Edge detection for weed recognition in lawns

Parra, Lorena; Marín, José; Yousfi, Salima; Rincón, Gregorio; Mauri, Pedro V.; Lloret, Jaime

doi:10.1016/j.compag.2020.105684

Cited by 34 publications

(22 citation statements)

References 12 publications

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“…They focus mainly on the flora and ve ge ta tion of lawn com mu nities (Thompson et al 2004, Stavretović & Jovanović 2005, Anishchenko 2005, Pal et al 2013, Ishbirdina et al 2019, Anishchenko et al 2019, Novaković et al 2020, the sta bility of lawn grass mix tures and their composition (Düb bern de Souza et al 2020, Wolski et al 2020, the im pact of various en vi ron men tal and anthropogenic factors (light, irrigation, trampling, soil conditions, etc. ) on the du ra bility of lawn carpets, as well as va rious issues of ma nage ment and the role of lawns in urban eco systems (Lap tev 1983, Tyuldyukov et al 2002, Petrova 2007, Ani shchen ko et al 2011, Vizirskaya et al 2013, Lukinykh 2013, Gladkov et al 2016, Chollet et al 2018, Watson et al 2019, Foti et al 2020, Parra et al 2020, Unruh et al 2020. The experience of lawn science accumulated shows that the problem of creating lawn grass stands can be successfully solved only on the basis of a deep knowledge of the bioecological features of the spe cies used in the creation of lawns in specific ecological and geo gra phi cal conditions of urban ecosystems.…”

Section: On the Ecology Of Lawn Communities In The Cities Of The Republic Of Bashkortostan Russiamentioning

confidence: 99%

On the ecology of lawn communities in the cities of the Republic of Bashkortostan, Russia

Golovanov¹,

Abramova²,

Zhigunov³

et al. 2021

Bot Pac

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This article provides information on the ecology of lawn communities in the cities of the Urals of the Republic of Bashkortostan (Ufa, Sterlitamak, Salavat, Ishimbay, Meleuz, Birsk, Neftekamsk, Yanaul). On the basis of 195 relevés, the ecological regimes of lawn communities and their species were determined using the ecological scales of Landolt. DCA ordination of the relevés confirmed their floristic differentiation into 4 associations, 2 subassociations, and 5 facies assigned to the alliance Cynosurion cristati of the class Molinio–Arrhenatheretea. The main contribution to the differentiation of communities is made by the thermoclimatic factor, soil moisture and nutrient regimes. The ranges of values of environmental factors of the lawn communities are determined. When conditions deteriorate, there is a succession of lawn communities from subass. typicum of the ass. Leontodono–Poetum pratensis via the subass. cichorietosum intybi of the same association to the ass. Poo pratensis– Plantaginetum majoris. At the same time, species diversity first increases due to the invassion of synanthropic species, and then decreases. Lawns considered to belong to synanthropized vegetation when the number of synanthropic species reaches 56–63 %. Along with traditional herbal mixtures with Poa pratensis, herbal mixtures with Festuca rubra and Lolium perenne are recommended, the combination of which better corresponds to a fairly wide range of environmental conditions of the Urals. Regular maintenance, watering and mowing are also necessary, which extend the life of the lawns.

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Section: On the Ecology Of Lawn Communities In The Cities Of The Republic Of Bashkortostan Russiamentioning

confidence: 99%

On the ecology of lawn communities in the cities of the Republic of Bashkortostan, Russia

Golovanov¹,

Abramova²,

Zhigunov³

et al. 2021

Bot Pac

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“…Chandel et al [27] applied deep learning models to monitor the water condition of crops and identified the water stress with over 90% accuracy. Parra et al [28] compared various edge detection filters for weed recognition in lawns and identified that the sharping filters provided the best results with low computing requirements.…”

Section: Related Work 21 Computer Vision In Precision Agriculturementioning

confidence: 99%

Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

Zhao

Yamada

et al. 2020

Remote Sensing

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In recent years, precision agriculture has been researched to increase crop production with less inputs, as a promising means to meet the growing demand of agriculture products. Computer vision-based crop detection with unmanned aerial vehicle (UAV)-acquired images is a critical tool for precision agriculture. However, object detection using deep learning algorithms rely on a significant amount of manually prelabeled training datasets as ground truths. Field object detection, such as bales, is especially difficult because of (1) long-period image acquisitions under different illumination conditions and seasons; (2) limited existing prelabeled data; and (3) few pretrained models and research as references. This work increases the bale detection accuracy based on limited data collection and labeling, by building an innovative algorithms pipeline. First, an object detection model is trained using 243 images captured with good illimitation conditions in fall from the crop lands. In addition, domain adaptation (DA), a kind of transfer learning, is applied for synthesizing the training data under diverse environmental conditions with automatic labels. Finally, the object detection model is optimized with the synthesized datasets. The case study shows the proposed method improves the bale detecting performance, including the recall, mean average precision (mAP), and F measure (F1 score), from averages of 0.59, 0.7, and 0.7 (the object detection) to averages of 0.93, 0.94, and 0.89 (the object detection + DA), respectively. This approach could be easily scaled to many other crop field objects and will significantly contribute to precision agriculture.

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“…This problem can be solved following the same principles used in weed detection in traditional agriculture. In this case, two main approaches can be identified: basic operations with RGB images (such as band combination or edge detection) and more powerful algorithms and artificial intelligence such as object recognition [6][7][8]. In order to have near real-time results, and considering the current processing limitations of nodes, it is recommended to focus on the first option if images must be processed locally in the field.…”

Section: Introductionmentioning

confidence: 99%

Methodology to Differentiate Legume Species in Intercropping Agroecosystems Based on UAV with RGB Camera

Parra

Mostaza-Colado²,

Marín³

et al. 2022

Electronics

Self Cite

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Mixed crops are one of the fundamental pillars of agroecological practices. Row intercropping is one of the mixed cropping options based on the combination of two or more species to reduce their impacts. Nonetheless, from a monitoring perspective, the coexistence of different species with different characteristics complicates some processes, requiring a series of adaptations. This article presents the initial development of a procedure that differentiates between chickpea, lentil, and ervil in an intercropping agroecosystem. The images have been taken with a drone at the height of 12 and 16 m and include the three crops in the same photograph. The Vegetation Index and Soil Index are used and combined. After generating the index, aggregation techniques are used to minimize false positives and false negatives. Our results indicate that it is possible to differentiate between the three crops, with the difference between the chickpea and the other two legume species clearer than that between the lentil and the ervil in images gathered at 16 m. The accuracy of the proposed methodology is 95% for chickpea recognition, 86% for lentils, and 60% for ervil. This methodology can be adapted to be applied in other crop combinations to improve the detection of abnormal plant vigour in intercropping agroecosystems.

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Edge detection for weed recognition in lawns

Cited by 34 publications

References 12 publications

On the ecology of lawn communities in the cities of the Republic of Bashkortostan, Russia

On the ecology of lawn communities in the cities of the Republic of Bashkortostan, Russia

Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

Methodology to Differentiate Legume Species in Intercropping Agroecosystems Based on UAV with RGB Camera

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