scholarly journals Segmentation of Apples in Aerial Images under Sixteen Different Lighting Conditions Using Color and Texture for Optimal Irrigation

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1634 ◽  
Author(s):  
Sajad Sabzi ◽  
Yousef Abbaspour-Gilandeh ◽  
Ginés García-Mateos ◽  
Antonio Ruiz-Canales ◽  
José Miguel Molina-Martínez

Due to the changes in the lighting intensity and conditions throughout the day, machine vision systems used in precision agriculture for irrigation management should be prepared for all possible conditions. For this purpose, a complete segmentation algorithm has been developed for a case study on apple fruit segmentation in outdoor conditions using aerial images. This algorithm has been trained and tested using videos with 16 different light intensities from apple orchards during the day. The proposed segmentation algorithm consists of five main steps: (1) transforming frames in RGB to CIE L*u*v* color space and applying thresholds on image pixels; (2) computing texture features of local standard deviation; (3) using intensity transformation to remove background pixels; (4) color segmentation applying different thresholds in RGB space; and (5) applying morphological operators to refine the results. During the training process of this algorithm, it was observed that frames in different light conditions had more than 58% color sharing. Results showed that the accuracy of the proposed segmentation algorithm is higher than 99.12%, outperforming other methods in the state of the art that were compared. The processed images are aerial photographs like those obtained from a camera installed in unmanned aerial vehicles (UAVs). This accurate result will enable more efficient support in the decision making for irrigation and harvesting strategies.

2019 ◽  
Vol 11 (10) ◽  
pp. 1157 ◽  
Author(s):  
Jorge Fuentes-Pacheco ◽  
Juan Torres-Olivares ◽  
Edgar Roman-Rangel ◽  
Salvador Cervantes ◽  
Porfirio Juarez-Lopez ◽  
...  

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.


2021 ◽  
Author(s):  
Andreas Linsbauer ◽  
Matthias Huss ◽  
Elias Hodel ◽  
Andreas Bauder ◽  
Mauro Fischer ◽  
...  

<p>With increasing anthropogenic greenhouse gas emissions and corresponding global warming, glaciers in Switzerland are shrinking rapidly as in many mountain ranges on Earth. Repeated glacier inventories are a key task to monitor such glacier changes and provide detailed information on the extent of glaciation, and important parameters such as area, elevation range, slope, aspect etc. for a given point or a period in time. Here we present the new Swiss Glacier Inventory (SGI2016) that has been acquired based on high-resolution aerial imagery and digital elevation models in cooperation with the Federal Office of Topography (swisstopo) and Glacier Monitoring in Switzerland (GLAMOS), bringing together topological and glaciological knowhow. We define the process, workflow and required glaciological adaptations to compile a highly accurate glacier inventory based on the digital Swiss topographic landscape model (swissTLM<sup>3D</sup>).</p><p>The SGI2016 provides glacier outlines (areas), supraglacial debris cover, ice divides and location points of all glaciers in Switzerland referring to the years 2013-2018, whereas most of the glacier outlines have been mapped based on aerial images acquired between 2015-2017 (75% in number and 87% in area), with the centre year 2016. The SGI2016 maps 1400 individual glacier entities with a total glacier surface area of 961 km<sup>2</sup> (whereof 11% / 104 km<sup>2</sup> are debris-covered) and constitutes the so far most detailed cartographic representation of glacier extent in Switzerland. Analysing the dependencies between topographic parameters and debris-cover fraction on the basis of individual glaciers reveals that short glaciers with a moderate mean slope and glaciers with a low median elevation tend to have high debris fractions. A change assessment between the SGI1973 and SGI2016 based on individual glacier entities affirms the largest relative area changes for small glaciers and for low-elevation glaciers, whereas the largest glaciers show small relative area changes, though large absolute changes. The analysis further indicates a tendency for glaciers with a high share of supraglacial debris to show larger relative area changes.</p><p>Despite of an observed strong glacier volume loss between 2010 and 2016, the total glacier surface area of the SGI2016 is somewhat larger than reported in the last Swiss glacier inventory SGI2010. Even though both inventories were created based on swisstopo aerial photographs, the additional data, tools, resources and methodologies used by the professional cartographers digitizing glacier outlines in 3D for the SGI2016, are able to explain the counter-intuitive difference between SGI2010 and SGI2016. A direct comparison of these two datasets is thus not meaningful, but an experiment where a representative glacier sample of the SGI2010 was re-assessed based on the approaches of the SGI2016 led to an upscaled total glacier surface area of 1010 km<sup>2</sup> for the Swiss Alps around 2010. This indicates an area loss of 49 km<sup>2</sup> between the two last Swiss glacier inventories. As swisstopo data products are and will be regularly updated, the SGI2016 is the first step towards a consistent and accurate data product of repeated glacier inventories in six-year time intervals that promises a high comparability for individual glaciers and glacier samples.</p>


2018 ◽  
Vol 23 (3) ◽  
pp. 123
Author(s):  
Indriatmoko Indriatmoko ◽  
Dimas A. Hedianto ◽  
Sari Budi Moria ◽  
Didik WH Tjahjo

