scholarly journals Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV)

PLoS ONE ◽  
2019 ◽  
Vol 14 (10) ◽  
pp. e0223906 ◽  
Author(s):  
Bipul Neupane ◽  
Teerayut Horanont ◽  
Nguyen Duy Hung
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Banana Plant ◽  
Detection And Counting ◽  
Aerial Vehicle ◽  
Rgb Images

scholarly journals AGB Estimation in a Tropical Mountain Forest (TMF) by Means of RGB and Multispectral Images Using an Unmanned Aerial Vehicle (UAV)

2019 ◽  
Vol 11 (12) ◽  
pp. 1413 ◽  
Author(s):  
Víctor González-Jaramillo ◽  
Andreas Fries ◽  
Jörg Bendix
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Tree Level ◽  
Mountain Forest ◽  
Multispectral Images ◽  
Individual Tree ◽  
Tropical Mountain ◽  
Aerial Vehicle ◽  
Rgb Images ◽  
Very High

The present investigation evaluates the accuracy of estimating above-ground biomass (AGB) by means of two different sensors installed onboard an unmanned aerial vehicle (UAV) platform (DJI Inspire I) because the high costs of very high-resolution imagery provided by satellites or light detection and ranging (LiDAR) sensors often impede AGB estimation and the determination of other vegetation parameters. The sensors utilized included an RGB camera (ZENMUSE X3) and a multispectral camera (Parrot Sequoia), whose images were used for AGB estimation in a natural tropical mountain forest (TMF) in Southern Ecuador. The total area covered by the sensors included 80 ha at lower elevations characterized by a fast-changing topography and different vegetation covers. From the total area, a core study site of 24 ha was selected for AGB calculation, applying two different methods. The first method used the RGB images and applied the structure for motion (SfM) process to generate point clouds for a subsequent individual tree classification. Per the classification at tree level, tree height (H) and diameter at breast height (DBH) could be determined, which are necessary input parameters to calculate AGB (Mg ha−1) by means of a specific allometric equation for wet forests. The second method used the multispectral images to calculate the normalized difference vegetation index (NDVI), which is the basis for AGB estimation applying an equation for tropical evergreen forests. The obtained results were validated against a previous AGB estimation for the same area using LiDAR data. The study found two major results: (i) The NDVI-based AGB estimates obtained by multispectral drone imagery were less accurate due to the saturation effect in dense tropical forests, (ii) the photogrammetric approach using RGB images provided reliable AGB estimates comparable to expensive LiDAR surveys (R2: 0.85). However, the latter is only possible if an auxiliary digital terrain model (DTM) in very high resolution is available because in dense natural forests the terrain surface (DTM) is hardly detectable by passive sensors due to the canopy layer, which impedes ground detection.

scholarly journals Feasibility of Combining Deep Learning and RGB Images Obtained by Unmanned Aerial Vehicle for Leaf Area Index Estimation in Rice

2020 ◽  
Vol 13 (1) ◽  
pp. 84
Author(s):  
Tomoaki Yamaguchi ◽  
Yukie Tanaka ◽  
Yuto Imachi ◽  
Megumi Yamashita ◽  
Keisuke Katsura
Keyword(s):  
Deep Learning ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Unmanned Aerial Vehicle ◽  
Regression Models ◽  
Estimation Model ◽  
Area Index ◽  
Aerial Vehicle ◽  
Rgb Images ◽  
Multispectral Camera

