deceptive human behavior with machine extractable evidences (i.e., cues). One of the most prominent challenges for building a ADDS is the availability of reliable cues. This study represents one of the first attempts to address the system's reliability by identifying the set of reliable cues in order to improve the system performance (detection accuracy).This study addresses two critical challenges of existing machine cues, irreproducibility and inconsistency. First, in order to mitigate the irreproducibility, the study introduces a set of machine measurable cues to estimate the commonality of related machine cues. These more reproducible cues are referred to as the macro cues which can be applied for automatic pattern recognition. Second, in order to address the consistency, the study separates cues based on the controllability, and defines the strategic cues as those can easily be manipulated by deceivers during interaction. The strategic cues fluctuate during deception and thus are less consistently reliable as predictors for the ADDS. On the contrary, the nonstrategic cues are more consistent. This study also considers other moderator effects that influencing the ADDS performance: time and the condition of interviewer's immediacy (ERIMD).The macro cues are automatically estimated from the micro cues based on the predefined relational models. The empirical data support the relationship models between macro and micro cues. Results show that macro cues mitigate the irreproducibility problem by reducing the variability in the single cues. However, the results also show that using macro cues as predictors in the discriminant analysis does not perform better than micro cues, and thus imply the needs to adjust weights of important components when constructing the macro cues. In terms of the consistent cues, results show that the nonstrategic cues are relatively more consistent than strategic ones in ADDS performance. Furthermore, the study suggests that particular detection methods must be tailored according to the feature of strategic and nonstrategic cues. The findings have many potential implications. One is to use the macro cues to recognize the dynamic patterns in deceptive behaviors. Specifically, truthtellers increase the certainty, immediacy, and tend to decrease the cognitive load; but deceivers behave the opposite. The other is to rely on the characteristics of strategic cues to manipulate the communication environment to improve the ADDS performance. This concept is also referred to as the Proactive Deception Detection (PDD). In the current study, the interviewer's immediacy is a controllable environment factor for PDD. The high ERIMD increase the system performance because it has higher overhead added to the deceptive behavior to trigger more abnormal cues. In sum, methods and results of this study have multiple impacts in information assurance and human-computer interaction.

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Combrinck, Hendrik Petrus. "A cost, complexity and performance comparison of two automatic language identification architectures." Pretoria : [s.n.], 2006. http://upetd.up.ac.za/thesis/available/etd-12212006-141335/.

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Kuriakose, Rengith Baby. "Automatic student attendance registration using radio frequency identification (RFID)." Thesis, [Bloemfontein?] : Central University of Technology, Free State, 2010. http://hdl.handle.net/11462/27.

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Thesis (M. Tech.) - Central University of Technology, Free State, 2010 The main aim of this research was to automate student attendance registration, thereby reducing human involvement in the whole process. This was made possible using Radio Frequency Identification (RFID) technology. The Central University of Technology uses student cards that are compatible for use with RFID technology. As a result, no initial investment (except for the existing personal computer’s and the constructed RFID reader) in infrastructure was required for this project. The basic working of the project was as follows. The students belonging to a specific class had their vital educational data (Student number, Name) entered into a database table at the time of registration. A student card containing a serial number, with reference to the data contained in the database table, was given to the students after registration. The students walk into their respective classes and scan their student cards with the RFID reader. The serial number stored in the student card is transferred to the reader and from there wirelessly to the main server using ZigBee technology. In the main server, using Java programming language, the card serial number is sent to the Integrated Development Environment (IDE). In this project the Netbeans IDE (Java platform) was used. The Netbeans IDE is connected to the Apache Derby database using Java Database Connector (JDBC), so the serial number (which is referenced to the educational data of the students) from the student card is automatically compared with the original database created at the time of registration. Once a match is confirmed between the two entries, the data is entered into a separate database table which serves as the basic attendance sheet for a specific day.

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Byrne, Carlton B. "Assembly task identification and strategy development using expert systems and neural networks." Thesis, Cardiff University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266650.

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Bhattacharjee, Partha Sarathi S. M. Massachusetts Institute of Technology. "VacSeen : semantically enriched automatic identification and data capture for improved vaccine logistics." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107582.

