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      Nonconventional Weed Management Strategies for Modern Agriculture

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      Weed Science
      Weed Science Society

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          Abstract

          Weeds are a significant problem in crop production and their management in modern agriculture is crucial to avoid yield losses and ensure food security. Intensive agricultural practices, changing climate, and natural disasters affect weed dynamics and that requires a change in weed management protocols. The existing manual control options are no longer viable because of labor shortages; chemical control options are limited by ecodegradation, health hazards, and development of herbicide resistance in weeds. We are therefore reviewing some potential nonconventional weed management strategies for modern agriculture that are viable, feasible, and efficient. Improvement in tillage regimes has long been identified as an impressive weed-control measure. Harvest weed seed control and seed predation have been shown as potential tools for reducing weed emergence and seed bank reserves. Development in the field of allelopathy for weed management has led to new techniques for weed control. The remarkable role of biotechnological advancements in developing herbicide-resistant crops, bioherbicides, and harnessing the allelopathic potential of crops is also worth mentioning in a modern weed management program. Thermal weed management has also been observed as a useful technique, especially under conservation agriculture systems. Last, precision weed management has been elaborated with sufficient details. The role of remote sensing, modeling, and robotics as an integral part of precision weed management has been highlighted in a realistic manner. All these strategies are viable for today's agriculture; however, site-specific selection and the use of right combinations will be the key to success. No single strategy is perfect, and therefore an integrated approach may provide better results. Future research is needed to explore the potential of these strategies and to optimize them on technological and cultural bases. The adoption of such methods may improve the efficiency of cropping systems under sustainable and conservation practices.

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          Most cited references123

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          The application of small unmanned aerial systems for precision agriculture: a review

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            Autonomous robotic weed control systems: A review

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              Is Open Access

              Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images

              The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r2=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (<5% weeds) was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance.
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                Author and article information

                Journal
                applab
                Weed Science
                Weed sci.
                Weed Science Society
                0043-1745
                1550-2759
                December 2015
                January 2017
                : 63
                : 04
                : 723-747
                Article
                10.1614/WS-D-15-00064.1
                494b96c2-7d69-4041-937f-a17ecedf7bb8
                © 2015
                History

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