Giant tiger shrimp (Penaeus monodon) has become a prime commodity in Indonesia which was produced by aquaculture and capture fisheries activities. Aceh Province, in this case mostly represented by Aceh Timur District, was well-known as the center of wild-captured-adult giant tiger shrimp. Several previous investigations had proved for its high-quality shrimp spawner in producing good eggs in quality and quantity under artificial spawning condition. Two main interesting points of wild giant tiger shrimp from Aceh Timur came from their coloration and population clusters. This report was aimed to provide that information pre-preliminary and highlighted quantitative information of coloration characteristic through RGB (Red Green Blue) and CIE Lab color space data analysis, as well as, 16S rDNA-PCR-RFLP genetic comparison among four population clusters in Aceh Timur Waters. The color analysis resulted in significant differences between wild-captured and pond-cultured giant tiger shrimp which produced R value 0.1524±0.0091 and 0.1268±0.0004, respectively. Total pixel analysis through L* a* b* color space has distinguished detailed differentiation between wild-captured and pond-cultured giant tiger shrimp acquired images. It is known that most of the wild-captured image pixels were concentrated in quadrant I (+a, +b) while pond-cultured in quadrant II (-a, +b) and III (-a, -b).Genotyping of represented samples from 4 population clusters, i.e. Aceh Tamiang, Langsa, Peudawa, and Julok produce 2 haplotype composite, AAA and AAB. Among 4 clusters, it was found that Julok has become the only cluster which has a different haplotype composite ratio (1:1) (D 0.0348, V 0,9501) from the others (4:1)(V 0.9504).


2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mu-Chun Su ◽  
Chun-Yen Cheng ◽  
Pa-Chun Wang

This paper presents a new white blood cell classification system for the recognition of five types of white blood cells. We propose a new segmentation algorithm for the segmentation of white blood cells from smear images. The core idea of the proposed segmentation algorithm is to find a discriminating region of white blood cells on the HSI color space. Pixels with color lying in the discriminating region described by an ellipsoidal region will be regarded as the nucleus and granule of cytoplasm of a white blood cell. Then, through a further morphological process, we can segment a white blood cell from a smear image. Three kinds of features (i.e., geometrical features, color features, and LDP-based texture features) are extracted from the segmented cell. These features are fed into three different kinds of neural networks to recognize the types of the white blood cells. To test the effectiveness of the proposed white blood cell classification system, a total of 450 white blood cells images were used. The highest overall correct recognition rate could reach 99.11% correct. Simulation results showed that the proposed white blood cell classification system was very competitive to some existing systems.


2020 ◽  
Vol 12 (18) ◽  
pp. 3030
Author(s):  
Ram Avtar ◽  
Stanley Anak Suab ◽  
Mohd Shahrizan Syukur ◽  
Alexius Korom ◽  
Deha Agus Umarhadi ◽  
...  

The information on biophysical parameters—such as height, crown area, and vegetation indices such as the normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE)—are useful to monitor health conditions and the growth of oil palm trees in precision agriculture practices. The use of multispectral sensors mounted on unmanned aerial vehicles (UAV) provides high spatio-temporal resolution data to study plant health. However, the influence of UAV altitude when extracting biophysical parameters of oil palm from a multispectral sensor has not yet been well explored. Therefore, this study utilized the MicaSense RedEdge sensor mounted on a DJI Phantom–4 UAV platform for aerial photogrammetry. Three different close-range multispectral aerial images were acquired at a flight altitude of 20 m, 60 m, and 80 m above ground level (AGL) over the young oil palm plantation area in Malaysia. The images were processed using the structure from motion (SfM) technique in Pix4DMapper software and produced multispectral orthomosaic aerial images, digital surface model (DSM), and point clouds. Meanwhile, canopy height models (CHM) were generated by subtracting DSM and digital elevation models (DEM). Oil palm tree heights and crown projected area (CPA) were extracted from CHM and the orthomosaic. NDVI and NDRE were calculated using the red, red-edge, and near-infrared spectral bands of orthomosaic data. The accuracy of the extracted height and CPA were evaluated by assessing accuracy from a different altitude of UAV data with ground measured CPA and height. Correlations, root mean square deviation (RMSD), and central tendency were used to compare UAV extracted biophysical parameters with ground data. Based on our results, flying at an altitude of 60 m is the best and optimal flight altitude for estimating biophysical parameters followed by 80 m altitude. The 20 m UAV altitude showed a tendency of overestimation in biophysical parameters of young oil palm and is less consistent when extracting parameters among the others. The methodology and results are a step toward precision agriculture in the oil palm plantation area.


Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1651 ◽  
Author(s):  
Suk-Ju Hong ◽  
Yunhyeok Han ◽  
Sang-Yeon Kim ◽  
Ah-Yeong Lee ◽  
Ghiseok Kim

Wild birds are monitored with the important objectives of identifying their habitats and estimating the size of their populations. Especially in the case of migratory bird, they are significantly recorded during specific periods of time to forecast any possible spread of animal disease such as avian influenza. This study led to the construction of deep-learning-based object-detection models with the aid of aerial photographs collected by an unmanned aerial vehicle (UAV). The dataset containing the aerial photographs includes diverse images of birds in various bird habitats and in the vicinity of lakes and on farmland. In addition, aerial images of bird decoys are captured to achieve various bird patterns and more accurate bird information. Bird detection models such as Faster Region-based Convolutional Neural Network (R-CNN), Region-based Fully Convolutional Network (R-FCN), Single Shot MultiBox Detector (SSD), Retinanet, and You Only Look Once (YOLO) were created and the performance of all models was estimated by comparing their computing speed and average precision. The test results show Faster R-CNN to be the most accurate and YOLO to be the fastest among the models. The combined results demonstrate that the use of deep-learning-based detection methods in combination with UAV aerial imagery is fairly suitable for bird detection in various environments.


2019 ◽  
Vol 11 (2) ◽  
pp. 108 ◽  
Author(s):  
Lu Xu ◽  
Dongping Ming ◽  
Wen Zhou ◽  
Hanqing Bao ◽  
Yangyang Chen ◽  
...  

Extracting farmland from high spatial resolution remote sensing images is a basic task for agricultural information management. According to Tobler’s first law of geography, closer objects have a stronger relation. Meanwhile, due to the scale effect, there are differences on both spatial and attribute scales among different kinds of objects. Thus, it is not appropriate to segment images with unique or fixed parameters for different kinds of objects. In view of this, this paper presents a stratified object-based farmland extraction method, which includes two key processes: one is image region division on a rough scale and the other is scale parameter pre-estimation within local regions. Firstly, the image in RGB color space is converted into HSV color space, and then the texture features of the hue layer are calculated using the grey level co-occurrence matrix method. Thus, the whole image can be divided into different regions based on the texture features, such as the mean and homogeneity. Secondly, within local regions, the optimal spatial scale segmentation parameter was pre-estimated by average local variance and its first-order and second-order rate of change. The optimal attribute scale segmentation parameter can be estimated based on the histogram of local variance. Through stratified regionalization and local segmentation parameters estimation, fine farmland segmentation can be achieved. GF-2 and Quickbird images were used in this paper, and mean-shift and multi-resolution segmentation algorithms were applied as examples to verify the validity of the proposed method. The experimental results have shown that the stratified processing method can release under-segmentation and over-segmentation phenomena to a certain extent, which ultimately benefits the accurate farmland information extraction.


Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 443 ◽  
Author(s):  
Marek Kovar ◽  
Marian Brestic ◽  
Oksana Sytar ◽  
Viliam Barek ◽  
Pavol Hauptvogel ◽  
...  

Nondestructive assessment of water content and water stress in plants is an important component in the rational use of crop irrigation management in precision agriculture. Spectral measurements of light reflectance in the UV/VIS/NIR region (350–1075 nm) from individual leaves were acquired under a rapid dehydration protocol for validation of the remote sensing water content assessment in soybean plants. Four gravimetrical approaches of leaf water content assessment were used: relative water content (RWC), foliar water content as percent of total fresh mass (FWCt), foliar water content as percent of dry mass (FWCd), and equivalent water thickness (EWT). Leaf desiccation resulted in changes in optical properties with increasing relative reflectance at wavelengths between 580 and 700 nm. The highest positive correlations were observed for the relations between the photochemical reflectance index (PRI) and EWT (rP = 0.860). Data analysis revealed that the specific water absorption band at 970 nm showed relatively weaker sensitivity to water content parameters. The prediction of leaf water content parameters from PRI measurements was better with RMSEs of 12.4% (rP = 0.786), 9.1% (rP = 0.736), and 0.002 (rP = 0.860) for RWC, FWCt, and EWT (p < 0.001), respectively. The results may contribute to more efficient crop water management and confirmed that EWT has a statistically closer relationship with reflectance indices than other monitored water parameters.


Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1508 ◽  
Author(s):  
Rafael González Perea ◽  
Aida Mérida García ◽  
Irene Fernández García ◽  
Emilio Camacho Poyato ◽  
Pilar Montesinos ◽  
...  

Climate change, water scarcity and higher energy requirements and electric tariff compromises the continuity of the irrigated agriculture. Precision agriculture (PA) or renewable energy sources which are based on communication and information technologies and a large amount of data are key to ensuring this economic activity and guaranteeing food security at the global level. Several works which are based on the use of PA and renewable energy sources have been developed in order to optimize different variables of irrigated agriculture such as irrigation scheduling. However, the large amount of technologies and sensors that these models need to be implemented are still far from being easily accessible and usable by farmers. In this way, a middleware called Real time Smart Solar Irrigation Manager (RESSIM) has been developed in this work and implemented in MATLABTM with the aim to provide to farmers a user-friendly tool for the daily making decision process of irrigation scheduling using a smart photovoltaic irrigation management module. RESSIM middleware was successfully tested in a real field during a full irrigation season of olive trees using a real smart photovoltaic irrigation system.


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