Implementation of Hybrid Algorithm for the UAV Images Preprocessing Based on Embedded Heterogeneous System: The Case of Precision Agriculture

2021 ◽  
pp. 151-164
Author(s):  
Rachid Latif ◽  
Laila Jamad ◽  
Amine Saddik
Keyword(s):  
Precision Agriculture ◽  
Hybrid Algorithm ◽  
Heterogeneous System ◽  
Uav Images

scholarly journals AUTOMATED CO-REGISTRATION OF INTRA-EPOCH AND INTER-EPOCH SERIES OF MULTISPECTRAL UAV IMAGES FOR CROP MONITORING

2020 ◽  
Vol V-1-2020 ◽  
pp. 309-316
Author(s):  
P. O. Mc’Okeyo ◽  
F. Nex ◽  
C. Persello ◽  
A. Vrieling
Keyword(s):  
Precision Agriculture ◽  
Spectral Response ◽  
Radiometric Calibration ◽  
Registration Accuracy ◽  
Maize Crop ◽  
Crop Monitoring ◽  
Speeded Up Robust Features ◽  
Novel Approach ◽  
Matching Strategy ◽  
Uav Images

Abstract. The application of UAV-based aerial imagery has advanced exponentially in the past two decades. This can be attributed to UAV operational flexibility, ultra-high spatial resolution, inexpensiveness, and UAV-based sensors enhancement. Nonetheless, the application of multitemporal series of multispectral UAV imagery still suffers significant misregistration errors, and therefore becoming a concern for applications such as precision agriculture. Direct image georeferencing and co-registration is commonly done using ground control points; this is usually costly and time consuming. This research proposes a novel approach for automatic co-registration of multitemporal UAV imagery using intensity-based keypoints. The Speeded Up Robust Features (SURF), Binary Robust Invariant Scalable Keypoints (BRISK), Maximally Stable Extremal Regions (MSER) and KAZE algorithms, were tested and parameters optimized. Image matching performance of these algorithms informed the decision to pursue further experiments with only SURF and KAZE. Optimally parametrized SURF and KAZE algorithms obtained co-registration accuracies of 0.1 and 0.3 pixels for intra-epoch and inter-epoch images respectively. To obtain better intra-epoch co-registration accuracy, collective band processing is advised whereas one-to-one matching strategy is recommended for inter-epoch co-registration. The results were tested using a maize crop monitoring case and the; comparison of spectral response of vegetation between the UAV sensors, Parrot Sequoia and Micro MCA was performed. Due to the missing incidence sensor, spectral and radiometric calibration of Micro MCA imagery is observed to be key in achieving optimal response. Also, the cameras have different specifications and thus differ in the quality of their respective photogrammetric outputs.

scholarly journals A Double Swath Configuration for Improving Throughput and Accuracy of Trait Estimate from UAV Images

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wenjuan Li ◽  
Alexis Comar ◽  
Marie Weiss ◽  
Sylvain Jay ◽  
Gallian Colombeau ◽  
...  
Keyword(s):  
Plant Height ◽  
Precision Agriculture ◽  
Focal Length ◽  
Critical Issue ◽  
Quality Level ◽  
Ground Sample ◽  
Height Estimates ◽  
Uav Images ◽  
Time Required ◽  
Multispectral Camera

Multispectral observations from unmanned aerial vehicles (UAVs) are currently used for precision agriculture and crop phenotyping applications to monitor a series of traits allowing the characterization of the vegetation status. However, the limited autonomy of UAVs makes the completion of flights difficult when sampling large areas. Increasing the throughput of data acquisition while not degrading the ground sample distance (GSD) is, therefore, a critical issue to be solved. We propose here a new image acquisition configuration based on the combination of two focal length (f) optics: an optics with f=4.2 mm is added to the standard f=8 mm (SS: single swath) of the multispectral camera (DS: double swath, double of the standard one). Two flights were completed consecutively in 2018 over a maize field using the AIRPHEN multispectral camera at 52 m altitude. The DS flight plan was designed to get 80% overlap with the 4.2 mm optics, while the SS one was designed to get 80% overlap with the 8 mm optics. As a result, the time required to cover the same area is halved for the DS as compared to the SS. The georeferencing accuracy was improved for the DS configuration, particularly for the Z dimension due to the larger view angles available with the small focal length optics. Application to plant height estimates demonstrates that the DS configuration provides similar results as the SS one. However, for both the DS and SS configurations, degrading the quality level used to generate the 3D point cloud significantly decreases the plant height estimates.