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Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program, 2016. Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, System Design and Management Program, 2016. Cataloged from PDF version of thesis. Includes bibliographical references (pages 79-82). Vaccines are globally recognized as a critical public health intervention. Routine immunization coverage in large parts of the developing world is around 80%. Technology and policy initiatives are presently underway to improve vaccine access in such countries. Efforts to deploy AIDC technologies, such as barcodes, on vaccine packaging in developing countries are currently ongoing under the aegis of the 'Decade of Vaccines' initiative by key stakeholders. Such a scenario presents an opportunity to evaluate novel approaches for enhancing vaccine access. In this thesis I report the development of VacSeen, a Semantic Web technology-enabled platform for improving vaccine access in developing countries. Furthermore, I report results of evaluation of a suite of constituent software and hardware tools pertaining to facilitating equitable vaccine access in resource-constrained settings through data linkage and temperature sensing. I subsequently discuss the value of such linkage and approaches to implementation using concepts from technology, policy, and systems analysis. by Partha Sarathi Bhattacharjee. S.M. in Technology and Policy S.M. in Engineering and Management

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Books on the topic "Automatic insect identification systems"

Hans, Du Buf, and Bayer Micha M, eds. Automatic diatom identification. World Scientific, 2002.

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Bolle, Ruud, and Nalini K. Ratha. Automatic fingerprint recognition systems. Springer, 2004.

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LaMoreaux, Robert D. Barcodes and other automatic identification systems. Pira International, 1995.

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1928-, Moran Robert, and Business Communications Co, eds. Automatic product/people identification: Systems, hardware. Business Communications Co., 1987.

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AIM, ed. Data communications in automatic identification systems. AIM, 1987.

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Cuttino, James Franklin. Identification and compensation of nonlinear effects in precision actuator systems. UMI Dissertation Services, 2001.

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Adams, Russell E. Sourcebook of automatic identification and data collection. Van Nostrand Reinhold, 1990.

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Adams, Russ. Sourcebook of automatic identification and data collection. Van Nostrand Reinhold, 1990.

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Hugues, Garnier, and Wang Liuping, eds. Identification of continuous-time models from sampled data. Springer, 2008.

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IEEE Workshop on Automatic Identification Advanced Technologies (2007 Alghero, Italy). 2007 IEEE Workshop on Automatic Identification Advanced Technologies: Proceedings : 7-8 June 2007, Alghero, Italy. IEEE, 2007.

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Book chapters on the topic "Automatic insect identification systems"

Read, Edward F. "Nearshore Automatic Identification Systems." In Coasts, marine structures and breakwaters: Adapting to change. Thomas Telford Ltd, 2010. http://dx.doi.org/10.1680/cmsb.41318.0073.

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Lewańska, Elżbieta. "Towards Automatic Business Networks Identification." In Business Information Systems Workshops. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52464-1_36.

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Saidi, Malak, Anis Tissaoui, Djamal Benslimane, and Sami Faiz. "Automatic Microservices Identification Across Structural Dependency." In Hybrid Intelligent Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96305-7_36.

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Zhdanova, Anna V. "Automatic Identification of European Languages." In Natural Language Processing and Information Systems. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36271-1_7.

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Banerjee, Debanjan, Suchibrota Dutta, and Arijit Ghosal. "Automatic Gender Identification Through Speech." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4367-2_36.

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Ludwig, Rafael, and Johannes Gerstmayr. "Automatic Parameter Identification for Mechatronic Systems." In Multibody System Dynamics, Robotics and Control. Springer Vienna, 2012. http://dx.doi.org/10.1007/978-3-7091-1289-2_12.

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O'Toole, Alice, and P. Jonathon Phillips. "Evaluating Automatic Face Recognition Systems with Human Benchmarks." In Forensic Facial Identification. John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118469538.ch11.

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Mayo, Michael, and Anna T. Watson. "Automatic Species Identification of Live Moths." In Applications and Innovations in Intelligent Systems XIV. Springer London, 2007. http://dx.doi.org/10.1007/978-1-84628-666-7_5.

