The Usefulness Of Unmanned Airborne Vehicle (UAV) Imagery For Automated Palm Oil Tree Counting Researchjournali's Journal of Forestry (original) (raw)
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The Usefulness Of Unmanned Airborne Vehicle (UAV) Imagery For Automated Palm Oil Tree Counting
Palm oil plantations are not exceptional, one of the most valuable resources that needs to be accurately measured for better and effective management; which commonly requires not only a reliable, timely but also up-to-date data that remote sensing can provide. High resolution data are crucial in plantation management, as it provide detailed information to plantation managers for better decision making. To combine the advantages of these conventional remote sensing platforms such as high resolution and flexibility of airborne platforms and cost effectiveness of spaceborne platforms, recently Unmanned Aerial Vehicle (UAV) platform is being deployed for many remote sensing applications. Tree counting is crucial for plantation and environmental management, biodiversity monitoring and many other applications. Despite of the factor that satellite and airborne images have been widely used to detect, delineate and count individual tree in plantations, still such high resolution data sets are expensive. Hence the need to deploy the use of UAV imagery for automated palm oil tree counting. The objective of the study is to assess the usefulness of images obtained from Unman Aerial vehicle (UAV) for Automatic palm oil tree counting. The methodology is based on the concept of crown geometry and vegetation response to radiation. Spatial analysis involving the use of convolution and morphological analysis are used to detect and delineates the palm oil crown; and image thresholding is used for creating the palm oil tree centroids. The result of the thresholding was later used as input for automated palm oil tree counting. The automated tree counting was executed using ENVI EX software and an open source program “ImageJ”. The result shows that UAV data set is crucial for palm oil tree counting. The accuracy of the result was assessed by comparing with ground truth; and it is found to be 96.5% accurate. This proves that the UAV date set is suitable for automatic palm oil tree counting. The omission error may be due to the factors such as canopy overlapping or due to the image blurriness. However, 96.5% accuracy can be considered as within the range of the standard accuracy for palm oil tree counting.
The cost of acquiring real time, high resolution spatial datasets required for effective management of plantation has been a major challenge to farm management in developing country like Nigeria. But recent commercialization of Unmanned Aerial Vehicles (UAVs) has gradually made an affordable means of acquiring these spatial datasets available to individuals and small organizations. This study counts the palm trees in a palm plantation by template matching of digital orthophoto produced by a low cost Unmanned Aerial Vehicle. An aerial survey was carried out at Obafemi Awolowo Teaching and Research Farm using Phantom II Vision quadcopter. Using a flying altitude of 100 m, the plantation, which covers a land area of about 56,000 2 m and containing 663
Development of Automatic Counting System for Palm Oil Tree Based on Remote Sensing Imagery
Advances in biological sciences research, 2022
Data on the number of palm oil tree plantations on cultivated land is essential in a company's cultivation activities. Limitations of collecting data number of palm oil trees using the terrestrial method are the effectiveness of times, in terms of costs, and coverage area. Utilization of remote sensing with aerial imagery and deep learning method could present the results more efficiently. This research aims to detect and calculate the number of palm oil trees using the You Only Look Once (YOLO) version 3 architecture object detection model based on remote sensing imagery. The aerial image is collected using the Unmanned Aerial Vehicle (UAV) to train and validation the model. The detection results by the model are stored as a shapefile for further processing using the Quantum Geographic Information System (Q-GIS) to determine the number and display the detection results of palm oil trees. The total number of objects detected as trees through the model is 559 palm oil trees. The actual number of palm oil trees recorded was 590 palm oil trees. Based on the Mean Average Percentage Error (MAPE) value obtained, which is 0.057627, it shows that the model built is good and can be used to estimate the number of palm oil trees. In the future, evaluation and optimization of the model can be carried out by adjusting the number of iterations and increasing the amount of training data.
International Journal of Remote Sensing, 2016
In the past, oil palm density has been determined by manually counting trees every year in oil palm plantations. The measurement of density provides important data related to palm productivity, fertilizer needed, weed control costs in a circle around each tree, labourers needed and needs for other activities. Manual counting requires many workers and has potential problems related to accuracy. Remote sensing provides a potential approach for counting oil palm trees. The main objective of this study is to build a robust and user-friendly method that will allow oil palm managers to count oil palm trees using a remote sensing technique. The oil palm trees analysed in this study have different ages and densities. QuickBird imagery was applied with the six pansharpening methods and was compared with panchromatic QuickBird imagery. The black and white imagery from a false colour composite of pansharpening imagery was processed in three ways: (1) oil palm tree detection, (2) delineation of the oil palm area using the red band, and (3) counting oil palm trees and accuracy assessment. For oil palm detection, we used several filters that contained a Sobel edge detector, texture analysis co-occurrence, and dilate, erode, high-pass, and opening filters. The results of this study improved upon the accuracy of several previous research studies that had an accuracy of about 90-95%. The results in this study show (1) modified intensity-hue-saturation (IHS) Resolution Merge is suitable for 16-yearold oil palm trees and have rather high density with 100% accuracy; (2) Colour Normalized (Brovey) is suitable for 21-year-old oil palm trees and have low density with 99.5% accuracy; (3) Subtractive Resolution Merge is suitable for 15-and 18-year-old oil palm trees and have a rather high density with 99.8% accuracy; (4) PC Spectral Sharpening with 99.3% accuracy is suitable for 10-year-old oil palm trees and have low density; and (5) for all study object conditions, Colour Normalized (Brovey) and Wavelet Resolution Merge are two pansharpening methods that are suitable for oil palm tree extraction and counting with 98.9% and 98.4% accuracy, respectively.