scholarly journals A Comparison of UAV and Satellites Multispectral Imagery in Monitoring Onion Crop. An Application in the ‘Cipolla Rossa di Tropea’ (Italy)

2020 ◽  
Vol 12 (20) ◽  
pp. 3424 ◽  
Author(s):  
Gaetano Messina ◽  
Jose M. Peña ◽  
Marco Vizzari ◽  
Giuseppe Modica
Keyword(s):  
Information And Communication Technologies ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Bare Soil ◽  
Spatial And Temporal Variability ◽  
Biotic Stresses ◽  
Object Based ◽  
Spectral Contribution ◽  
Information And Communication ◽  
Uav Images

Precision agriculture (PA) is a management strategy that analyzes the spatial and temporal variability of agricultural fields using information and communication technologies with the aim to optimize profitability, sustainability, and protection of agro-ecological services. In the context of PA, this research evaluated the reliability of multispectral (MS) imagery collected at different spatial resolutions by an unmanned aerial vehicle (UAV) and PlanetScope and Sentinel-2 satellite platforms in monitoring onion crops over three different dates. The soil adjusted vegetation index (SAVI) was used for monitoring the vigor of the study field. Next, the vigor maps from the two satellite platforms with those derived from UAV were compared by statistical analysis in order to evaluate the contribution made by each platform for monitoring onion crops. Besides, the two coverage’s classes of the field, bare soil and onions, were spatially identified using geographical object-based image classification (GEOBIA), and their spectral contribution was analyzed comparing the SAVI calculated considering only crop pixels (i.e., SAVI onions) and that calculated considering only bare soil pixels (i.e., SAVI soil) with the SAVI from the three platforms. The results showed that satellite imagery, coherent and correlated with UAV images, could be useful to assess the general conditions of the field while UAV permits to discriminate localized circumscribed areas that the lowest resolution of satellites missed, where there are conditions of inhomogeneity in the field, determined by abiotic or biotic stresses.

scholarly journals Expedited generation of terrain digital classes in flat areas from UAV images for precision agriculture purposes

2017 ◽  
Vol 8 (2) ◽  
pp. 828-832
Author(s):  
M. C. Pineda ◽  
C. Perdomo ◽  
R. Caballero ◽  
A. Valera ◽  
J. A. Martínez-Casasnovas ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Topographic Wetness Index ◽  
Site Specific ◽  
Soil Variables ◽  
Digital Terrain ◽  
Digital Elevation ◽  
Elevation Model ◽  
Uav Images ◽  
Specific Management

Precision agriculture (PA) requires reasonably homogeneous areas for site-specific management. This work explores the applicability of digital terrain classes obtained from a digital elevation model derived from UAV-acquired images, to define management units in in a relative flat area of about 6 ha. Elevation, together with other terrain variables such as: slope degree, profile curvature, plan curvature, topographic wetness index, sediment transport index, were clustered using the Fuzzy Kohonen Clustering Network (FKCN). Four terrain classes were obtained. The result was compared with a map produced by a classification of soil properties previously interpolated by ordinary kriging. The results suggest that areas for site-specific management can be defined from terrain classes based on environmental covariates, saving time and cost in comparison with interpolation of soil variables.

scholarly journals Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images

2018 ◽  
Vol 10 (11) ◽  
pp. 1690 ◽  
Author(s):  
M Bah ◽  
Adel Hafiane ◽  
Raphael Canals
Keyword(s):  
Deep Learning ◽  
Precision Agriculture ◽  
Training Data ◽  
Training Dataset ◽  
Second Phase ◽  
Agricultural Field ◽  
Classification Problems ◽  
Weed Detection ◽  
Uav Images ◽  
The Right

