Multi-format open-source weed image dataset for real-time weed identification in precision agriculture.

Weeds are considered obnoxious and a hindrance to crop yield. Due to their uneven spatial distribution pattern, a ground or aerial robot are deployed to spot spray herbicides. This herbicidal application depends entirely on the computer vision algorithms that assist with in-field weed identification prior to spot spraying. Therefore, to develop advanced computer vision algorithms, big data pertaining to agricultural weed dataset are required. In the past, public domain weed dataset have been released but mostly acquired using ground-based technologies. The dataset discussed in this paper is unique in that it incorporates data captured both from handheld camera and unmanned aerial system (UAS), thus catering to both ground-based and aerial-based weeding robots. This dataset comprises of 3,975 images featuring five different weed species commonly found in North Dakota: kochia (Bassia scoparia), common ragweed (Ambrosia artemisiifolia), horseweed (Erigeron canadensis), redroot pigweed (Amaranthus retroflexus), and waterhemp (Amaranthus tuberculatus). These images have been meticulously annotated in various formats to facilitate the development and advancements of computer vision algorithms. Furthermore, various augmentation techniques have been applied to ensure that the dataset closely represents the real-world field conditions. Additionally, this dataset is open-source to assist precision weeding technologies for real-time in-field weed identification followed by herbicidal spot spraying application, ultimately contributing to more efficient and sustainable agricultural practices.

Towards reducing chemical usage for weed control in agriculture using UAS imagery analysis and computer vision techniques.

Currently, applying uniform distribution of chemical herbicide through a sprayer without considering the spatial distribution information of crops and weeds is the most common method of controlling weeds in commercial agricultural production system. This kind of weed management practice lead to excessive amounts of chemical herbicides being applied in a given field. The objective of this study was to perform site-specific weed control (SSWC) in a corn field by: (1) using a unmanned aerial system (UAS) to map the spatial distribution information of weeds in the field; (2) creating a prescription map based on the weed distribution map, and (3) spraying the field using the prescription map and a commercial size sprayer. In this study, we assumed that plants growing outside the corn rows are weeds and they need to be controlled. The first step in implementing such an approach is identifying the corn rows. For that, we are proposing a Crop Row Identification algorithm, a computer vision algorithm that identifies corn rows on UAS imagery. After being identified, the corn rows were then removed from the imagery and remaining vegetation fraction was classified as weeds. Based on that information, a grid-based weed prescription map was created and the weed control application was implemented through a commercial-size sprayer. The decision of spraying herbicides on a particular grid was based on the presence of weeds in that grid cell. All the grids that contained at least one weed were sprayed, while the grids free of weeds were not. Using our SSWC approach, we were able to save 26.2% of the acreage from being sprayed with herbicide compared to the current method. This study presents a full workflow from UAS image collection to field weed control implementation using a commercial size sprayer, and it shows that some level of savings can potentially be obtained even in a situation with high weed infestation, which might provide an opportunity to reduce chemical usage in corn production systems.

Inefficiency of anthraquinone-based avian repellents when applied to sunflower: the importance of crop vegetative and floral characteristics in field applications.

BACKGROUND: Blackbirds (Icteridae) cause significant damage to sunflower (Helianthus annuus L.) prompting the need for effective management tools. Anthraquinone-based repellents can reduce feeding by > 80% in laboratory settings, but require birds to learn the negative association through repellent ingestion. We evaluated an anthraquinone-based repellent applied directly to mature sunflower plants for its ability to reduce bird damage. We used captive male red-winged blackbirds (Agelaius phoeniceus) to evaluate efficacy of two anthraquinone-based formulations in varying concentrations and applied in a manner attainable by sunflower producers. We also assessed field application methods for repellent coverage and anthraquinone residues when using ground-rigs equipped with drop-nozzles situated below the crop canopy. RESULTS: The repellents failed to reduce feeding and birds did not exhibit a preference between untreated and treated sunflowers at concentrations 2.7× the suggested application rate (i.e. 9.35 L ha-1 of repellent). In the absence of disk flowers, which obstruct repellent from reaching the achenes, the repellents failed to reduce consumption. Anthraquinone concentrations in field applications were considerably less than those in the laboratory experiments and did not reduce bird damage. CONCLUSION: Efficacy is difficult to achieve in the field due to application issues where growth patterns and floral components of sunflower limit residues on achenes, thus contact with foraging birds. Although field residues could be improved by increasing anthraquinone concentrations in tank mixtures and decreasing droplet size, repellents optimized for loose achenes are inefficient in reducing avian consumption of sunflower when applied to intact plants in a manner representative of commercial agriculture. © 2020 Society of Chemical Industry.

Development and characterization of mutant winter wheat (Triticum aestivum L.) accessions resistant to the herbicide quizalofop.

KEY MESSAGE: New herbicide resistance traits in wheat were produced through the use of induced mutagenesis. While herbicide-resistant crops have become common in many agricultural systems, wheat has seen few introductions of herbicide resistance traits. A population of Hatcher winter wheat treated with ethyl methanesulfonate was screened with quizalofop to identify herbicide-resistant plants. Initial testing identified plants that survived multiple quizalofop applications. A series of experiments were designed to characterize this trait. In greenhouse studies the mutants exhibited high levels of quizalofop resistance compared to non-mutant wheat. Sequencing ACC1 revealed a novel missense mutation causing an alanine to valine change at position 2004 (Alopecurus myosuroides reference sequence). Plants carrying single mutations in wheat's three genomes (A, B, D) were identified. Acetyl co-enzyme A carboxylase in resistant plants was 4- to 10-fold more tolerant to quizalofop. Populations of segregating backcross progenies were developed by crossing each of the three individual mutants with wild-type wheat. Experiments conducted with these populations confirmed largely normal segregation, with each mutant allele conferring an additive level of resistance. Further tests showed that the A genome mutation conferred the greatest resistance and the B genome mutation conferred the least resistance to quizalofop. The non-transgenic herbicide resistance trait identified will enhance weed control strategies in wheat.

What have the mechanisms of resistance to glyphosate taught us?

The intensive use of glyphosate alone to manage weeds has selected populations that are glyphosate resistant. The three mechanisms of glyphosate resistance that have been elucidated are (1) target-site mutations, (2) gene amplification and (3) altered translocation due to sequestration. What have we learned from the selection of these mechanisms, and how can we apply those lessons to future herbicide-resistant crops and new mechanisms of action? First, the diversity of glyphosate resistance mechanisms has helped further our understanding of the mechanism of action of glyphosate and advanced our knowledge of plant physiology. Second, the relatively rapid evolution of glyphosate-resistant weed populations provides further evidence that no herbicide is invulnerable to resistance. Third, as new herbicide-resistant crops are developed and new mechanisms of action are discovered, the weed science community needs to ensure that we apply the lessons we have learned on resistance management from the experience with glyphosate. Every new weed management system must be evaluated during development for its potential to select for resistance, and stewardship programs should be in place when the new program is introduced.