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Carvalho, Silvestre, and Elsa Ferreira Gomes. "Automatic Identification of Bird Species from Audio." In Intelligent Information and Database Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73280-6_4.

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Ferilli, Stefano, Giovanni Luca Izzi, and Tiziano Franza. "Automatic Stopwords Identification from Very Small Corpora." In Intelligent Systems in Industrial Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67148-8_3.

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Conference papers on the topic "Automatic insect identification systems"

Sharma, Jatin. "EfficientNetB3 for High-Performance Insect Identification." In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI). IEEE, 2024. http://dx.doi.org/10.1109/icoici62503.2024.10696281.

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Reddyboina, Ashok, Talari Pavan Kumar, Saripineni Varshitha, and Matta Naveen Reddy. "Smart Agriculture by Leveraging AI and IoT for Intelligent Insect Identification and Control." In 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS). IEEE, 2024. http://dx.doi.org/10.1109/iciteics61368.2024.10625323.

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Mettildha Mary, I., T. Ragunthar, M. Priyadharsini, H. Shyam Krishnaa, and M. K. Sujit. "Automatic Fish Species Identification Using Convolutional Neural Networks (CNN)." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10762987.

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Kodali, Ravi Kishore, and Arindam Pattanayak. "Automatic Identification of the American Sign Language Using Machine Learning." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10968373.

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König, Eva, Nicolai Voget, Max Hartmann, and Dieter Moormann. "Automatic Identification of Safety Landing Points for VTOL UAVs Using Geodata." In 2025 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2025. https://doi.org/10.1109/icuas65942.2025.11007811.

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Silva, Diego, and Gustavo Batista. "Signal classification by similarity and feature extraction with application in automatic insect identification." In XXVIII Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/ctd.2015.10006.

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Insects have a strong relationship with the human-beings. For example, some species of mosquito transmit diseases that kill millions of people around the world. At the same time, the presence of certain insects is essential for the ecological balance and food production. For this reason, we are developing a novel sensor as a tool to efficiently control disease vectors and agricultural pests without harming other species. In this paper, we demonstrate how we overtook the most important challenge to make this sensor practical: the creation of accurate classification systems. Despite the short duration and the very simple structure of the signal, we managed to successfully identify relevant features using speech and audio analysis techniques. We show that we can achieve an accuracy of 98% in the task of disease vector mosquitoes identification.

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Li Zhigang, Fu Zetian, Shi Yan, and Xia Tiehua. "Prototype system of automatic identification cotton insect pests and intelligent decision based on machine vision." In 2003, Las Vegas, NV July 27-30, 2003. American Society of Agricultural and Biological Engineers, 2003. http://dx.doi.org/10.13031/2013.13703.

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Yi Zhang and Zeeshan Fareed. "Prototype system of insects identification based on computer vision." In 2011 2nd International Conference on Control, Instrumentation, and Automation (ICCIA). IEEE, 2011. http://dx.doi.org/10.1109/icciautom.2011.6183926.

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K, Subhashini, Dhanush Kumar V, Rohith Jones S, et al. "Insect Identification in Field Crops Using CNN." In 2023 Intelligent Computing and Control for Engineering and Business Systems (ICCEBS). IEEE, 2023. http://dx.doi.org/10.1109/iccebs58601.2023.10449045.

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Uyan, Tekin, Kalle Jalava, Juhani Orkas, and Kevin Otto. "Additively Manufactured Tags for Cast Part Traceability Using Two-Dimensional Digital Code Direct-Part-Marking." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22310.

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Abstract Statistical quality control is applied in factories and foundries to identify special cause defects and to identify root causes through statistical correlation of process input variations to defects. A difficulty arises in associating process data collected with individual cast parts as they are worked through the foundry and out into the supply chain. Typically, alphanumeric labels for marking castings and manual identification of the castings with route-paper based tracing approaches have been used. Such manual based systems make root cause analysis of quality defect issues tedious. We here develop a semi-automated approach using 3D printed sand mold inserts shaped as 2D matrix codes which thereby permit directly cast identification code into the parts. This enables automated part tracking at the very beginning of the casting process including mold making. Automated scan-based tracking of parts through a foundry and subsequent supply chain allows for statistical process data collected to also be associated with each part processed with unique identification.