In recent years, weeds have been responsible for most agricultural yield losses. To deal with this threat, farmers resort to spraying the fields uniformly with herbicides. This method not only requires huge quantities of herbicides but impacts the environment and human health. One way to reduce the cost and environmental impact is to allocate the right doses of herbicide to the right place and at the right time (precision agriculture). Nowadays, unmanned aerial vehicles (UAVs) are becoming an interesting acquisition system for weed localization and management due to their ability to obtain images of the entire agricultural field with a very high spatial resolution and at a low cost. However, despite significant advances in UAV acquisition systems, the automatic detection of weeds remains a challenging problem because of their strong similarity to the crops. Recently, a deep learning approach has shown impressive results in different complex classification problems. However, this approach needs a certain amount of training data, and creating large agricultural datasets with pixel-level annotations by an expert is an extremely time-consuming task. In this paper, we propose a novel fully automatic learning method using convolutional neuronal networks (CNNs) with an unsupervised training dataset collection for weed detection from UAV images. The proposed method comprises three main phases. First, we automatically detect the crop rows and use them to identify the inter-row weeds. In the second phase, inter-row weeds are used to constitute the training dataset. Finally, we perform CNNs on this dataset to build a model able to detect the crop and the weeds in the images. The results obtained are comparable to those of traditional supervised training data labeling, with differences in accuracy of 1.5% in the spinach field and 6% in the bean field.

scholarly journals Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks

Drones ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 39 ◽  
Author(s):  
Ovidiu Csillik ◽  
John Cherbini ◽  
Robert Johnson ◽  
Andy Lyons ◽  
Maggi Kelly
Keyword(s):  
Precision Agriculture ◽  
Fruit Production ◽  
Research Priority ◽  
Disease Occurrence ◽  
Aerial Vehicle ◽  
Simple Linear Iterative Clustering ◽  
Uav Images ◽  
Crop Condition ◽  
Individual Trees ◽  
Citrus Trees

Remote sensing is important to precision agriculture and the spatial resolution provided by Unmanned Aerial Vehicles (UAVs) is revolutionizing precision agriculture workflows for measurement crop condition and yields over the growing season, for identifying and monitoring weeds and other applications. Monitoring of individual trees for growth, fruit production and pest and disease occurrence remains a high research priority and the delineation of each tree using automated means as an alternative to manual delineation would be useful for long-term farm management. In this paper, we detected citrus and other crop trees from UAV images using a simple convolutional neural network (CNN) algorithm, followed by a classification refinement using superpixels derived from a Simple Linear Iterative Clustering (SLIC) algorithm. The workflow performed well in a relatively complex agricultural environment (multiple targets, multiple size trees and ages, etc.) achieving high accuracy (overall accuracy = 96.24%, Precision (positive predictive value) = 94.59%, Recall (sensitivity) = 97.94%). To our knowledge, this is the first time a CNN has been used with UAV multi-spectral imagery to focus on citrus trees. More of these individual cases are needed to develop standard automated workflows to help agricultural managers better incorporate large volumes of high resolution UAV imagery into agricultural management operations.

scholarly journals An Comprehensive Survey on Applications of Precision Agriculture in the Context of Weed Classification, Leave Disease Detection, Yield Prediction and UAV Image Analysis

2021 ◽  
Author(s):  
Preethi C ◽  
Brintha NC ◽  
Yogesh CK
Keyword(s):  
Image Analysis ◽  
Precision Agriculture ◽  
The State ◽  
Disease Detection ◽  
Yield Prediction ◽  
Comprehensive Survey ◽  
Uav Images ◽  
Uav Image ◽  
Image Analysis Techniques ◽  
State Of Art

Advancement in technologies such as Machine vision, Machine Learning, Deep Learning algorithms enables them to extend its horizon in different applications including precision agriculture. The objective of this work is to study the various works pertaining to precision agriculture under four categories, weed classification, disease detection in leaves, yield prediction and image analysis techniques in UAV. In case of the weed classification, both classifying weeds from the crops and classifying the different types of weeds are analysed. In disease detection, only the diseases that occur in the leaves of different plants are considered and studied. It is continued with the state of art models that predicts yields of different crops. The last part of the work concentrates on analysing the images captured UAV in the context of precision agriculture. This work would pave a way for getting a deep insight about the state of art models related to the above specified applications of precision agriculture and the methods of analysing the UAV images.

