Download or read book An Assessment of the Use of Site specific Weed Control for Improving Prediction based Management Decisions and Automating On farm Research written by Edward Charles Luschei and published by . This book was released on 2001 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dissertation Abstracts International written by and published by . This book was released on 2001 with total page 790 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Development of a Site specific Herbicide Application Decision Support System written by Wade Alexander Givens and published by . This book was released on 2007 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sampling techniques and interpolation methods were compared to assess the accuracy of each pattern/method combination. Neither the pattern nor method impacted the results of the predicted average values for a given weed species.

Download or read book Automation The Future of Weed Control in Cropping Systems written by Stephen L. Young and published by Springer Science & Business Media. This book was released on 2013-11-21 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technology is rapidly advancing in all areas of society, including agriculture. In both conventional and organic systems, there is a need to apply technology beyond our current approach to improve the efficiency and economics of management. Weeds, in particular, have been part of cropping systems for centuries often being ranked as the number one production cost. Now, public demand for a sustainably grown product has created economic incentives for producers to improve their practices, yet the development of advanced weed control tools beyond biotech has lagged behind. An opportunity has been created for engineers and weed scientists to pool their knowledge and work together to ‘fill the gap’ in managing weeds in crops. Never before has there been such pressure to produce more with less in order to sustain our economies and environments. This book is the first to provide a radically new approach to weed management that could change cropping systems both now and in the future.

Download or read book American Doctoral Dissertations written by and published by . This book was released on 2000 with total page 816 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book DEVELOPMENT OF A SITE SPECIFIC HERBICIDE APPLICATION DECISION SUPPORT SYSTEM written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Weeds typically grow in patches across agricultural landscapes. Because of this characteristic growth pattern, it seems logical to apply herbicides site-specifically to control them. To do this effectively, methods must be identified to accurately map weed presence and make cost effective herbicide application decisions to control them. The primary objective of this research was to develop a site-specific herbicide decision support system. Additional objectives include evaluating the effects of sampling patterns and interpolation techniques for weed mapping accuracy and evaluating texture analysis for weed patch detection in row-crops. A geographic information system (GIS) extension was developed to work in conjunction with a commercial software component for calculating site-specific herbicide applications based on user input weed maps. Results of the extension were compared to that of the commercial software. The GIS extension was able to accurately develop herbicide options based on the given weed densities and potential net return for treatment of the weeds in any specific area of the field. Sampling techniques and interpolation methods were compared to assess the accuracy of each pattern/method combination. The patterns used in this study were the W- and Z-shaped pattern, and the interpolation methods used were kriging and inverse distance weighted. Neither the pattern nor method impacted the results of the predicted average values for a given weed species. The last objective addressed was texture analysis? ability to distinguish weed patches in row-crops. Texture analysis was also tested to determine its ability to distinguish between areas requiring a herbicide application and areas not requiring a herbicide application. The analysis was performed on 3 vegetative indices and the NIR band of multispectral imagery at three different spatial resolutions (0.14 m, 0.5 m, and 1 m), and for two dates in the growing season. Texture analysis performed better on late s.

Download or read book Decision Support Systems for Weed Management written by Guillermo R. Chantre and published by Springer Nature. This book was released on 2020-07-31 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: Weed management Decision Support Systems (DSS) are increasingly important computer-based tools for modern agriculture. Nowadays, extensive agriculture has become highly dependent on external inputs and both economic costs, as well the negative environmental impact of agricultural activities, demands knowledge-based technology for the optimization and protection of non-renewable resources. In this context, weed management strategies should aim to maximize economic profit by preserving and enhancing agricultural systems. Although previous contributions focusing on weed biology and weed management provide valuable insight on many aspects of weed species ecology and practical guides for weed control, no attempts have been made to highlight the forthcoming importance of DSS in weed management. This book is a first attempt to integrate `concepts and practice’ providing a novel guide to the state-of-art of DSS and the future prospects which hopefully would be of interest to higher-level students, academics and professionals in related areas.