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Reports on the topic "Automatic insect identification systems"

Sole, Robert, Brent Bedford, and Gary Patrick. Electromagnetic Compatibility between Marine Automatic Identification and Public Correspondence Systems in the Maritime Mobile VHF Band. Institute for Telecommunication Sciences, 2000. https://doi.org/10.70220/hq4m8qmt.

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Sadot, Einat, Christopher Staiger, and Zvi Kam Weizmann. functional genomic screen for new plant cytoskeletal proteins and the determination of their role in actin mediated functions and guard cells regulation. United States Department of Agriculture, 2003. http://dx.doi.org/10.32747/2003.7587725.bard.

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The original objectives of the approved proposal were: 1. To construct a YFP fused Arabidopsis cDNA library in a mammalian expression vector. 2. To infect the library into a host fibroblast cell line and to screen for new cytoskeletal associated proteins using an automated microscope. 3. Isolate the new genes. 4. Characterize their role in plants. The project was approved as a feasibility study to allow proof of concept that would entail building the YFP library and picking up a couple of positive clones using the fluorescent screen. We report here on the construction of the YFP library, the development of the automatic microscope, the establishment of the screen and the isolation of positive clones that are plant cDNAs encoding cytoskeleton associated proteins. The rational underling a screen of plant library in fibroblasts is based on the high conservation of the cytoskeleton building blocks, actin and tubulin, between the two kingdoms (80-90% homology at the level of amino acids sequence). In addition, several publications demonstrated the recognition of mammalian cytoskeleton by plant cytoskeletal binding proteins and vice versa. The major achievements described here are: 1. The development of an automated microscope equipped with fast laser auto-focusing for high magnification and a software controlling 6 dimensions; X, Y position, auto focus, time, color, and the distribution and density of the fields acquired. This system is essential for the high throughput screen. 2. The construction of an extremely competent YFP library efficiently cloned (tens of thousands of clones collected, no empty vectors detected) with all inserts oriented 5't03'. These parameters render it well representative of the whole transcriptome and efficient in "in-frame" fusion to YFP. 3. The strategy developed for the screen allowing the isolation of individual positive cDNA clones following three rounds of microscopic scans. The major conclusion accomplished from the work described here is that the concept of using mammalian host cells for fishing new plant cytoskeletal proteins is feasible and that screening system developed is complete for addressing one of the major bottlenecks of the plant cytoskeleton field: the need for high throughput identification of functionally active cytoskeletal proteins. The new identified plant cytoskeletal proteins isolated in the pilot screen and additional new proteins which will be isolated in a comprehensive screen will shed light on cytoskeletal mediated processes playing a major role in cellular activities such as cell division, morphogenesis, and functioning such as chloroplast positioning, pollen tube and root hair elongation and the movement of guard cells. Therefore, in the long run the screen described here has clear agricultural implications.

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Chejanovsky, Nor, and Bruce D. Hammock. Enhancement of Baculoviruses' Insecticidal Potency by Expression of Synergistic Anti-Insect Scorpion Toxins. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7573070.bard.