Leaf area index (LAI) is a vital parameter for predicting rice yield. Unmanned aerial vehicle (UAV) surveillance with an RGB camera has been shown to have potential as a low-cost and efficient tool for monitoring crop growth. Simultaneously, deep learning (DL) algorithms have attracted attention as a promising tool for the task of image recognition. The principal aim of this research was to evaluate the feasibility of combining DL and RGB images obtained by a UAV for rice LAI estimation. In the present study, an LAI estimation model developed by DL with RGB images was compared to three other practical methods: a plant canopy analyzer (PCA); regression models based on color indices (CIs) obtained from an RGB camera; and vegetation indices (VIs) obtained from a multispectral camera. The results showed that the estimation accuracy of the model developed by DL with RGB images (R2 = 0.963 and RMSE = 0.334) was higher than those of the PCA (R2 = 0.934 and RMSE = 0.555) and the regression models based on CIs (R2 = 0.802-0.947 and RMSE = 0.401–1.13), and comparable to that of the regression models based on VIs (R2 = 0.917–0.976 and RMSE = 0.332–0.644). Therefore, our results demonstrated that the estimation model using DL with an RGB camera on a UAV could be an alternative to the methods using PCA and a multispectral camera for rice LAI estimation.

scholarly journals Automated Counting of Rice Panicle by Applying Deep Learning Model to Images from Unmanned Aerial Vehicle Platform

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3106 ◽  
Author(s):  
Chengquan Zhou ◽  
Hongbao Ye ◽  
Jun Hu ◽  
Xiaoyan Shi ◽  
Shan Hua ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Field Experiments ◽  
Disease Diagnosis ◽  
Global Scale ◽  
High Definition ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Detection And Counting ◽  
Aerial Vehicle

The number of panicles per unit area is a common indicator of rice yield and is of great significance to yield estimation, breeding, and phenotype analysis. Traditional counting methods have various drawbacks, such as long delay times and high subjectivity, and they are easily perturbed by noise. To improve the accuracy of rice detection and counting in the field, we developed and implemented a panicle detection and counting system that is based on improved region-based fully convolutional networks, and we use the system to automate rice-phenotype measurements. The field experiments were conducted in target areas to train and test the system and used a rotor light unmanned aerial vehicle equipped with a high-definition RGB camera to collect images. The trained model achieved a precision of 0.868 on a held-out test set, which demonstrates the feasibility of this approach. The algorithm can deal with the irregular edge of the rice panicle, the significantly different appearance between the different varieties and growing periods, the interference due to color overlapping between panicle and leaves, and the variations in illumination intensity and shading effects in the field. The result is more accurate and efficient recognition of rice-panicles, which facilitates rice breeding. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a global scale.

scholarly journals A High-Resolution Spatial and Time-Series Labeled Unmanned Aerial Vehicle Image Dataset for Middle-Season Rice

2020 ◽  
Vol 9 (12) ◽  
pp. 728
Author(s):  
Dongbo Zhou ◽  
Shuangjian Liu ◽  
Jie Yu ◽  
Hao Li
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Temporal Information ◽  
Learning Networks ◽  
Image Dataset ◽  
Aerial Vehicle ◽  
Uav Image

The existing remote sensing image datasets target the identification of objects, features, or man-made targets but lack the ability to provide the date and spatial information for the same feature in the time-series images. The spatial and temporal information is important for machine learning methods so that networks can be trained to support precision classification, particularly for agricultural applications of specific crops with distinct phenological growth stages. In this paper, we built a high-resolution unmanned aerial vehicle (UAV) image dataset for middle-season rice. We scheduled the UAV data acquisition in five villages of Hubei Province for three years, including 11 or 13 growing stages in each year that were accompanied by the annual agricultural surveying business. We investigated the accuracy of the vector maps for each field block and the precise information regarding the crops in the field by surveying each village and periodically arranging the UAV flight tasks on a weekly basis during the phenological stages. Subsequently, we developed a method to generate the samples automatically. Finally, we built a high-resolution UAV image dataset, including over 500,000 samples with the location and phenological growth stage information, and employed the imagery dataset in several machine learning algorithms for classification. We performed two exams to test our dataset. First, we used four classical deep learning networks for the fine classification of spatial and temporal information. Second, we used typical models to test the land cover on our dataset and compared this with the UCMerced Land Use Dataset and RSSCN7 Dataset. The results showed that the proposed image dataset supported typical deep learning networks in the classification task to identify the location and time of middle-season rice and achieved high accuracy with the public image dataset.