scholarly journals VddNet: Vine Disease Detection Network Based on Multispectral Images and Depth Map

2020 ◽  
Vol 12 (20) ◽  
pp. 3305
Author(s):  
Mohamed Kerkech ◽  
Adel Hafiane ◽  
Raphael Canals
Keyword(s):  
Deep Learning ◽  
Precision Agriculture ◽  
Depth Map ◽  
Semantic Segmentation ◽  
Learning Technologies ◽  
Disease Detection ◽  
Registration Method ◽  
Multispectral Data ◽  
3D Processing ◽  
Uav Images

Vine pathologies generate several economic and environmental problems, causing serious difficulties for the viticultural activity. The early detection of vine disease can significantly improve the control of vine diseases and avoid spread of virus or fungi. Currently, remote sensing and artificial intelligence technologies are emerging in the field of precision agriculture. They offer interesting potential for crop disease management. However, despite the advances in these technologies, particularly deep learning technologies, many problems still present considerable challenges, such as semantic segmentation of images for disease mapping. In this paper, we present a new deep learning architecture called Vine Disease Detection Network (VddNet). It is based on three parallel auto-encoders integrating different information (i.e., visible, infrared and depth). Then, the decoder reconstructs and retrieves the features, and assigns a class to each output pixel. An orthophotos registration method is also proposed to align the three types of images and enable the processing by VddNet. The proposed architecture is assessed by comparing it with the most known architectures: SegNet, U-Net, DeepLabv3+ and PSPNet. The deep learning architectures were trained on multispectral data from an unmanned aerial vehicle (UAV) and depth map information extracted from 3D processing. The results of the proposed architecture show that the VddNet architecture achieves higher scores than the baseline methods. Moreover, this study demonstrates that the proposed method has many advantages compared to methods that directly use the UAV images.

Robust Long-Term Registration of UAV Images of Crop Fields for Precision Agriculture

2018 ◽  
Vol 3 (4) ◽  
pp. 3097-3104 ◽  
Author(s):  
Nived Chebrolu ◽  
Thomas Labe ◽  
Cyrill Stachniss
Keyword(s):  
Precision Agriculture ◽  
Crop Fields ◽  
Uav Images

scholarly journals Deep Learning with Unsupervised Data Labeling for Weeds Detection on UAV Images

2018 ◽  
Author(s):  
M Dian Bah ◽  
Adel Hafiane ◽  
Raphael Canals
Keyword(s):  
Deep Learning ◽  
Precision Agriculture ◽  
Low Cost ◽  
Classification Problem ◽  
Training Data ◽  
Training Dataset ◽  
Second Phase ◽  
Agricultural Field ◽  
Uav Images ◽  
The Right

In recent years, weeds is responsible for most of the agricultural yield losses. To deal with this threat, farmers resort to spraying pesticides throughout the field. Such method not only requires huge quantities of herbicides but impact environment and humans health. One way to reduce the cost and environmental impact is to allocate the right doses of herbicide at the right place and at the right time (Precision Agriculture). Nowadays, Unmanned Aerial Vehicle (UAV) is becoming an interesting acquisition system for weeds localization and management due to its ability to obtain the images of the entire agricultural field with a very high spatial resolution and at low cost. Despite the important advances in UAV acquisition systems, automatic weeds detection remains a challenging problem because of its strong similarity with the crops. Recently Deep Learning approach has shown impressive results in different complex classification problem. However, this approach needs a certain amount of training data but, creating large agricultural datasets with pixel-level annotations by expert is an extremely time consuming task. In this paper, we propose a novel fully automatic learning method using Convolutional Neuronal Networks (CNNs) with unsupervised training dataset collection for weeds detection from UAV images. The proposed method consists in three main phases. First we automatically detect the crop lines and using them to identify the interline weeds. In the second phase, interline weeds are used to constitute the training dataset. Finally, we performed CNNs on this dataset to build a model able to detect the crop and weeds in the images. The results obtained are comparable to the traditional supervised training data labeling. The accuracy gaps are 1.5% in the spinach field and 6% in the bean field.

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