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The extensive use or non-specific, hazardous, chemical insecticides demands the development of "healthier" alternative means for pest control. Insect-specific, baculoviruses expressing anti-insect toxin genes (from mites or scorpions) demonstrated in laboratory assays and field trials enhanced insecticidal activity and provided some protection from lepidopterous larvae to agricultural plantations. To utilize recombinant baculoviruses as commercial biopesticides in row crop agriculture, further increase in their speed of kill should be achieved and the reduction in crop damage should be comparable to the levels obtained with organic insecticides (the problem). In this project we developed strategies to improve further the efficacy of recombinant baculoviruses which included: I) Synergism among baculoviruses expressing different anti-insect toxins: a) Synergism among two complementary anti-insect scorpion neurotoxins each expressed by a separate recombinant baculovirus, both regulated by the same or a different viral promoter. b) Synergism among two complementary anti-insect scorpion neurotoxins expressed by the same recombinant virus, both regulated by the same or a different viral promoter respectively. The above included two classes of pharmacologically complementary toxins: i) toxins with strictly anti-insect selectivity (excitatory and depressant); ii) toxins with preferential anti-insect activity (anti-insect alpha toxins). c) Synergism among wild type viruses, recombinant baculoviruses and chemicals (insecticides and phytochemicals) II) Identification of more potent toxins against lepidopterous pests for their expression by baculoviruses. Our approach was based on the synergistic effect displayed by the combined application of pairs of anti-insect toxins to blowfly and lepidopterous larvae that resulted in 5 fold increase in their insecticidal activity without apparent increase in their anti-mammal toxicity (toxins LqhIT2 and LqhaIT, LqhIT2 and AaIT, and LqhaIT and AaIT (1). Thus, we developed new concepts and produced a "second generation" of recombinant baculoviruses with enhanced potencies and speeds of kill comparable to classical insecticides. These achievements contribute to make these biopesticides a viable alternative to minimize the use of hazardous chemicals in pest control. Also, our project contributed new tools and model systems to advance the study of insect sodium channels.

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Gurevitz, Michael, William A. Catterall, and Dalia Gordon. Learning from Nature How to Design Anti-insect Selective Pesticides - Clarification of the Interacting Face between Insecticidal Toxins and their Na-channel Receptors. United States Department of Agriculture, 2010. http://dx.doi.org/10.32747/2010.7697101.bard.

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Structural details on the interacting faces of toxins and sodium channels (Navs), and particularly identification of elements that confer specificity for insects, are difficult to approach and require suitable experimental systems. Therefore, natural toxins capable of differential recognition of insect and mammalian Navs are valuable leads for design of selective compounds in insect control. We have characterized several scorpion toxins that vary in preference for insect and mammalian Navs, and identified residues important for their action. However, despite many efforts worldwide, only little is known about the receptor sites of these toxins, and particularly on differences between these sites on insect and mammalian Navs. Another problem arises from the massive overuse of chemical insecticides, which increases resistance buildup among various insect pests. A possible solution to this problem is to combine different insecticidal compounds, especially those that provide synergic effects. Our recent finding that combinations of insecticidal receptor site-3 toxins (sea anemone and scorpion alpha) with scorpion beta toxins or their truncated derivatives are synergic in toxicity to insects is therefore timely and strongly supports this approach. Our ability to produce toxins and various Navs in recombinant forms, enable thorough analysis and structural manipulations of both toxins and receptors. On this basis we propose to (1) restrict by mutagenesis the activity of insecticidal scorpion -toxins and sea anemone toxins to insects, and clarify the molecular basis of their synergic toxicity with antiinsect selective -toxins; (2) identify Nav elements that interact with scorpion alpha and sea anemone toxins and those that determine toxin selectivity to insects; (3) determine toxin-channel pairwise side-chain interactions by thermodynamic mutant cycle analysis using our large collection of mutant -toxins and Nav mutants identified in aim 2; (4) clarify the mode of interaction of truncated -toxins with insect Navs, and elucidate how they enhance the activity of insecticidal site-3 toxins. This research may lead to rational design of novel anti-insect peptidomimetics with minimal impact on human health and the environment, and will establish the grounds for a new strategy in insect pest control, whereby a combination of allosterically interacting compounds increase insecticidal action and reduce risks of resistance buildup.

