Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images

Ma, Qi; Han, Wenting; Huang, Shenjin; Dong, Sining; Li, Guang; Chen, Haipeng

doi:10.3390/s21061994

Cited by 14 publications

(16 citation statements)

References 48 publications

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“…Many experiments showed that when ntree was 50, the error gradually converges and tended to be stable. Mtry was the square root of the total feature [32].…”

Section: Random Forest Methodsmentioning

confidence: 99%

“…Due to years of uncontrolled irrigation from the Yellow River, the groundwater level in this region is high and the soil salinity degree is high. As a result, only salinity resistant crops can be planted in this region, such as maize and sunflower, among which the largest planting scale is sunflower [31,32].…”

Section: Selection Of Study Areasmentioning

confidence: 99%

“…In order to understand the changes in the spectra of the lodging area, spectral curves of the lodging sunflower, soil, and normal sunflower were obtained using FieldSpec Hand Held (ASD, Westborough, CO, USA) during drone monitoring. For parameters and usage of FieldSpec Hand Held, refer to [32]. The spectral curve is shown in Figure 11.…”

Section: Comparison Of Classification Of Zones Of Sunflower Lodgingmentioning

confidence: 99%

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Extraction of Sunflower Lodging Information Based on UAV Multi-Spectral Remote Sensing and Deep Learning

Han

Huang

et al. 2021

Remote Sensing

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The rapid and accurate identification of sunflower lodging is important for the assessment of damage to sunflower crops. To develop a fast and accurate method of extraction of information on sunflower lodging, this study improves the inputs to SegNet and U-Net to render them suitable for multi-band image processing. Random forest and two improved deep learning methods are combined with RGB, RGB + NIR, RGB + red-edge, and RGB + NIR + red-edge bands of multi-spectral images captured by a UAV (unmanned aerial vehicle) to construct 12 models to extract information on sunflower lodging. These models are then combined with the method used to ignore edge-related information to predict sunflower lodging. The results of experiments show that the deep learning methods were superior to the random forest method in terms of the obtained lodging information and accuracy. The predictive accuracy of the model constructed by using a combination of SegNet and RGB + NIR had the highest overall accuracy of 88.23%. Adding NIR to RGB improved the accuracy of extraction of the lodging information whereas adding red-edge reduced it. An overlay analysis of the results for the lodging area shows that the extraction error was mainly caused by the failure of the model to recognize lodging in mixed areas and low-coverage areas. The predictive accuracy of information on sunflower lodging when edge-related information was ignored was about 2% higher than that obtained by using the direct splicing method.

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“…Many experiments showed that when ntree was 50, the error gradually converges and tended to be stable. Mtry was the square root of the total feature [32].…”

Section: Random Forest Methodsmentioning

confidence: 99%

Section: Selection Of Study Areasmentioning

confidence: 99%

Section: Comparison Of Classification Of Zones Of Sunflower Lodgingmentioning

confidence: 99%

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Extraction of Sunflower Lodging Information Based on UAV Multi-Spectral Remote Sensing and Deep Learning

Han

Huang

et al. 2021

Remote Sensing

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“…Before considering the details, the different mentions of intercropping in remote sensing literature are summarised. Q. Ma et al [19] compiled information on several papers mentioning that crop identification in intercropping of multiple crops makes it difficult to attain high accuracy and that most studies focus on three to five different crops. They also mention that, although spectral and texture features calculated with UAV multispectral remote sensing data combined with an object-oriented Support Vector Machine (SVM) obtain high accuracies in mono-crop systems (up to 94% in maize [20]), there is significant interferences in mixed crop and intercropping.…”

Section: Comparison Of Results With Literaturementioning

confidence: 99%

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

Parra

Mostaza-Colado²,

Marín³

et al. 2022

Electronics

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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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“…Drones are rapidly evolving in the field of agriculture and can perform numerous tasks, such as weed mapping [ 14 ], soil and crop status monitoring [ 15 ], pesticide spraying [ 16 ], diagnosis of insect pests [ 17 ], and artificial pollination [ 18 ].…”

Section: Related Workmentioning

confidence: 99%

LoRa Communications as an Enabler for Internet of Drones towards Large-Scale Livestock Monitoring in Rural Farms

Behjati

Noh

Alobaidy

et al. 2021

Sensors

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Currently, smart farming is considered an effective solution to enhance the productivity of farms; thereby, it has recently received broad interest from service providers to offer a wide range of applications, from pest identification to asset monitoring. Although the emergence of digital technologies, such as the Internet of Things (IoT) and low-power wide-area networks (LPWANs), has led to significant advances in the smart farming industry, farming operations still need more efficient solutions. On the other hand, the utilization of unmanned aerial vehicles (UAVs), also known as drones, is growing rapidly across many civil application domains. This paper aims to develop a farm monitoring system that incorporates UAV, LPWAN, and IoT technologies to transform the current farm management approach and aid farmers in obtaining actionable data from their farm operations. In this regard, an IoT-based water quality monitoring system was developed because water is an essential aspect in livestock development. Then, based on the Long-Range Wide-Area Network (LoRaWAN®) technology, a multi-channel LoRaWAN® gateway was developed and integrated into a vertical takeoff and landing drone to convey collected data from the sensors to the cloud for further analysis. In addition, to develop LoRaWAN®-based aerial communication, a series of measurements and simulations were performed under different configurations and scenarios. Finally, to enhance the efficiency of aerial-based data collection, the UAV path planning was optimized. Measurement results showed that the maximum achievable LoRa coverage when operating on-air via the drone is about 10 km, and the Longley–Rice irregular terrain model provides the most suitable path loss model for the scenario of large-scale farms, and a multi-channel gateway with a spreading factor of 12 provides the most reliable communication link at a high drone speed (up to 95 km/h). Simulation results showed that the developed system can overcome the coverage limitation of LoRaWAN® and it can establish a reliable communication link over large-scale wireless sensor networks. In addition, it was shown that by optimizing flight paths, aerial data collection could be performed in a much shorter time than industrial mission planning (up to four times in our case).

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Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images

Cited by 14 publications

References 48 publications

Extraction of Sunflower Lodging Information Based on UAV Multi-Spectral Remote Sensing and Deep Learning

Extraction of Sunflower Lodging Information Based on UAV Multi-Spectral Remote Sensing and Deep Learning

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

LoRa Communications as an Enabler for Internet of Drones towards Large-Scale Livestock Monitoring in Rural Farms

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