Download or read book Developing a Technique for Evaluating Weed specific Mapping Systems written by and published by . This book was released on 2007 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: Federal regulation and public awareness of agricultural chemical use have fueled precision agriculture research for the last decade. An extensive body of research on potential reduction of herbicide inputs by automated patch-spraying or site-specific management has developed. Two dominant methods have developed for site-specific application of herbicide. Map-based systems use predefined application maps to direct herbicide application and sensor-based systems use real-time weed sensors to identify and treat weeds as the sprayer moves through the field. Weed maps, generated for map-based application of herbicide are beneficial for out-of-field decision-making but are labor intensive to create and sensitive to many types of sampling errors. Real-time sensor-based systems are not as labor-intensive but have historically made no record of what parts of the field received herbicide and are subject to weed discrimination errors. The University of Tennessee Weed Mapping System (UTWMS) is made up of a digital event recorder and a WeedSeeker discrete herbicide application system. The overarching objective of this study was to evaluate the UTWMS under field conditions. Specific objectives included the use of georeferenced manually-sampled plots for evaluation of map accuracy; development of an automated documentation system for quantifying hits, misses, and false triggers of a real-time sensor-based spraying system; updating the logging software of the UTWMS to include a count of spray transitions; and investigate potentials for reducing number of sensors to reflect the existing spatial correlation of weeds. Manually sampled subplots at one-meter resolution did not correlate with weed maps and only weakly correlated when averaged by plot (8x30m). A video documentation system was successfully developed for evaluating discrimination accuracy of sensor-based sprayers. While investigating sensor resolution reduction to reflect spatial correlation of weeds, a sensor was replaced with a conditionally triggered solenoid valve during a simulation. More than 75% of the simulated weeds were accurately sprayed for all four conditional scenarios tested. A software modification to the UTWMS provided enumeration of spray transitions for weed scientist to investigate weed distribution during "percent time on" integration. The update rate of the GPS unit in the UTWMS should be increased if weed maps are to be representative of small research subplots.

Download or read book AI in Agriculture for Sustainable and Economic Management written by Sirisha Potluri and published by CRC Press. This book was released on 2024-08-01 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains the best practices and their respective outcomes in artificial intelligence (AI) to meet sustainable development goals and demands. It examines the practices, technologies, and innovations at the core of various research issues to meet the sustainable development demands in agriculture to balance social, economic, and environmental sustainability with AI. AI in Agriculture for Sustainable and Economic Management discusses AI-driven nanotechnology approaches for precision agriculture and solutions for the optimization of farming resources and their management. The authors examine the impact of AI in agriculture and how technology-driven sustainable farming with smart waste-water treatment for zero waste for the circular economy can extend crop shelf-life. It discusses how AI expertise can be advantageous to envisage and evaluate the increasing demands of productivity, and to help to maintain ecosystems and strengthen the capacity for crop adaptation in response to drastic changes in climate and weather, natural disasters, and other significant factors. These findings and practices are also useful to emphasize how an agricultural ecosystem can be advanced and industrialized so that it can aid not only large commercial farms but also smaller farmlands. Finally, it also discusses how AI practices will help to find a balance between the volume of food manufactured and the proper maintenance of the ecosystem. This book is intended for researchers and upper graduate students interested in artificial intelligence in agricultural engineering, AI advances in crop science and technology for sustainable development.