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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, spectral characteristics in the VIS/NIR (0.4-1.0 micron) will be used in conjunction with thermal data to provide accurate and robust detection of fruit in the tree canopy. Hyper-spectral image pairs will be combined to provide automatic stereo matching for accurate 3D position. Secondly, VIS/NIR/FIR (0.4-15.0 micron) spectral sensor technology will be evaluated for potential in-field on-the-tree grading of surface defect, maturity and size for selective fruit harvest. Thirdly, new adaptive Lyapunov-basedHBVS (homography-based visual servo) methods to compensate for camera uncertainty, distortion effects, and provide range to target from a single camera will be developed, simulated, and implemented on a camera testbed to prove concept. HBVS methods coupled with imagespace navigation will be implemented to provide robust target tracking. And finally, harvesting test will be conducted on the developed technologies using the University of Florida harvesting manipulator test bed. During the course of the project it was determined that the second objective was overly ambitious for the project period and effort was directed toward the other objectives. The results reflect the synergistic efforts of the three principals. The USA team has focused on citrus based approaches while the Israeli counterpart has focused on apples. The USA team has improved visual servo control through the use of a statistical-based range estimate and homography. The results have been promising as long as the target is visible. In addition, the USA team has developed improved fruit detection algorithms that are robust under light variation and can localize fruit centers for partially occluded fruit. Additionally, algorithms have been developed to fuse thermal and visible spectrum image prior to segmentation in order to evaluate the potential improvements in fruit detection. Lastly, the USA team has developed a multispectral detection approach which demonstrated fruit detection levels above 90% of non-occluded fruit. The Israel team has focused on image registration and statistical based fruit detection with post-segmentation fusion. The results of all programs have shown significant progress with increased levels of fruit detection over prior art.

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Gurevitz, Michael, William A. Catterall, and Dalia Gordon. face of interaction of anti-insect selective toxins with receptor site-3 on voltage-gated sodium channels as a platform for design of novel selective insecticides. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7699857.bard.

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Voltage-gated sodium channels (Navs) play a pivotal role in excitability and are a prime target of insecticides like pyrethroids. Yet, these insecticides are non-specific due to conservation of Navs in animals, raising risks to the environment and humans. Moreover, insecticide overuse leads to resistance buildup among insect pests, which increases misuse and risks. This sad reality demands novel, more selective, insect killers whose alternative use would avoid or reduce this pressure. As highly selective insect toxins exist in venomous animals, why not exploit this gift of nature and harness them in insect pest control? Many of these peptide toxins target Navs, and since their direct use via transformed crop plants or mediator microorganisms is problematic in public opinion, we focus on the elucidation of their receptor binding sites with the incentive of raising knowledge for design of toxin peptide mimetics. This approach is preferred nowadays by agro-industries in terms of future production expenses and public concern. However, characterization of a non-continuous epitope, that is the channel receptor binding site for such toxins, requires a suitable experimental system. We have established such a system within more than a decade and reached the stage where we employ a number of different insect-selective toxins for the identification of their receptor sites on Navs. Among these toxins we wish to focus on those that bind at receptor site-3 and inhibit Nav inactivation because: (1) We established efficient experimental systems for production and manipulation of site-3 toxins from scorpions and sea anemones. These peptides vary in size and structure but compete for site-3 on insect Navs. Moreover, these toxins exhibit synergism with pyrethroids and with other channel ligands; (2) We determined their bioactive surfaces towards insect and mammalian receptors (see list of publications); (3) We found that despite the similar mode of action on channel inactivation, the preference of the toxins for insect and mammalian channel subtypes varies greatly, which can direct us to structural features in the basis of selectivity; (4) We have identified by channel loop swapping and point mutagenesis extracellular segments of the Navinvolved with receptor site-3. On this basis and using channel scanning mutagenesis, neurotoxin binding, electrophysiological analyses, and structural data we offer: (i) To identify the residues that form receptor site-3 at insect and mammalian Navs; (ii) To identify by comparative analysis differences at site-3 that dictate selectivity toward various Navs; (iii) To exploit the known toxin structures and bioactive surfaces for modeling their docking at the insect and mammalian channel receptors. The results of this study will enable rational design of novel anti-insect peptide mimetics with minimized risks to human health and to the environment. We anticipate that the release of receptor site-3 molecular details would initiate a worldwide effort to design peptide mimetics for that site. This will establish new strategies in insect pest control using alternative insecticides and the combined use of compounds that interact allosterically leading to increased efficiency and reduced risks to humans or resistance buildup among insect pests.

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Tarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru, and Xueqian Shi. Guidelines for Evaluating Safety Using Traffic Encounters: Proactive Crash Estimation on Roadways with Conventional and Autonomous Vehicle Scenarios. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317587.

