Extraction of onion fields infected by anthracnose-twister disease in selected municipalities of Nueva Ecija using UAV imageries

Alberto, Ronaldo T.; Rivera, Jaime; Biagtan, A. R.; Isip, M. F.

doi:10.1007/s41324-019-00302-z

Cited by 8 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This clearly proved the merit of the RF classifier in modeling high-dimensional data because it intrinsically works with a random subset of features instead of all of the features of the model at each splitting point of an individual tree in the forest, thereby averaging away the feature variance. Numerous pathological and entomological vegetation studies have reported that SVM succeeded in modeling f VIs extracted from spectral bands [ 39 , 89 , 90 ], while at the same time the modeling performance of for the RF classifier was found to be stable and superlative with transformed spectral reflectance data [ 91 , 92 , 93 ].…”

Section: Discussionmentioning

confidence: 99%

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Nguyen

Sagan

Maimaitiyiming

et al. 2021

Sensors

113

View full text Add to dashboard Cite

Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.

show abstract

Section: Discussionmentioning

confidence: 99%

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Nguyen

Sagan

Maimaitiyiming

et al. 2021

Sensors

113

View full text Add to dashboard Cite

show abstract

“…Multiview stereo (MVS) is utilized to build a dense point cloud, along with the DSM generation method using reverse distance weight interpolation. (29,30) Figure 2 presents the number of overlapped images used to build the point cloud. Green areas represent an overlap of at least five images for every pixel.…”

Section: Video-based Thermal Mosaicmentioning

confidence: 99%

Comparative Evaluation of Tracking Capability of Spatial Patterns on Defective Urban Solar Panels between Unmanned Aerial Vehicle Video Stream and Photomosaics

Hwang¹,

Um²,

Kim³

2022

Sensors and Materials

View full text Add to dashboard Cite

Unmanned aerial vehicle (UAV) autopilot flight requires a specific altitude when inspecting defective urban solar panels to avoid obstacles such as high-rise buildings, trees, and telegraph poles. Therefore, autopilot-based thermal imaging has severe data redundancy because the nonsolar panel area occupies more than 99% of the ground target. We aim to explore the tracking capability of a UAV video stream for defective urban solar panels by comparing spatial and clustering patterns with autopilot-based photomosaics. The spatial patterns of distributions and clusters in defective solar panels have high similarity (80-100%) to those of autopilot-based photomosaics. The results of this study can serve as a valuable reference for video-stream-based thermal deficiency inspections of defective solar panels in urban areas.

show abstract

Plant disease detection using drones in precision agriculture

Chin

Çatal

2023

Precision Agric

View full text Add to dashboard Cite

Plant diseases affect the quality and quantity of agricultural products and have an impact on food safety. These effects result in a loss of income in the production sectors which are particularly critical for developing countries. Visual inspection by subject matter experts is time-consuming, expensive and not scalable for large farms. As such, the automation of plant disease detection is a feasible solution to prevent losses in yield. Nowadays, one of the most popular approaches for this automation is to use drones. Though there are several articles published on the use of drones for plant disease detection, a systematic overview of these studies is lacking. To address this problem, a systematic literature review (SLR) on the use of drones for plant disease detection was undertaken and 38 primary studies were selected to answer research questions related to disease types, drone categories, stakeholders, machine learning tasks, data, techniques to support decision-making, agricultural product types and challenges. It was shown that the most common disease is blight; fungus is the most important pathogen and grape and watermelon are the most studied crops. The most used drone type is the quadcopter and the most applied machine learning task is classification. Color-infrared (CIR) images are the most preferred data used and field images are the main focus. The machine learning algorithm applied most is convolutional neural network (CNN). In addition, the challenges to pave the way for further research were provided.

show abstract

Extraction of onion fields infected by anthracnose-twister disease in selected municipalities of Nueva Ecija using UAV imageries

Cited by 8 publications

References 18 publications

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Comparative Evaluation of Tracking Capability of Spatial Patterns on Defective Urban Solar Panels between Unmanned Aerial Vehicle Video Stream and Photomosaics

Plant disease detection using drones in precision agriculture

Contact Info

Product

Resources

About