Oil Palm Tree Growth Monitoring for Smallholders by Using Unmanned Aerial Vehicle
Jurnal Teknologi, 2015
The development of the latest technology in agriculture such as using Unmanned Aerial Vehicle (UAV) platform, oil palm tree monitoring can be carried out efficiently by smallholders. Therefore, this study aims to determine the spectral response curve of oil palm tree growth for smallholders by using UAV Platform and payloaded with digital compact camera. The series of UAV images are then to be used to generate an orthophotos image whereby contains two types of spectrum bands which are single spectrum of near Infra-Red (NIR) and three spectrums of visible bands (RGB), respectively. Hence, a spectral response curve graph of oil palm tree condition is able to be produced based on the orthophoto as well as on-site ground validation using handheld spectroradiometer. The growth of the oil palm trees also able to be determined by analyzing the reflectance recorded from the images after generating the Normalized Difference Vegetation Index (NDVI) and Modified Soil-Adjusted Vegetation Index 2 (MSAVI2), respectively. This study is successful determined that the low cost UAV platform and digital compact camera able to be used by smallholders in monitoring the oil palm tree growth condition by utilizing remote sensing techniques. As conclusion, this study has showed a good approach for smallholders in determining their oil palm crops condition whereby the results indicate all are identified healthy palm tree after spectral analysis from combination of NIR and RGB UAV images, respectively.
Assessing the Influence of UAV Altitude on Extracted Biophysical Parameters of Young Oil Palm
Remote Sensing, 2020
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 i...
Remote Sensing, 2019
Sustainable management of non-timber forest products such as palm fruits is crucial for the long-term conservation of intact forest. A major limitation to expanding sustainable management of palms has been the need for precise information about the resources at scales of tens to hundreds of hectares, while typical ground-based surveys only sample small areas. In recent years, small unmanned aerial vehicles (UAVs) have become an important tool for mapping forest areas as they are cheap and easy to transport, and they provide high spatial resolution imagery of remote areas. We developed an object-based classification workflow for RGB UAV imagery which aims to identify and delineate palm tree crowns in the tropical rainforest by combining image processing and GIS functionalities using color and textural information in an integrative way to show one of the potential uses of UAVs in tropical forests. Ten permanent forest plots with 1170 reference palm trees were assessed from October to ...
Turkish Journal of Agriculture - Food Science and Technology
Some vegetative properties measured in fruit trees are important indicators in examining of plant growth calculation, estimation of leaf area index in evapotranspiration, fertilizer requirement etc. These measurements reflect the effects of the cultivation treatments in many areas of commercial growing and scientific studies. One of the most important measurements is the status of the canopy development. Canopy width, area and volume can be measured with some calculations. However, more technological equipment may be needed to reduce work and labor, and to make the results more precise and clearer. Recently, unmanned aerial vehicles, which have become widespread, have a wide potential for use in agriculture. By using image processing methods, it is possible to make more objective and high accuracy evaluations much faster. In this study, the images of the apple trees (Malus domestica Borkh) cultivar Golden grafted onto MM106 rootstock, were taken by light unmanned aerial vehicle to c...
Automated Inventory of Broadleaf Tree Plantations with UAS Imagery
Remote Sensing
With the increased availability of unmanned aerial systems (UAS) imagery, digitalized forest inventory has gained prominence in recent years. This paper presents a methodology for automated measurement of tree height and crown area in two broadleaf tree plantations of different species and ages using two different UAS platforms. Using structure from motion (SfM), we generated canopy height models (CHMs) for each broadleaf plantation in Indiana, USA. From the CHMs, we calculated individual tree parameters automatically through an open-source web tool developed using the Shiny R package and assessed the accuracy against field measurements. Our analysis shows higher tree measurement accuracy with the datasets derived from multi-rotor platform (M600) than with the fixed wing platform (Bramor). The results show that our automated method could identify individual trees (F-score > 90%) and tree biometrics (root mean square error < 1.2 m for height and <1 m2 for the crown area) wit...