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With the expected arrival of autonomous vehicles, and the ever-increasing levels of automation in today’s human driven vehicles, road safety is changing at a rapid pace. This project aimed to address the need for an efficient and rapid method of safety evaluation and countermeasure identification via traffic encounters, specifically traffic conflicts that are considered useful surrogates of crashes. Recent research-delivered methods for estimating crash frequencies based on these events were observed in the field. In this project we developed a method for observing traffic encounters with two LiDAR-based traffic monitoring units, called TScan, which were recently developed in JTRP-funded projects SPR-3831 and SPR-4102. The TScan units were deployed in the field for several hours to collect data at selected intersections. These large data sets were used to improve object detection and tracking algorithms in order to better assist in detecting traffic encounters and conflicts. Consequently, the software of the TScan trailer-based units was improved and the results generated with the upgraded system include a list of potential encounters for further analysis. We developed an engineering application for analyzing the trajectories of vehicles involved in the pre-selected encounters to identify final traffic encounters and conflicts. Another module of the engineering application visualized the traffic encounters and conflicts to inspect the spatial patterns of these events and to estimate the number of crashes for the observation period. Furthermore, a significant modeling effort resulted in a method of producing factors that expand the conflict-based crash estimates in short observation periods to an entire year. This report provides guidelines for traffic encounters and conflicts, the user manuals for setting up and operating the TScan research unit. and manuals for the engineering applications mentioned above.

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Rafaeli, Ada, Wendell Roelofs, and Anat Zada Byers. Identification and gene regulation of the desaturase enzymes involved in sex-pheromone biosynthesis of pest moths infesting grain. United States Department of Agriculture, 2008. http://dx.doi.org/10.32747/2008.7613880.bard.

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The original objectives of the approved proposal included: 1. Establishment of the biosynthetic pathways for pheromone production using labeled precursors and GC-MS. 2. The elucidation of a circadian regulation of key enzymes in the biosynthetic pathway. 3. The identification, characterization and confirmation of functional expression of the delta-desaturases. 4. The identification of gene regulatory processes involved in the expression of the key enzymes in the biosynthetic pathway. Background to the topic: Moths constitute one of the major groups of pest insects in agriculture and their reproductive behavior is dependent on chemical communication. Sex-pheromone blends are utilized by a variety of moth species to attract conspecific mates. The sex pheromones used are commonly composed of blends of aliphatic molecules that vary in chain length, geometry, degree and position of double bonds and functional groups. They are formed by various actions of specific delta-desaturases to which chain shortening, elongation, reduction, acetylation, and oxidation of a common fatty acyl precursor is coupled. In most of the moth species sex-pheromone biosynthesis is under circadian control by the neurohormone, PBAN (pheromone-biosynthesis-activating neuropeptide). The development of specific and safe insect control strategies utilizing pheromone systems depends on a clear knowledge of the molecular mechanisms involved. In this proposal we aimed at identifying and characterizing specific desaturases involved in the biosynthetic pathway of two moth pest-speciesof stored products, P. interpunctella and S. cerealella, and to elucidate the regulation of the enzymes involved in pheromone biosynthesis. Due to technical difficulties the second stored product pest was excluded from the study at an early phase of the research project. Major conclusions: Within the framework of the planned objectives we confirmed the pheromone biosynthetic pathway of P. interpunctella and H. armigera by using labeled precursor molecules. In addition, in conjunction with various inhibitors we determined the PBAN-stimulated rate-limiting step for these biosynthetic pathways. We thereby present conclusive evidence that the enzyme Acetyl Coenzyme A Carboxylase is activated as a result of PBAN stimulation. We also found that P. interpunctella produce the main pheromone component Z9, E12 Tetradecenyl acetate through the action of a D11 desaturase working on the 16:Acid precursor. This is evidenced by the high amount of incorporation of ²H-labeled 16:Acid into pheromone when compared to the incorporation of ²H-labeled 14:Acid. However, in contrast to reports on other moth species, P. interpunctella is also capable of utilizing the 14:Acid precursor, although to a much lesser extent than the 16:Acid precursor. Despite the discovery of nine different desaturase gene transcripts in this species, from the present study it is evident that although PCR detected all nine gene transcripts, specific to female pheromone glands, only two are highly expressed whereas the other 7 are expressed at levels of at least 10⁵ fold lower showing very low abundance. These two genes correspond to D11-like desaturases strengthening the hypothesis that the main biosynthetic pathway involves a D11 desaturase.