Download or read book Hyperspectral Vision based Machine Learning for Robust Plant Recognition in Autonomous Weed Control written by Yun Zhang and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: While herbicide application and mechanical cultivation remain the primary means for weed control in agricultural production, the only solution to-date for weed control within close proximity of crop plants in the seedline is hand hoeing. To reduce manual labor cost and minimize herbicide usage for organic farming, this research developed an intelligent robotic system for automated weed detection and control within the seedline. The system utilized visible and near infrared (NIR) reflectance-based features in hyperspectral images of plant foliage for real-time species recognition. This technique is less computationally intensive than the shape- and texture-based machine vision methods. For real-time, in-field applications, this technique is superior to traditional shape-based machine vision, as it does not require singulation of individual plants, and is robust to visual occlusion and less susceptible to leaf morphological variation or damage. A principle challenge of reflectance-based species recognition has been that the optical properties of plant foliage are functions of external growing conditions, and are greatly impacted by the variability in natural environment of agricultural fields. Prior studies of using visible and NIR spectroscopy for plant recognition were restricted to reflectance spectra measured on plants grown in a single season with exposure to a single environmental condition. This work, for the first time, demonstrated the potential of hyperspectral imaging technology for plant species identification under varying external factors of growing temperature, soil moisture and sunlight intensity, as well as over three multiple seasons of natural field environment. This work also developed adaptive learning techniques that mitigated environmental effects and provided solutions to robust plant recognition across the variation in the studied single conditions and seasons. Finally, the machine vision system was coupled with a thermal micro-dosing application system and validated under outdoor conditions for real-time automated weed control with heated food-grade oil. This research provided a complete solution to automated weed control in row crops using machine vision reflectance-based plant recognition. The technical results of this research are summarized as below: Multispectral Bayesian classifiers were developed for distinguishing tomatoes among black nightshade and pigweed. The effects of variation in the three single environmental factors of temperature, soil moisture, and solar irradiance, on spectroscopy-based plant recognition demonstrated that (i) the optimum performance, ranging from 88.2% to 95.3%, occurred when the models were applied in same environmental conditions as represented in training; (ii) increasing the deviation of the validation conditions from the calibration conditions degraded the performance to 62.5-81.7%; (iii) environmental stress made the plant species more distinguishable and slightly improved the overall accuracy by 1.3-6.9% for same condition applications; (iv) the Bayesian classifiers optimized for the normal conditions demonstrated more robust plant recognition over environmental variations. An environmentally-adaptive machine learning algorithm was developed for automatic site-specific recalibration of a Bayesian classifier in a dynamic environment. Validation performance of the classifier demonstrated that site-specific recalibration can be implemented by establishing the models exclusively with a fraction of new data (approximately 30 to 80 plants) without adverse impact due to ignoring the old data originally used to train the model. This method alleviated the bias produced by single-condition calibration and, overall, it improved the classification rates to 90.4-94.5% across variation in the three studied environmental factors. Global calibration was another approach investigated to improve robustness of the Bayesian classifiers to varying environmental conditions of the three studied factors. The overall classification rates of global classifiers ranged from 90.0% to 93.0% and the performance stability was also improved. Global calibration was recommended as it was able to provide robust classification performances across variation in the three studied environmental factors and was comparable to the optimal results obtained when the single-condition models were cross-validated on their training conditions. However, global calibration was not superior when used to discriminate tomatoes among weeds over three seasons (2005, 2006 and 2008), in which the plants were grown in a natural agricultural field environment. To improve the seasonal stability of plant recognition, a multiclassifier system was developed by integrating expert knowledge from historical data that most closely matched the new field environment. This method improved the performance of the global model by 10.5% (from 85.0% to over 95.5%) and provided an innovative direction for achieving robust plant recognition over the variation in field environment of multiple seasons. In an outdoor test of the complete weed control system, the hyperspectral vision system correctly mapped, by species, 91.0% of plant canopy and the thermal micro-dosing system then delivered preheated (160°C) food-grade oil exclusively to the targeted weed foliage. Fifteen days post application, the system successfully controlled 95.8% of black nightshade and 93.8% of pigweed; while only 2.4% of tomato was damaged to the extent of non-viability due to inadvertent spray.

Download or read book Integrated Weed and Soil Management written by J. L. Hatfield and published by CRC Press. This book was released on 1997-11-01 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrated Weed and Soil Management explores the connection between soil science and weed control, providing the latest research and applications for weed management in agricultural systems. Five major areas discussed include: surface residue, tillage, and weed and soil management integration of soil and weed management to reduce environmental degradation modeling weed emergence, interference, and management new technology and management development of the next generation of weed management systems Throughout the text, the editors and contributors replace weed control terminology with weed management terminology, shifting the paradigm of one of control to one of better management. Integrated Weed and Soil Management, a great reference for higher research, improves your understanding of soil science, weed biology, and ecology - leading to effective practical application and maximum results.

Download or read book Agricultural Internet of Things and Decision Support for Precision Smart Farming written by Annamaria Castrignano and published by Academic Press. This book was released on 2020-01-09 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Agricultural Internet of Things and Decision Support for Smart Farming reveals how a set of key enabling technologies (KET) related to agronomic management, remote and proximal sensing, data mining, decision-making and automation can be efficiently integrated in one system. Chapters cover how KETs enable real-time monitoring of soil conditions, determine real-time, site-specific requirements of crop systems, help develop a decision support system (DSS) aimed at maximizing the efficient use of resources, and provide planning for agronomic inputs differentiated in time and space. This book is ideal for researchers, academics, post-graduate students and practitioners who want to embrace new agricultural technologies. Presents the science behind smart technologies for agricultural management Reveals the power of data science and how to extract meaningful insights from big data on what is most suitable based on individual time and space Proves how advanced technologies used in agriculture practices can become site-specific, locally adaptive, operationally feasible and economically affordable

