Spectral differentiation of sugarcane from weeds

Souza, Mateus Ferreira de; Amaral, Lucas Rios do; Oliveira, Stanley Robson de Medeiros; Coutinho, Marcos Antonio Neris; Netto, Camila Ferreira

doi:10.1016/j.biosystemseng.2019.11.023

Cited by 21 publications

(24 citation statements)

References 21 publications

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“…SIMCA models using feature wavelengths in VIS (640, 676, and 730 nm) and NIR (1078, 1435, 1490, and 1615 nm) regions classified three weed species (water-hemp, kochia, and lamb's-quarters) with over 90% accuracy [44]. Compared to random forests (RF), higher accuracy (97%) was obtained by the SIMCA using four VIS/NIR variables (500-550, 650-750, 1300-1450, and 1800-1900 nm) to differentiate sugarcane from weeds [62]. A good classification result was achieved due to the different constituent elements or concentrations of compounds (such as anthocyanin, chlorophyll, and moisture) between this crops and weeds, which caused a significant difference in spectral features (Figure 3).…”

Section: Point Spectroscopymentioning

confidence: 99%

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Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review

2020

Smart Cities

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Crop productivity is readily reduced by competition from weeds. It is particularly important to control weeds early to prevent yield losses. Limited herbicide choices and increasing costs of weed management are threatening the profitability of crops. Smart agriculture can use intelligent technology to accurately measure the distribution of weeds in the field and perform weed control tasks in selected areas, which cannot only improve the effectiveness of pesticides, but also increase the economic benefits of agricultural products. The most important thing for an automatic system to remove weeds within crop rows is to utilize reliable sensing technology to achieve accurate differentiation of weeds and crops at specific locations in the field. In recent years, there have been many significant achievements involving the differentiation of crops and weeds. These studies are related to the development of rapid and non-destructive sensors, as well as the analysis methods for the data obtained. This paper presents a review of the use of three sensing methods including spectroscopy, color imaging, and hyperspectral imaging in the discrimination of crops and weeds. Several algorithms of machine learning have been employed for data analysis such as convolutional neural network (CNN), artificial neural network (ANN), and support vector machine (SVM). Successful applications include the weed detection in grain crops (such as maize, wheat, and soybean), vegetable crops (such as tomato, lettuce, and radish), and fiber crops (such as cotton) with unsupervised or supervised learning. This review gives a brief introduction into proposed sensing and machine learning methods, then provides an overview of instructive examples of these techniques for weed/crop discrimination. The discussion describes the recent progress made in the development of automated technology for accurate plant identification as well as the challenges and future prospects. It is believed that this review is of great significance to those who study automatic plant care in crops using intelligent technology.

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Section: Point Spectroscopymentioning

confidence: 99%

“…Cities 2020, 3 FOR PEER REVIEW 7 Mean visible-near infrared (VIS-NIR) spectra from sugarcane and weeds leaves[62].…”

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confidence: 99%

Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review

2020

Smart Cities

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“…These similar findings suggest that the environmental factors, microclimates, plant leaves, solar angles, and changing morphological and spectral properties are different at every growth stage, which will influence weed detection [29]. Humans are unable to differentiate between these plants features using the naked eye; thus, by using spectral signatures, the plants can be differentiated and classified using statistical analysis and machine learning [30][31][32].…”

Section: Classification and Validationmentioning

confidence: 82%

“…A similar study used spectral signatures (500-550, 650-750, 1300-1450, and 1800-1900 nm) to successfully discriminate weeds from sugarcane [30,32]. The shortwave infrared bands (SWIR; 1660, 1890, and 2000 nm) matched the primary features of the pure cellulose and lignin spectra of the plants.…”

Section: Band Identificationmentioning

confidence: 99%

Assessment of Weed Classification Using Hyperspectral Reflectance and Optimal Multispectral UAV Imagery

2021

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Weeds compete with crops and are hard to differentiate and identify due to their similarities in color, shape, and size. In this study, the weed species present in sorghum (sorghum bicolor (L.) Moench) fields, such as amaranth (Amaranthus macrocarpus), pigweed (Portulaca oleracea), mallow weed (Malva sp.), nutgrass (Cyperus rotundus), liver seed grass (Urochoa panicoides), and Bellive (Ipomea plebeian), were discriminated using hyperspectral data and were detected and analyzed using multispectral images. Discriminant analysis (DA) was used to identify the most significant spectral bands in order to discriminate weeds from sorghum using hyperspectral data. The results demonstrated good separation accuracy for Amaranthus macrocarpus, Urochoa panicoides, Malva sp., Cyperus rotundus, and Sorghum bicolor (L.) Moench at 440, 560, 680, 710, 720, and 850 nm. Later, the multispectral images of these six bands were collected to detect weeds in the sorghum crop fields using object-based image analysis (OBIA). The results showed that the differences between sorghum and weed species were detectable using the six selected bands, with data collected using an unmanned aerial vehicle. Here, the highest spatial resolution had the highest accuracy for weed detection. It was concluded that each weed was successfully discriminated using hyperspectral data and was detectable using multispectral data with higher spatial resolution.

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“…3,8,11 The spectroscopic method has been studied extensively for vegetation detection and species discrimination. Although there are numerous studies reporting the successful discrimination of plant species using the spectroscopic method, [12][13][14] there is an argument that finding unique spectral signatures that can distinguish between species is unrealistic based upon the spectral similarities shared by different plants and the variability in reflected data within a single species. 15 In addition, successful applications were usually conducted under well-controlled conditions using expensive spectrometers and complicated processing algorithms, which is not realistic for low-cost, fast applications in the field.…”

Section: Introductionmentioning

confidence: 99%

Vegetation detection based on spectral information and development of a low‐cost vegetation sensor for selective spraying

Wang

Men

et al. 2022

Pest Management Science

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BACKGROUND Real‐time site‐specific selective spraying for crop protection against weeds, pests and diseases requires fast and precise detection of targets. This study investigated several aspects of vegetation target detection based on spectral information and developed a low‐cost vegetation sensor. Specific objectives included: (1) compare vegetation‐detection performance using reflected and fluorescence data; (2) evaluate the effect of light source and bandwidth, and select the optimal light source and waveband; and (3) develop a low‐cost vegetation sensor and test its performance under different light conditions. RESULTS The main outcomes of this study are as follows. (1) The fluorescence excited by blue and red light‐emitting diodes (LEDs) was an effective attribute with which to differentiate vegetation from non‐vegetation and provided results comparative with reflected spectroscopy. (2) A blue LED could excite strong fluorescence and was recommended as the light source. (3) Under illumination by blue and red LEDs, bandwidth did not have any obvious effect on classification accuracy within the studied bandwidths from 0 nm (single wavelength) to 120 nm. (4) A low‐cost vegetation sensor was developed and tested, with 100% detection accuracy in both dark and outdoor environments. CONCLUSION This study suggests that single‐waveband fluorescence spectroscopy is an effective approach to detect vegetation targets and low‐cost LEDs can be used for illumination. Light source modulation with a sinusoidal signal is an effective way to resist the influence of environmental light. Overall, the findings of this study contribute to an improved understanding of developing low‐cost and effective vegetation sensors for selective spraying from theory to application. © 2022 Society of Chemical Industry.

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Spectral differentiation of sugarcane from weeds

Cited by 21 publications

References 21 publications

Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review

Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review

Assessment of Weed Classification Using Hyperspectral Reflectance and Optimal Multispectral UAV Imagery

Vegetation detection based on spectral information and development of a low‐cost vegetation sensor for selective spraying

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