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Gottlieb, Yuval, Bradley Mullens, and Richard Stouthamer. investigation of the role of bacterial symbionts in regulating the biology and vector competence of Culicoides vectors of animal viruses. United States Department of Agriculture, 2015. http://dx.doi.org/10.32747/2015.7699865.bard.

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Symbiotic bacteria have been shown to influence host reproduction and defense against biotic and abiotic stressors, and this relates to possible development of a symbiont-based control strategy. This project was based on the hypothesis that symbionts have a significant impact on Culicoides fitness and vector competence for animal viruses. The original objectives in our proposal were: 1. Molecular identification and localization of the newly-discovered symbiotic bacteria within C. imicola and C. schultzei in Israel and C. sonorensis in California. 2. Determination of the prevalence of symbiotic bacteria within different vector Culicoides populations. 3. Documentation of specific symbiont effects on vector reproduction and defense: 3a) test for cytoplasmic incompatibility in Cardinium-infected species; 3b) experimentally evaluate the role of the symbiont on infection or parasitism by key Culicoides natural enemies (iridescent virus and mermithid nematode). 4. Testing the role(s) of the symbionts in possible protection against infection of vector Culicoides by BTV. According to preliminary findings and difficulties in performing experimental procedures performed in other insect symbiosis systems where insect host cultures are easily maintained, we modified the last two objectives as follows: Obj. 3, we tested how symbionts affected general fitness of Israeli Culicoides species, and thoroughly described and evaluated the correlation between American Culicoides and their bacterial communities in the field. We also tried alternative methods to test symbiont-Culicoides interactions and launched studies to characterize low-temperature stress tolerances of the main US vector, which may be related to symbionts. Obj. 4, we tested the correlation between EHDV (instead of BTV) aquisition and Cardinium infection. Culicoides-bornearboviral diseases are emerging or re-emerging worldwide, causing direct and indirect economic losses as well as reduction in animal welfare. One novel strategy to reduce insects’ vectorial capacity is by manipulating specific symbionts to affect vector fitness or performance of the disease agent within. Little was known on the bacterial tenants occupying various Culicoides species, and thus, this project was initiated with the above aims. During this project, we were able to describe the symbiont Cardinium and whole bacterial communities in Israeli and American Culicoides species respectively. We showed that Cardinium infection prevalence is determined by land surface temperature, and this may be important to the larval stage. We also showed no patent significant effect of Cardinium on adult fitness parameters. We showed that the bacterial community in C. sonorensis varies significantly with the host’s developmental stage, but it varies little across multiple wastewater pond environments. This may indicate some specific biological interactions and allowed us to describe a “core microbiome” for C. sonorensis. The final set of analyses that include habitat sample is currently done, in order to separate the more intimately-associated bacteria from those inhabiting the gut contents or cuticle surface (which also could be important). We were also able to carefully study other biological aspects of Culicoides and were able to discriminate two species in C. schultzei group in Israel, and to investigate low temperature tolerances of C. sonorensis that may be related to symbionts. Scientific implications include the establishment of bacterial identification and interactions in Culicoides (our work is cited in other bacteria-Culicoides studies), the development molecular identification of C. schultzei group, and the detailed description of the microbiome of the immature and matched adult stages of C. sonorensis. Agricultural implications include understanding of intrinsic factors that govern Culicoides biology and population regulation, which may be relevant for vector control or reduction in pathogen transmission. Being able to precisely identify Culicoides species is central to understanding Culicoides borne disease epidemiology.

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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, 2001. http://dx.doi.org/10.32747/2001.7575271.bard.

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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.

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