Download or read book Infrastructure Possibilities and Human Centered Approaches With Industry 5 0 written by Khan, Mohammad Ayoub and published by IGI Global. This book was released on 2024-01-25 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Infrastructure Possibilities and Human-Centered Approaches With Industry 5.0 is a research book that serves as a comprehensive exploration of the potential impact of Industry 5.0 and the research opportunities presented by it, a new era of industrial revolution that integrates advanced technologies with human expertise and creativity. This book delves into the transformative effects of Industry 5.0 on society, with a particular focus on human-centric approaches and the key areas of agriculture, transportation, healthcare, and more. The book examines the revolutionary impact of Industry 5.0 in various domains. It explores the application of AI and machine learning in revolutionizing agriculture, improving livestock management, optimizing fertilizer usage, and detecting agricultural diseases. Additionally, it delves into the integration of advanced technologies in healthcare, including wearable devices, sensors, and robotics, to provide personalized and efficient healthcare services. Furthermore, the book explores the implications of Industry 5.0 on transportation, smart grid systems, and education. Throughout the discussion, the book addresses the ethical and social considerations associated with Industry 5.0, such as privacy, data protection, and social inequality. Written for research scholars, graduate engineering students, and postgraduate students in the fields of computer science, agriculture, and health engineering, this book serves as a valuable resource for understanding the transformative potential of Industry 5.0.

Download or read book Remote Sensing in Precision Agriculture written by Salim Lamine and published by Elsevier. This book was released on 2023-10-20 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: Remote Sensing in Precision Agriculture: Transforming Scientific Advancement into Innovation compiles the latest applications of remote sensing in agriculture using spaceborne, airborne and drones’ geospatial data. The book presents case studies, new algorithms and the latest methods surrounding crop sown area estimation, determining crop health status, assessment of vegetation dynamics, crop diseases identification, crop yield estimation, soil properties, drone image analysis for crop damage assessment, and other issues in precision agriculture. This book is ideal for those seeking to explore and implement remote sensing in an effective and efficient manner with its compendium of scientifically and technologically sound information. Presents a well-integrated collection of chapters, with quality, consistency and continuity Provides the latest RS techniques in Precision Agriculture that are addressed by leading experts Includes detailed, yet geographically global case studies that can be easily understood, reproduced or implemented Covers geospatial data, with codes available through shared links

Download or read book Evaluation of Weed Populations Under the Influence of Site specific Weed Control to Derive Decision Rules for a Sustainable Weed Management written by Carina Ritter and published by . This book was released on 2008 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Precision Weed Management in Crops and Pastures written by Richard William Medd and published by . This book was released on 1998 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Physical and Allelochemical Cover Crop Effects for Weed Suppression written by Alexander John Hewitt and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Winter annual weeds can delay soil warming, inhibit planting operations, and compete for water and nutrients resulting in yield loss of spring planted cash crop. Understanding the timing and extent of weed emergence in different cropping systems is important to producers to be able to predict occurrence and to better manage weeds. The first objective of this research was to model the emergence of winter annual weed species in two different cropping systems based on the accumulation of thermal time. Results show that winter annual weed species composition and emergence timing can vary significantly between locations and are highly site-specific. Certain weeds such as henbit had predictable and consistent emergence timings across years in a no-tillage system in eastern Kansas but was more variable in southeast Kansas. This information can be used by farmers for weed management decisions, such as timing of control methods. The use of cover crop monocultures and mixes were evaluated for their physical and chemical weed suppressive capabilities. The second objective was to assess the levels of physical weed suppression by each cover crop treatment through weed biomass and weed density at the time of cover crop harvest. Cover crop monocultures and mixes composed entirely or mostly of aggressive grass species were found to be the most weed suppressive due to their high biomass accumulation. Certain varieties of cereal rye, annual ryegrass, winter oat, and mixes containing oat and ryegrass were found to be the highest biomass producers. Overall, cover crops provided superior weed control relative to a fallow with herbicide treatment that had no residual activity. Fertility regimes can impact cover crop biomass production and influence their allelopathic potential. The third objective was to investigate the role of nitrogen and sulfur fertilizers on cover crop weed suppression through allelopathy by conducting a weed seed germination bioassay. The results indicate that higher amounts of cover crop residues can potentially result in greater levels of weed suppression through inhibition of seed germination. Increasing soil fertility may decrease the allelopathic potential of cover crops, but can increase their biomass production, still resulting in adequate weed control.