Detection of Weed and Wheat Using Image Processing

Hameed, Sarmad; Amin, Imran

doi:10.1109/icetas.2018.8629137

Cited by 29 publications

(10 citation statements)

References 7 publications

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“…Precise and timely weed maps, taking advantage of the high spatial resolution provided by UAVs, are thus essential. Successful application of UAVs has been demonstrated for weed mapping in sunflower [15][16][17][18], wheat [19,20], cotton and soybean [21], maize [22], rice [23,24], or sugar beet [25], but not in alfalfa, as in the present study.…”

Section: Introductionsupporting

confidence: 52%

UAV Detection of Sinapis arvensis Infestation in Alfalfa Plots Using Simple Vegetation Indices from Conventional Digital Cameras

et al. 2020

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Unmanned Aerial Vehicles (UAVs) offer excellent survey capabilities at low cost to provide farmers with information about the type and distribution of weeds in their fields. In this study, the problem of detecting the infestation of a typical weed (charlock mustard) in an alfalfa crop has been addressed using conventional digital cameras installed on a lightweight UAV to compare RGB-based indices with the widely used Normalized Difference Vegetation Index (NDVI) index. The simple (R−B)/(R+B) and (R−B)/(R+B+G) vegetation indices allowed one to easily discern the yellow weed from the green crop. Moreover, they avoided the potential confusion of weeds with soil observed for the NDVI index. The small overestimation detected in the weed identification when the RGB indices were used could be easily reduced by using them in conjunction with NDVI. The proposed methodology may be used in the generation of weed cover maps for alfalfa, which may then be translated into site-specific herbicide treatment maps.

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Section: Introductionsupporting

confidence: 52%

UAV Detection of Sinapis arvensis Infestation in Alfalfa Plots Using Simple Vegetation Indices from Conventional Digital Cameras

et al. 2020

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“…For training after every 1000 epochs of training, on the validation dataset, the model would be evaluated to calculate precision, remember, and imply common precision (mAP) [6]. This method tests for the following.…”

Section: Algorithmmentioning

confidence: 99%

Smart Weed Detection: An Real-Time Weed Detection for Onion Plantation

Sugany

Raj

David

et al. 2023

Advances in Transdisciplinary Engineering

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Weed control is fundamental to farming since weeds decline yields, increment creation costs, impede reap and lower item quality. Weeds additionally hinder water system water-stream, obstruct pesticide application, and harbor infection creatures. Early techniques for weed control included hand cultivation with hoes powered cultivation with cultivators, lethal wilting with high heat. However the results of these techniques are not significant, different means are maybe more commonplace today, especially the utilization of herbicide synthetic compounds. Automating the weed removal process is very important as we have both the need and technology to do the same, how, ever we need a machine-learning model, which can differentiate between the main crops and weed. Weed identification is the first step in automating the weed removal process. The proposed method facilitates the identification of weeds, and onions, in the plantation field using Machine learning in real-time.

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“…[38] использовали полною свёрточную сеть (FCN) для распознавания сорняков по изображениям, собранным устройством. Hameed et al [39], использовали самолет для сбора информации об урожае. Многие виды исследований описывают метод с общим анализом продуктивности без точного распознавания урожая [40,41].…”

Section: беспилотный летательный аппарат для задач распознавания сорняковunclassified

Нейросети, Глубокое Обучение, Машинное Зрение В Сельском Хозяйстве. Краткий Обзор Для 2021 Года

Rakhmatulin¹

2021

Preprint

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Беспрецедентный прогресс в области глубокого обучения повлиял на многие отрасли, в том числе и на сектор сельского хозяйства. Использование нейронных сетей в агропромышленной деятельности в задаче распознавания различных культур и сорняков - новое направление с историей менее 10 лет. Десятки новых нейронных сети появляются каждый год, но отсутствие каких-либо стандартов существенно затрудняет понимание реальной ситуации использования нейронной сети в аграрном секторе. В рукописи мы проанализировали исследования за последние 10 лет по использованию нейронных сетей для классификации и отслеживание посевов и сорняков в сельском хозяйстве. Мы представили анализ результатов использования различных нейронной сети для задач классификации и отслеживания. Наконец, мы сделали рекомендации по использованию нейронных сетей в задачах распознавания культивируемого объекта и сорняков.

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Detection of Weed and Wheat Using Image Processing

Cited by 29 publications

References 7 publications

UAV Detection of Sinapis arvensis Infestation in Alfalfa Plots Using Simple Vegetation Indices from Conventional Digital Cameras

UAV Detection of Sinapis arvensis Infestation in Alfalfa Plots Using Simple Vegetation Indices from Conventional Digital Cameras

Smart Weed Detection: An Real-Time Weed Detection for Onion Plantation

Нейросети, Глубокое Обучение, Машинное Зрение В Сельском Хозяйстве. Краткий Обзор Для 2021 Года

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