Weed Management in Cranberries: A Historical Perspective and a Look to the Future

Sandler, Hilary A.

doi:10.3390/agriculture8090138

Cited by 6 publications

(7 citation statements)

References 46 publications

(40 reference statements)

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“…Many weed scientists have seen the potential of UAS and are currently working on projects to build weed maps for various agricultural crops, such as wheat and cranberry [47,48]. In 2013, a research article was published that assessed the optimal flight conditions (e.g., altitude and quantity of GCPs) needed for weed detection in young wheat fields.…”

Section: Weed Managementmentioning

confidence: 99%

Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

2019

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Numerous sensors have been developed over time for precision agriculture; though, only recently have these sensors been incorporated into the new realm of unmanned aircraft systems (UAS). This UAS technology has allowed for a more integrated and optimized approach to various farming tasks such as field mapping, plant stress detection, biomass estimation, weed management, inventory counting, and chemical spraying, among others. These systems can be highly specialized depending on the particular goals of the researcher or farmer, yet many aspects of UAS are similar. All systems require an underlying platform—or unmanned aerial vehicle (UAV)—and one or more peripherals and sensing equipment such as imaging devices (RGB, multispectral, hyperspectral, near infra-red, RGB depth), gripping tools, or spraying equipment. Along with these wide-ranging peripherals and sensing equipment comes a great deal of data processing. Common tools to aid in this processing include vegetation indices, point clouds, machine learning models, and statistical methods. With any emerging technology, there are also a few considerations that need to be analyzed like legal constraints, economic trade-offs, and ease of use. This review then concludes with a discussion on the pros and cons of this technology, along with a brief outlook into future areas of research regarding UAS technology in agriculture.

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Section: Weed Managementmentioning

confidence: 99%

Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

2019

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“…Cranberry is a perennial crop grown primarily in North America. The United States and Canada dominate production with 98,000 and 50,000 ha, respectively, in 2017 (Sandler 2018). In South America, approximately 4,200 ha were in production in Chile in the same year, along with minor hectarage distributed across Eastern Europe and the Netherlands.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, despite more than 200 yr of commercial production in the United States (Sandler 2018), reports of direct quantification of weed impact on cranberry yield and quality is limited to a single paper by Patten and Wang (1994), in which three perennial weed species were investigated: Pacific silverleaf [Argentina egedii (Wormsk.) Rydb.…”

Section: Introductionmentioning

confidence: 99%

Exploring the influence of weeds on cranberry yield and quality

Colquhoun

Besançon

Ghantous³

et al. 2022

Weed Technol

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The influence of weeds on cranberry yield and quality is not well known and cannot be extrapolated from other cropping systems given the unique nature of both cranberry production and the weed species spectrum. The work presented here addresses this need with four common weed species across multiple production seasons and systems in Wisconsin, Massachusetts, and New Jersey, USA: Carolina redroot, earth loosestrife, bristly dewberry, and polytrichum moss. The objectives were to use these representative species to quantify the impact of weed density, groundcover and biomass on several cranberry yield components and related interactions with other cranberry pests, and to determine whether these relationships were consistent enough across seasons to be reliably used in weed management decision-making. The relationships between Carolina redroot and bristly dewberry growth measures and marketable cranberry yield were highly significant (p ≤ 0.001 in 12 of 13 regressions) and consistent across growing seasons, but not significant for similar regressions with earth loosestrife. In particular, the strong relationship between Carolina redroot and bristly dewberry visual groundcover observations and cranberry yield suggests a simple way for growers and crop scouts to reliably estimate yield loss. The relationship between polytrichum moss biomass and cranberry yield was also significant in both years, but not consistent between years. Weed competition also affected cranberry quality, where Carolina redroot density was strongly related to the percentage of insect-damaged fruit and bristly dewberry growth reduced cranberry color development. Practically, this information can be used to educate growers, consultants, agrichemical registrants and regulators about the impacts of weeds on cranberry yield and quality, and to economically prioritize management efforts based on the weed species and extent of infestation.

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“…The potential of UAVs for conducting detailed surveys in precision agriculture has been demonstrated for a range of applications such as crop monitoring [10,11], field mapping [12], biomass estimation [13,14], weed management [15,16], plant population counting [17][18][19], and spraying [20]. A large amount of data and information is collected by UAVs to improve agricultural practices [21].…”

Section: Introductionmentioning

confidence: 99%

Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review

Neupane

Baysal-Gurel

2021

Remote Sensing

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Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extractions to interpret results. Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The future of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance systems, and so on. This review briefly highlights the advantages of automatic detection of plant diseases to the growers.

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Weed Management in Cranberries: A Historical Perspective and a Look to the Future

Cited by 6 publications

References 46 publications

Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

Exploring the influence of weeds on cranberry yield and quality

Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review

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