Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery

Li, Xiuhua; Ba, Yuxuan; Nong, Mengling; Yang, Ce; Zhang, Shimin

doi:10.3390/s22072711

Cited by 10 publications

(3 citation statements)

References 69 publications

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“…This is consistent with the finding of Osco et al (2020) that vegetation indices contributed more to the prediction of leaf N content than spectral bands. Meanwhile, the green and red band reflectance provided a high information gain (Figure 4A) and a weak correlation with the vegetation index (Figure 4C), which is also consistent with the finding of Li et al (2022) that the combination of vegetation index plus spectral band variables can improve the accuracy of the model. In addition, we verified by supplementing the treatments with deficient elements that the change was indeed due to differences in N deficiency.…”

Section: Effect Of N Deficiency On Crop Phenotype and Canopy Spectrumsupporting

confidence: 87%

Integrated diagnosis and time-series sensitivity evaluation of nutrient deficiencies in medicinal plant (Ligusticum chuanxiong Hort.) based on UAV multispectral sensors

Wang²,

Han³

et al. 2023

Front. Plant Sci.

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BackgroundNitrogen(N), phosphorus(P), and potassium(K) are essential elements that are highly deficient during plant growth. Existing diagnostic methods are not suitable for rapid diagnosis of large-scale planting areas. Near-ground remote sensing technology based on unmanned aerial vehicle (UAV) and sensor is often applied to crop growth condition monitoring and agricultural management. It has been proven to be used for monitoring plant N, P, and K content. However, its integrated diagnostic model has been less studied.MethodsIn this study, we collected UAV multispectral images of Ligusticum chuanxiong Hort. in different periods of nutritional stress and constructed recognition models with different heights and algorithms. The optimal model variables were selected, and the effects of different sampling heights and modeling algorithms on the model efficiency under the time span were evaluated. At the same time, we evaluated the timeliness of the model based on leaf element content determination and SPAD. It was also validated in field crop production.ResultsThe results showed that the LR algorithm’s model had optimal performance at all periods and flight altitudes. The optimal accuracy of N-deficient plants identification reached 100%, P/K-deficient plants reached 92.4%, and normal plants reached 91.7%. The results of UAV multispectral diagnosis, chemical diagnosis, and SPAD value diagnosis were consistent in the diagnosis of N deficiency, and the diagnosis of P and K deficiency was slightly lagging behind that of chemical diagnosis.ConclusionsThis research uses UAV remote sensing technology to establish an efficient, fast, and timely nutritional diagnosis method for L. Chuanxiong, which is applied in production. Meanwhile, the standardized production of medicinal plant resources provides new solutions.

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Section: Effect Of N Deficiency On Crop Phenotype and Canopy Spectrumsupporting

confidence: 87%

Integrated diagnosis and time-series sensitivity evaluation of nutrient deficiencies in medicinal plant (Ligusticum chuanxiong Hort.) based on UAV multispectral sensors

Wang²,

Han³

et al. 2023

Front. Plant Sci.

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“…Lastly, with water being a scarce resource in most production areas, an efficient water management scheme that maintains crop yield but has a moderate and controlled level of moisture stress on their crops is required [29]. Multispectral images acquired from a UAV for water irrigation level recognition can potentially be used to help address over-and under-irrigation [38]. This can be achieved by capturing the canopy temperature of the crops using infrared thermometers to estimate the irrigation levels and the required irrigation scheduling methods.…”

Section: Over-and Under-irrigationmentioning

confidence: 99%

Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey

Zualkernan

Abuhani

Hussain

et al. 2023

Drones

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Unmanned aerial vehicles (UAVs) are increasingly being integrated into the domain of precision agriculture, revolutionizing the agricultural landscape. Specifically, UAVs are being used in conjunction with machine learning techniques to solve a variety of complex agricultural problems. This paper provides a careful survey of more than 70 studies that have applied machine learning techniques utilizing UAV imagery to solve agricultural problems. The survey examines the models employed, their applications, and their performance, spanning a wide range of agricultural tasks, including crop classification, crop and weed detection, cropland mapping, and field segmentation. Comparisons are made among supervised, semi-supervised, and unsupervised machine learning approaches, including traditional machine learning classifiers, convolutional neural networks (CNNs), single-stage detectors, two-stage detectors, and transformers. Lastly, future advancements and prospects for UAV utilization in precision agriculture are highlighted and discussed. The general findings of the paper demonstrate that, for simple classification problems, traditional machine learning techniques, CNNs, and transformers can be used, with CNNs being the optimal choice. For segmentation tasks, UNETs are by far the preferred approach. For detection tasks, two-stage detectors delivered the best performance. On the other hand, for dataset augmentation and enhancement, generative adversarial networks (GANs) were the most popular choice.

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“…In recent years, agricultural digitization has continuously improved, promoting the rational utilization of modern production technologies and traditional agricultural production elements, which plays a crucial role in adjusting agricultural production methods and achieving precision agriculture [4,5]. Agricultural digitization refers to the use of advanced technologies such as big data [6], machine learning [7,8], the Internet of Things [9], and deep learning [10][11][12] in the agricultural production process. Shantam Shorewala et al [13] proposed a semi-supervised decision method to identify the density and distribution of weeds from color images to locate weeds in fields.…”

Section: Introductionmentioning

confidence: 99%

Walnut Recognition Method for UAV Remote Sensing Images

Wu,

Yun,

Xue

et al. 2024

Agriculture

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During the process of walnut identification and counting using UAVs in hilly areas, the complex lighting conditions on the surface of walnuts somewhat affect the detection effectiveness of deep learning models. To address this issue, we proposed a lightweight walnut small object recognition method called w-YOLO. We reconstructed the feature extraction network and feature fusion network of the model to reduce the volume and complexity of the model. Additionally, to improve the recognition accuracy of walnut objects under complex lighting conditions, we adopted an attention mechanism detection layer and redesigned a set of detection heads more suitable for walnut small objects. A series of experiments showed that when identifying walnut objects in UAV remote sensing images, w-YOLO outperforms other mainstream object detection models, achieving a mean Average Precision (mAP0.5) of 97% and an F1-score of 92%, with parameters reduced by 52.3% compared to the YOLOv8s model. Effectively addressed the identification of walnut targets in Yunnan, China, under the influence of complex lighting conditions.

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Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery

Cited by 10 publications

References 69 publications

Integrated diagnosis and time-series sensitivity evaluation of nutrient deficiencies in medicinal plant (Ligusticum chuanxiong Hort.) based on UAV multispectral sensors

Integrated diagnosis and time-series sensitivity evaluation of nutrient deficiencies in medicinal plant (Ligusticum chuanxiong Hort.) based on UAV multispectral sensors

Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey

Walnut Recognition Method for UAV Remote Sensing Images

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