New method for tree stands parameters determination using high resolution images done by unmanned aerial vehicle

2016 ◽  
Author(s):  
А.С. Алексеев ◽  
А.А. Никифоров ◽  
А.А. Михайлова ◽  
М.Р. Вагизов
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
New Method ◽  
Growth And Yield ◽  
Special Treatment ◽  
Resolution Image ◽  
Growing Stock ◽  
High Resolution Image ◽  
Aerial Vehicle ◽  
Area Unit

В связи со старением информационных материалов о состоянии лесов существует потребность в разработке новых методов таксации древостоев, основанных на применении последних научно-технических достижений в области теории структуры и продуктивности древостоев, дистанционных методов изучения лесов, информационных и ГИС технологий. В статье приведены результаты разработки и проверки нового метода определения таксационных характеристик сомкнутых насаждений на основе правила 3/2 и подобных ему правил Хильми и Рейнеке, с одной стороны, и определения числа деревьев на единице площади по снимку сверх высокого разрешения, полученного с помощью БПЛА, с другой. С теоретической точки зрения эта зависимости величин запаса, средней высоты и среднего диаметра от числа стволов на единице площади относятся к классу аллометрических связей, очень часто встречающихся при количественном описании соотношений частей биологических систем разных уровней иерархии, от организмов до экосистем. Параметры аллометрических зависимостей запаса, средних высоты и диаметра от числа стволов на единице площади были определены для основных лесообразующих пород по данным таблиц хода роста нормальных (полных) древостоев с теоретическим показателем степени и затем использованы для расчетов. Число деревьев на единице площади определялось по снимку с разрешением 7,13 см/пиксель, полученному с помощью 4-роторной платформы. Обработка материалов аэрофотосъемки была выполнена в специализированной фотограмметрической системе Agisoft Photoscan. В результате были получены ортофотоплан и цифровая модель поверхности крон деревьев на изучаемую территорию с определением их высот. Для автоматизированной обработки полученных изображений с целью получения значений числа деревьев на единицу площади был создан специализированный скрипт на языке Java. Погрешности определения таксационных характеристик древостоев предлагаемым методом не выше установленных действующими нормативными материалами. Every time there is a demand for new innovative methods of forest resources estimation based on last achievements in theoretical science, remote sensing methods, information and GIS-technologies. In the paper are presented a new method and the results of its application to forest stands growing stock, mean height and diameter determination. The method is based on rule 3/2 and similar Reineke and Hilmy rules, on one hand and high resolution image made by unmanned aerial vehicle, which used for determination of number of trees per area unit, on other. The above rules are well known in quantitative biology as an allometric and widely used for description of different kind of relations in biological systems of various scale: from organisms to ecosystems. Parameters of above allometric relationships between growing stock, mean height and diameter and stems density per area unit was determine on the base of full stock growth and yield tables for main tree species and after used for experimental calculations. The number of trees per area unit was determined after special treatment of high resolution image made by unmanned flying machine. The growing stock, mean height and diameter determined by suggested method was compared with the data of regular forest inventory. Comparison gives positive result and method may be recommended for further development.

scholarly journals Identifying the Branch of Kiwifruit Based on Unmanned Aerial Vehicle (UAV) Images Using Deep Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4442
Author(s):  
Zijie Niu ◽  
Juntao Deng ◽  
Xu Zhang ◽  
Jun Zhang ◽  
Shijia Pan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Semantic Segmentation ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Distribution Maps ◽  
Time Operation ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Segmentation Image

It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.

High-resolution monitoring of beach topography and its change using unmanned aerial vehicle imagery

2018 ◽  
Vol 160 ◽  
pp. 103-116 ◽  
Author(s):  
Benqing Chen ◽  
Yanming Yang ◽  
Hongtao Wen ◽  
Hailin Ruan ◽  
Zaiming Zhou ◽  
...  
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Aerial Vehicle
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