Multimodal remote sensing application for weed competition time series analysis in maize farmland ecosystems

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

doi:10.1016/j.jenvman.2023.118376

Cited by 8 publications

(3 citation statements)

References 45 publications

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“…Red-edge along with NIR was noted to be crucial in the prediction of maize chlorophyll content, which was an important indicator of crop health [61]. Additionally, red-edge band can serve as an indicator of weed infestation levels in maize crops [62]. Other features with high variable importance were the V (brightness values) and b* bands of the HSV and Lab* color spaces.…”

Section: Discussionmentioning

confidence: 99%

Weed Detection in Rainfed Maize Crops Using UAV and PlanetScope Imagery

de Villiers,

Munghemezulu,

Mashaba-Munghemezulu

et al. 2023

Sustainability

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Weed invasion of crop fields, such as maize, is a major threat leading to yield reductions or crop right-offs for smallholder farming, especially in developing countries. A synoptic view and timeous detection of weed invasions can save the crop. The sustainable development goals (SDGs) have identified food security as a major focus point. The objectives of this study are to: (1) assess the precision of mapping maize-weed infestations using multi-temporal, unmanned aerial vehicle (UAV), and PlanetScope data by utilizing machine learning algorithms, and (2) determine the optimal timing during the maize growing season for effective weed detection. UAV and PlanetScope satellite imagery were used to map weeds using machine learning algorithms—random forest (RF) and support vector machine (SVM). The input features included spectral bands, color space channels, and various vegetation indices derived from the datasets. Furthermore, principal component analysis (PCA) was used to produce principal components (PCs) that served as inputs for the classification. In this study, eight experiments are conducted, four experiments each for UAV and PlanetScope datasets spanning four months. Experiment 1 utilized all bands with the RF classifier, experiment 2 used all bands with SVM, experiment 3 employed PCs with RF, and experiment 4 utilized PCs with SVM. The results reveal that PlanetScope achieves accuracies below 49% in all four experiments. The best overall performance was observed for experiment 1 using the UAV based on the highest mean accuracy score (>0.88), which included the overall accuracy, precision, recall, F1 score, and cross-validation scores. The findings highlight the critical role of spectral information, color spaces, and vegetation indices in accurately identifying weeds during the mid-to-late stages of maize crop growth, with the higher spatial resolution of UAV exhibiting a higher precision in the classification accuracy than the PlanetScope imagery. The most optimal stage for weed detection was found to be during the reproductive stage of the crop cycle based on the best F1 scores being indicated for the maize and weeds class. This study provides pivotal information about the spatial distribution of weeds in maize fields and this information is essential for sustainable weed management in agricultural activities.

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Section: Discussionmentioning

confidence: 99%

Weed Detection in Rainfed Maize Crops Using UAV and PlanetScope Imagery

de Villiers,

Munghemezulu,

Mashaba-Munghemezulu

et al. 2023

Sustainability

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“…Multimodal imaging systems find diverse applications across various fields due to their ability to provide comprehensive insights by combining different imaging techniques. Some of the key applications include medical diagnostics [1][2][3][4], biomedical research [5], environmental and earth sciences [6,7]. In addition, the detection content in the industry is becoming more and more complex, and the information obtained by a single sensor cannot meet the detection needs.…”

Section: Introductionmentioning

confidence: 99%

Design of a Multimodal Detection System Tested on Tea Impurity Detection

Kuang,

Yu,

Guo

et al. 2024

Remote Sensing

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A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral features and color features at the same time and has higher spatial resolution than a single spectral camera. This system was applied to detect impurities in Pu’er tea to verify its high efficiency. The spectral and color features of each pixel in the images of Pu’er tea were obtained by this system and used for pixel classification. The experimental results showed that the accuracy of a support vector machine (SVM) model based on combined features was 93%, which was 7% higher than that based on spectral features only. By applying a median filtering algorithm and a contour detection algorithm to the label matrix extracted from pixel-classified images, except hair, eight impurities were detected successfully. Moreover, taking advantage of the high resolution of a visible light camera, small impurities could be clearly imaged. By comparing the segmented color image with the pixel-classified image, small impurities such as hair could be detected successfully. Finally, it was proved that the system could obtain multiple images to allow a more detailed and comprehensive understanding of the detected items and had an excellent ability to detect small impurities.

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“…Maize (Zea mays L.) is one of China's most important crops, and weeds affect maize yields through competition for relevant natural resources, such as light, water, and fertil-Biology 2023, 12, 1192 2 of 14 izer [1,2]. Large crabgrass (Digitaria sanguinalis (L.) Scop.…”

Section: Introductionmentioning

confidence: 99%

Cytochrome P450s-Involved Enhanced Metabolism Contributes to the High Level of Nicosulfuron Resistance in Digitaria sanguinalis from China

Wang,

Hu,

et al. 2023

Biology

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Large crabgrass (Digitaria sanguinalis (L.) Scop.) is one of the major malignant grass weeds in Chinese maize (Zea mays L.) fields, and it has recently developed resistance to the acetolactate synthase (ALS)-inhibiting herbicide nicosulfuron. This study focused on a suspected nicosulfuron-resistant (R) population (LJ-01) of D. sanguinalis, collected from Lujiang County in Anhui Province, China, to explore the resistance level and potential resistance mechanism. Whole-plant dose–response testing confirmed that the LJ-01 population evolved a high level of resistance to nicosulfuron (11.5-fold) compared to the susceptible (S) population, DY-02. The ALS gene sequencing and relative expression assay of the R plants indicated that target gene mutation and overexpression were not responsible for the resistance phenotype. However, pretreatment with malathion, a known cytochrome P450 monooxygenase (P450) inhibitor, alleviated the resistance of the R population to nicosulfuron by approximately 36%. High-performance liquid chromatography (HPLC) analysis revealed that the R plants metabolized nicosulfuron faster than the S plants. Moreover, cross-resistance testing suggested that the R population exhibited low levels of resistance to thifensulfuron-methyl and pyrazosulfuron-ethyl, but it remained susceptible to rimsulfuron. Multiple resistance patterns showed that the R population evolved low resistance to the photosystem inhibitors bromoxynil octanoate and atrazine and sensitivity to the acetyl-CoA carboxylase (ACCase) inhibitor cyhalofop-butyl and the 4-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitors tembotrione, mesotrione, and topramezone. This study reports, for the first time, the simultaneous resistance to ALS and different photosystem inhibitors in D. sanguinalis. The nicosulfuron resistance observed in the R population could primarily be attributed to an enhanced metabolism involving P450 enzymes.

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Multimodal remote sensing application for weed competition time series analysis in maize farmland ecosystems

Cited by 8 publications

References 45 publications

Weed Detection in Rainfed Maize Crops Using UAV and PlanetScope Imagery

Weed Detection in Rainfed Maize Crops Using UAV and PlanetScope Imagery

Design of a Multimodal Detection System Tested on Tea Impurity Detection

Cytochrome P450s-Involved Enhanced Metabolism Contributes to the High Level of Nicosulfuron Resistance in Digitaria sanguinalis from China

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