Hyperspectral remote sensing to assess weed competitiveness in maize farmland ecosystems

Lou, Zhaoxia; Quan, Longzhe; Sun, Deng; Li, Hailong; Xia, Fulin

doi:10.1016/j.scitotenv.2022.157071

Cited by 20 publications

(9 citation statements)

References 48 publications

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“…DC. ], and other species are speculated to be due to changes in leaf structure or the collapse of mesophyll structure caused by abiotic stress (such as water-deficit stress) (Li et al 2022; Lou et al 2022). This leads to increased infrared light absorption by plants.…”

Section: Resultsmentioning

confidence: 99%

Characterization and classification of urban weed species in northeast China using terrestrial hyperspectral images

Wang,

Chen,

et al. 2023

Weed Sci

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Weeds contribute to biodiversity and a wide range of ecosystem functions. It is crucial to map different weed species and analyze their physiological activities. Remote sensing techniques for plant identification, especially hyperspectral imaging, are being developed using spectral response patterns to vegetation for detection and species identification. A library of hyperspectral images of 40 urban weed species in northeast China was established in this study. A terrestrial hyperspectral camera was used to acquire 435 hyperspectral images. The hyperspectral information for each weed species was extracted and analyzed. The spectral characteristics and vegetation indices of different weeds revealed the differences between weed species in the cities of northeast China and indirectly characterized the growth and physiological activity levels of different species, but could not effectively distinguish different species. Five methods—first derivative spectrum (FDS), second derivative spectrum (SDS), standard normal variate (SNV), moving averages (MA), and Savitzky-Golay (SG) smoothing—were used to pretreat the spectral curves to maximize the retention of spectral characteristics while removing the influence of noise. We investigated the application of a convolutional neural network (CNN) with terrestrial hyperspectral remote sensing to identify urban weeds in northeast China. A CNN classification model was established to distinguish weeds from the hyperspectral images and demonstrated a test accuracy of 95.32% to 98.15%. The accuracy of the original spectrum was 97.45%; SNV had the best accuracy (98.15%) and SG was the least accurate (95.32%). This provides a baseline for understanding the hyperspectral characteristics of urban weed species and monitoring their growth. It also contributes to the development of a hyperspectral imaging database with global applicability.

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Section: Resultsmentioning

confidence: 99%

Characterization and classification of urban weed species in northeast China using terrestrial hyperspectral images

Wang,

Chen,

et al. 2023

Weed Sci

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“…However, this method has the limitation of retaining an excessive number of bands in the band selection process, which may identify irrelevant bands as significant. Therefore, the use of the VIP method in band selection may require further research [36,37].…”

Section: Image Data Collectionmentioning

confidence: 99%

“…from the vegetation (i.e., leaves of different weeds). Zhaoxia Lou et al [37] extracted the vegetation canopy spectra for the acquired images. The contrast between the vegetation canopy and the soil background was improved using OSAVI.…”

Section: Background Removalmentioning

confidence: 99%

“…Spectroscopy is also categorized in many ways; the common ones are Point Spectroscopy, RGB and hyperspectral imaging, fluorescence spectroscopy and multispectral Imaging. The theoretical basis for using spectral detection is that weed rivalry leads to changes in plant physiology that alter light-absorbing and crown reflectance properties [37]. Figure 4 shows the regions of interest for corn seedlings and weeds on hyperspectral images and the corresponding average spectral curves.…”

Section: Spectral Featurementioning

confidence: 99%

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Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review

Qu,

2024

Agronomy

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Weeds and crops engage in a relentless battle for the same resources, leading to potential reductions in crop yields and increased agricultural costs. Traditional methods of weed control, such as heavy herbicide use, come with the drawback of promoting weed resistance and environmental pollution. As the demand for pollution-free and organic agricultural products rises, there is a pressing need for innovative solutions. The emergence of smart agricultural equipment, including intelligent robots, unmanned aerial vehicles and satellite technology, proves to be pivotal in addressing weed-related challenges. The effectiveness of smart agricultural equipment, however, hinges on accurate detection, a task influenced by various factors, like growth stages, environmental conditions and shading. To achieve precise crop identification, it is essential to employ suitable sensors and optimized algorithms. Deep learning plays a crucial role in enhancing weed recognition accuracy. This advancement enables targeted actions such as minimal pesticide spraying or precise laser excision of weeds, effectively reducing the overall cost of agricultural production. This paper provides a thorough overview of the application of deep learning for crop and weed recognition in smart agricultural equipment. Starting with an overview of intelligent agricultural tools, sensors and identification algorithms, the discussion delves into instructive examples, showcasing the technology’s prowess in distinguishing between weeds and crops. The narrative highlights recent breakthroughs in automated technologies for precision plant identification while acknowledging existing challenges and proposing prospects. By marrying cutting-edge technology with sustainable agricultural practices, the adoption of intelligent equipment presents a promising path toward efficient and eco-friendly weed management in modern agriculture.

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“…At the same time, different types of sensors need to be installed, to improve the intelligence of agricultural robots. The commonly used ones include anti-collision, infrared, ultrasonic, laser, visual, tactile, and so on, to complete the information fusion of multiple sensors and obtain more reliable and accurate information [159,160]. Therefore, agricultural robots must integrate different subsystems, transmit the required information, and ensure correct synchronization [161].…”

Section: Information Fusionmentioning

confidence: 99%

Actuators and Sensors for Application in Agricultural Robots: A Review

et al. 2022

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In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future.

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Hyperspectral remote sensing to assess weed competitiveness in maize farmland ecosystems

Cited by 20 publications

References 48 publications

Characterization and classification of urban weed species in northeast China using terrestrial hyperspectral images

Characterization and classification of urban weed species in northeast China using terrestrial hyperspectral images

Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review

Actuators and Sensors for Application in Agricultural Robots: A Review

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