Precision weed management, an application of precision agriculture, accounts for within-field variability of weed infestation and herbicide damage. Unmanned aerial vehicles (UAVs) provide a unique platform for remote sensing of field crops. They are more efficient and flexible than manned agricultural airplanes in acquiring high-resolution images at low altitudes and low speeds. UAVs are more universal than agricultural aircraft, because the latter are used only in specific regions. We have developed and used UAV systems for red–green–blue digital and color–infrared imaging over crop fields to identify weed species, determine crop injury from dicamba at different doses, and detect naturally grown glyphosate-resistant weeds. This article presents remote sensing technologies for weed management and focuses on development and application of UAV-based low-altitude remote sensing technology for precision weed management. In particular, this article futher discusses the potential application of UAV-based plant-sensing systems for mapping the distributions of glyphosate-resistant and glyphosate-susceptible weeds in crop fields.

Traditional field survey methods for detection of water leaks in irrigation canal systems are costly and timeconsuming. In this study, a rapid, cost-effective method was developed for identifying irrigation canal locations likely to have leaks and/or seepage. The method involves the use of a multispectral imager equipped with red, near infrared, and thermal sensors which is mounted on an aircraft and flown at low altitude to collect the images. A three-step process, image acquisition, image processing, and field reconnaissance, was developed for processing the imagery and identification of locations likely to have leaks. The method was evaluated in the Lower Rio Grande Valley of Texas, USA. Images were collected of 24 selected canal segments within 11 irrigation districts in this region. Evaluation of the imagery indicated that 140 sites had possible canal leakage problems (point leak and/or seepage). A field site evaluation form was developed and used to document the type and severity of the leaks at 28 of the sites. Twenty-six sites were confirmed to have leaks, representing a success rate of 93%. The methods used in this study should have widespread application for detecting leaks and seepage in irrigation canals. Les méthodes utilisées dans cette étude devraient avoir une application généralisée de détection des fuites et des infiltrations dans les canaux d'irrigation.

GR and GS Palmer amaranth plants have unique hyperspectral reflectance properties, and there are four distinct regions of the spectrum that can separate the GR from GS plants. These results demonstrate that hyperspectral imaging has potential application to distinguish GR from GS Palmer amaranth plants (without a glyphosate treatment), with future implications for glyphosate resistance management. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

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Weed management is a major component of a soybean (Glycine max L.) production system; thus, managers need tools to help them distinguish soybean from weeds. Vegetation indices derived from light reflectance properties of plants have shown promise as tools to enhance differences among plants. The objective of this study was to evaluate normalized difference vegetation indices derived from multispectral leaf reflectance data as input into random forest machine learner to differentiate soybean and three broad leaf weeds: Palmer amaranth (Amaranthus palmeri L.), redroot pigweed (A. retroflexus L.), and velvetleaf (Abutilon theophrasti Medik). Leaf reflectance measurements were acquired from plants grown in two separate greenhouse experiments conducted in 2014. Twelve normalized difference vegetation indices were derived from the reflectance measurements, including advanced, green, greenred, green-blue, and normalized difference vegetation indices, shortwave infrared water stress indices, normalized difference pigment and red edge indices, and structure insensitive pigment index. Using the twelve vegetation indices as input variables, the conditional inference version of random forest (cforest) readily distinguished soybean and velvetleaf from the two pigweeds (Palmer amaranth and redroot pigweed) and from each other with classification accuracies ranging from 93.3% to 100%. The greatest errors were observed between the two pigweed classes, with classification accuracies ranging from 70% to 93.3%. Results suggest combining them into one class to increase classification accuracy. Vegetation indices results were equivalent to or slightly better than results obtained with sixteen multispectral bands used as input data into cforest. This research further supports using vegetation indices and machine learning algorithms such as cforest as decision support tools for weed identification.

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QuickBird multispectral satellite imagery was evaluated for distinguishing giant salvinia (Salvinia molesta Mitchell) in a large reservoir in east Texas. The imagery had four bands (blue, green, red, and near-infrared) and contained 11-bit data. Color-infrared (green, red, and near-infrared bands), normal color (blue, green and red bands), and four-band composite (blue, green, red, and near-infrared bands) images were studied. Unsupervised image analysis was used to classify the imagery. Accuracy assessments performed on the classification maps of the three composite images had producer's and user's accuracies for giant salvinia ranging from 87.8 to 93.5%. Color-infrared, normal color, and four-band satellite imagery were excellent for distinguishing giant salvinia in a complex field habitat.