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The discipline of weed science is at a critical juncture. Decades of efficient chemical weed control have led to a rise in the number of herbicide-resistant weed populations, with few new herbicides with unique modes of action to counter this trend and often no economical alternatives to herbicides in large-acreage crops. At the same time, the world population is swelling, necessitating increased food production to feed an anticipated 9 billion people by the year 2050. Here, we consider these challenges along with emerging trends in technology and innovation that offer hope of providing sustainable weed management into the future. The emergence of natural product leads in discovery of new herbicides and biopesticides suggests that new modes of action can be discovered, while genetic engineering provides additional options for manipulating herbicide selectivity and creating entirely novel approaches to weed management. Advances in understanding plant pathogen interactions will contribute to developing new biological control agents, and insights into plant-plant interactions suggest that crops can be improved by manipulating their response to competition. Revolutions in computing power and automation have led to a nascent industry built on using machine vision and global positioning system information to distinguish weeds from crops and deliver precision weed control. These technologies open multiple possibilities for efficient weed management, whether through chemical or mechanical mechanisms. Information is also needed by growers to make good decisions, and will be delivered with unprecedented efficiency and specificity, potentially revolutionizing aspects of extension work. We consider that meeting the weed management needs of agriculture by 2050 and beyond is a challenge that requires commitment by funding agencies, researchers, and students to translate new technologies into durable weed management solutions. Integrating old and new weed management technologies into more diverse weed management systems based on a better understanding of weed biology and ecology can provide integrated weed management and resistance management strategies that will be more sustainable than the technologies that are now failing.

Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants.This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance.

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Herbicide-resistant weeds were first reported in the 1950s and the number of weeds resistant to existing herbicides has grown over time. At the same time, Global food demand continues to increase and the regulatory requirements for new agricultural products shift and expand. To address these challenges, one approach Dow AgroSciences is pursuing is the discovery of herbicides with novel modes of action. Using an imidazole carboxylic acid herbicide hit as a case study, the process of herbicide discovery, addressing mode of action concerns, and the optimization of chemical structures will be presented.

Fmit firmness measurement is a good way to monitor fmit softening and to predict bmising damage during harvest and postharvest handling. Ripening protocols traditionally utilize a destmctive penetrometer-type fmit firmness measure to monitor ripening. Until recently, methods of assessing fmit texture properties nondestmctively were not commercially available. The nondestmctive Sinclair iQ™ firmness tester was investigated to monitor ripening and predict bmising susceptibility in stone fmit. This work was carried out on four peach, three plum, and five nectarine cultivars over two seasons. The correlations between destmctive and nondestmctive firmness measurements were significant (p-value = 0.0001), although too low for commercial applications as they varied from ^ = 0.60-0.71 according to fmit type. Using a different approach, the relationship between destmctive and nondestmctive firmness measures was characterized in terms of segregating these fmit according to their stages of ripening. This was done by using discriminant analysis (66-90% agreement in ripeness stage classification was observed in validation tests). Discriminant analysis consistently segregated nondestmctive firmness measured fmit into commercially important classes ("ready to eat", "ready to buy", "mature and immature"). These represented key ripening stages with different bmising potentials and consumer acceptance. This work points out the importance to relate nondestmctive measurements directly to important commercial physiological stages rather than to correlate them with the current standard penetrometer valúes. Thus, destmctive and nondestmctive firmness measurements can be directly used to identify the stage of ripeness and potential susceptibility to bmising during postharvest changes. Further work is recommended to evalúate the performance of this nondestmctive sensor in segregating fmit according to their stage of ripeness under packinghouse or processing plant conditions.

A nondestructive optical method for determining the soluble solids content of fresh whole tomatoes was investigated. The method, based upon near infrared spectrophotometric techniques, could predict the soluble solids content of tomatoes (r ϭ 0.92, SEC ϭ 0.27ЊBrix). Tomatoes from more than 30 popular fresh market cultivars at stages of maturity from mature green to ripe red fruit were studied.

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