Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery

Yang, Ming‐Der; Huang, Kai-Shiang; Kuo, Yi-Hsuan; Tsai, Hui Ping; Lin, Liang-Mao

doi:10.3390/rs9060583

Cited by 139 publications

(97 citation statements)

References 63 publications

(34 reference statements)

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“…When anomalies were observed, the canopy structure was compromised. This factor was observed and suggested by other authors who evaluated lodging in maize and rice (Han et al, 2018;Chu et al, 2017;Yang et al, 2017). When the structure of the canopy presents anomalies, different angles of inclination occurs, reflecting the difference between plant height and soil level (Chu et al, 2017).…”

Section: Resultsmentioning

confidence: 53%

Identification of Maize Lodging: A Case Study Using a Remotely Piloted Aircraft System

2019

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A common agricultural problem in many regions of Brazil is maize lodging, as a consequence of strong winds and rain which impacts on crop growth and yield. However, collecting data using ground-based, manual field measurement methods is inefficient. An emerging tool is the Remotely Piloted Aircraft System (RPAS), capable of delivering spatial data with high resolution and flexible periodicity. In this study, the potential to detect the maize lodging using crop surface models derived from RPAS was assessed. Our RPA-based approach uses a quantitative threshold to determine lodging percentage.The threshold values of plant height, used to detect the occurrence of lodging, were based on fixed and variable values. The validation of percentage lodging was performed using the RGB orthomosaic. The derived lodging estimates showed a very high correlation to the reference data. High correlations were observed for the fixed threshold at 60% (R² = 0.93, RSME = 8.72%) and the variable thresholds, Jenks natural breaks and iso-clusters (R² = 0.92, RSME = 8.89% and R²= 0.92, RSME = 9%, respectively). This study demonstrated the potential of the use of this technique, reducing the subjectivity of ground-based evaluation and the laborious traditional technique of lodging inference.

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Section: Resultsmentioning

confidence: 53%

Identification of Maize Lodging: A Case Study Using a Remotely Piloted Aircraft System

2019

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“…Stanton et al [79] reported that crop height extracted from SfM DSM and NDVI from UAV products were related to stress caused by aphid plagues. Similarly, Yang et al [80] used a hybrid method of spectral and DSM data to classify logging damage in rice fields. As we see, the existing literature only evaluates study cases or use DSM data as a proxy.…”

Section: Discussionmentioning

confidence: 99%

Crop Loss Evaluation Using Digital Surface Models from Unmanned Aerial Vehicles Data

Millán

Rankine²,

Sánchez‐Azofeifa

2020

Remote Sensing

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Precision agriculture and Unmanned Aerial Vehicles (UAV) are revolutionizing agriculture management methods. Remote sensing data, image analysis and Digital Surface Models derived from Structure from Motion and Multi-View Stereopsis offer new and fast methods to detect the needs of crops, greatly improving crops efficiency. In this study, we present a tool to detect and estimate crop damage after a disturbance (i.e., weather event, wildlife attacks or fires). The types of damage that are addressed in this study affect crop structure (i.e., plants are bent or gone), in the shape of depressions in the crop canopy. The aim of this study was to evaluate the performance of four unsupervised methods based on terrain analyses, for the detection of damaged crops in UAV 3D models: slope detection, variance analysis, geomorphology classification and cloth simulation filter. A full workflow was designed and described in this article that involves the postprocessing of the raw results from the terrain analyses, for a refinement in the detection of damages. Our results show that all four methods performed similarly well after postprocessing-reaching an accuracy above to 90%-in the detection of severe crop damage, without the need of training data. The results of this study suggest that the used methods are effective and independent of the crop type, crop damage and growth stage. However, only severe damages were detected with this workflow. Other factors such as data volume, processing time, number of processing steps and spatial distribution of targets and errors are discussed in this article for the selection of the most appropriate method. Among the four tested methods, slope analysis involves less processing steps, generates the smallest data volume, is the fastest of methods and resulted in best spatial distribution of matches. Thus, it was selected as the most efficient method for crop damage detection. Under many climate change scenarios, many authors point out that human systems developed during the Holocene (such as agriculture) are under risk of disappearing or at least be compromised in the Anthropocene era that we have entered, and it is expected that this situation might become worse in the coming years [6][7][8]. While the total world precipitation has not significantly changed in the last decade, the distribution of rainfall across the year and space has changed, implying relevant effects in biomes and agriculture lands [9].Farmers and agronomists are aware of the risk of their business, with an eye on possible changes on climate. Thus, in the literature we found many studies related to alternative management practices to face climate change [10,11]. Some effects, unfortunately, cannot be prevented, such as floods, storms and fires. For this reason, an accurate evaluation and prediction of damages is of major importance for farmers and stakeholders in the agriculture business, including insurance companies. Many farmers and agriculture corporations invest in insurance in case they lose part or the totality of t...

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“…The common approach to mitigate noise effects is to either use spatial contextual information or apply an object-oriented classification approach. For the spatial contextual information approach, texture information is firstly extracted from a gray-level co-occurrence matrix (GLCM) [20] and then combined with spectral information for classification [21][22][23]. The utilization of such texture information can reduce the impacts of isolated pixels within the pixel-based approach.…”

Section: Introductionmentioning

confidence: 99%

Impact of Texture Information on Crop Classification with Machine Learning and UAV Images

Kwak

Park

2019

Applied Sciences

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Unmanned aerial vehicle (UAV) images that can provide thematic information at much higher spatial and temporal resolutions than satellite images have great potential in crop classification. Due to the ultra-high spatial resolution of UAV images, spatial contextual information such as texture is often used for crop classification. From a data availability viewpoint, it is not always possible to acquire time-series UAV images due to limited accessibility to the study area. Thus, it is necessary to improve classification performance for situations when a single or minimum number of UAV images are available for crop classification. In this study, we investigate the potential of gray-level co-occurrence matrix (GLCM)-based texture information for crop classification with time-series UAV images and machine learning classifiers including random forest and support vector machine. In particular, the impact of combining texture and spectral information on the classification performance is evaluated for cases that use only one UAV image or multi-temporal images as input. A case study of crop classification in Anbandegi of Korea was conducted for the above comparisons. The best classification accuracy was achieved when multi-temporal UAV images which can fully account for the growth cycles of crops were combined with GLCM-based texture features. However, the impact of the utilization of texture information was not significant. In contrast, when one August UAV image was used for crop classification, the utilization of texture information significantly affected the classification performance. Classification using texture features extracted from GLCM with larger kernel size significantly improved classification accuracy, an improvement of 7.72%p in overall accuracy for the support vector machine classifier, compared with classification based solely on spectral information. These results indicate the usefulness of texture information for classification of ultra-high-spatial-resolution UAV images, particularly when acquisition of time-series UAV images is difficult and only one UAV image is used for crop classification.

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Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery

Cited by 139 publications

References 63 publications

Identification of Maize Lodging: A Case Study Using a Remotely Piloted Aircraft System

Identification of Maize Lodging: A Case Study Using a Remotely Piloted Aircraft System

Crop Loss Evaluation Using Digital Surface Models from Unmanned Aerial Vehicles Data

Impact of Texture Information on Crop Classification with Machine Learning and UAV Images

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