Palm Tree Detection Research Articles

Deorphanizing an odorant receptor tuned to palm tree volatile esters in the Asian palm weevil sheds light on the mechanisms of palm tree selection

The Asian palm weevil, Rhynchophorus ferrugineus, is a tremendously important agricultural pest primarily adapted to palm trees and causes severe destruction, threatening sustainable palm cultivation worldwide. The host plant selection of this weevil is mainly attributed to the functional specialization of odorant receptors (ORs) that detect palm-derived volatiles. Yet, ligands are known for only two ORs of R. ferrugineus, and we still lack information on the mechanisms of palm tree detection. This study identified a highly expressed antennal R. ferrugineus OR, RferOR2, thanks to newly generated transcriptomic data. The phylogenetic analysis revealed that RferOR2 belongs to the major coleopteran OR group 2A and is closely related to a sister clade containing an R. ferrugineus OR (RferOR41) tuned to the non-host plant volatile and antagonist, α-pinene. Functional characterization of RferOR2 via heterologous expression in Drosophila olfactory neurons revealed that this receptor is tuned to several ecologically relevant palm-emitted odors, most notably ethyl and methyl ester compounds, but not to any of the pheromone compounds tested, including the R. ferrugineus aggregation pheromone. We did not evidence any differential expression of RferOR2 in the antennae of both sexes, suggesting males and females detect these compounds equally. Next, we used the newly identified RferOR2 ligands to demonstrate that including synthetic palm ester volatiles as single compounds and in combinations in pheromone-based mass trapping has a synergistic attractiveness effect to R. ferrugineus aggregation pheromone, resulting in significantly increased weevil catches. Our study identified a key OR from a palm weevil species tuned to several ecologically relevant palm volatiles and represents a significant step forward in understanding the chemosensory mechanisms of host detection in palm weevils. Our study also defines RferOR2 as an essential model for exploring the molecular basis of host detection in other palm weevil species. Finally, our work showed that insect OR deorphanization could aid in identifying novel behaviorally active volatiles that can interfere with weevil host-searching behavior in sustainable pest management applications.

Large-scale assessment of date palm plantations based on UAV remote sensing and multiscale vision transformer

Timely and efficient mapping of date palm plantations through unmanned aerial vehicle (UAV) remote sensing is critical for continuous observation, health and risk evaluation, pest management, resource optimization, and ensuring the long-term sustainability of the dates industry. This study presents an efficient and cost-effective transformer-based approach to identify, countify, monitor, and evaluate the overall well-being of palm trees using extensive UAV imagery. The suggested approach integrates an improved multiscale vision transformer, feature pyramid network, Mask R–CNN, and improved slicing-aided hyper inference for practical large-scale assessments. This combination enabled the extraction of multiscale features, capturing long-range dependencies in the data and boosting the model's generalizability. The proposed architecture outperformed several CNN-based architectures (including Mask R–CNN, Cascade Mask R–CNN, Point-based Rendering, and You Only Look At CoefficientTs), achieving F-scores of 94.33% and 94.2% for date palm tree detection and segmentation, respectively. The transformer-based architecture was optimized using transfer learning to differentiate between healthy and unhealthy date palm trees, particularly those with severe infestations. The potential generic condition of date palm trees was predicted with an F-score of 88.4%. Further advancements in this field could pave the way for a proactive strategy, enabling timely detection, which would aid in pest management and support the sustainable growth of the dates sector.

Multiphase Domain Adaptation Mechanism for Drone Vision-based Automated Oil Palm Tree Detection Task

Multiphase Domain Adaptation Mechanism for Drone Vision-based Automated Oil Palm Tree Detection Task

Detection and Analysis of Dubas-Infested Date Palm Trees Using Deep Learning, Remote Sensing, and GIS Techniques in Wadi Bani Kharus

Many insects attack date palm trees but date palm trees in the Sultanate are particularly under threat due to the spread of pests and the Dubas bug (Db). Date palm productivity in Oman has been reduced by 28% due to Db infestation. The manual field detection of these pests requires huge efforts and costs, making field surveys time consuming and difficult. In this context, remote sensing integrated with deep learning techniques can help in the early detection of Db infestation. A total of 240 date palms with corrected geospatial locations and coordinates and their health status were systematically recorded throughout the 66-square-kilometer study area. We used advanced remote sensing tools and deep learning techniques to detect individual palm trees and their health levels in terms of Db infestation. Very-high-resolution (50 cm) satellite images rendered in visible and NIR bands were used as datasets to delineate and identify individual tree positions and determine their health condition. Our proposed method resulted in an overall accuracy of 87% for the detection of date palm trees and 85% for the detection of health levels of the plants. The overall detection accuracy of high and low infestation levels was observed with high precision at 95% and 93%, respectively. Hence, we can conclude with confidence that our technique performed well by accurately detecting individual date palm trees and determining their level of Db infestation. The approach used in this study can also provide farmers with useful knowledge regarding the Db risk and damage control for better management of Db. Moreover, the model used in this study may also lay the foundations for other models to detect infested plants and trees other than date palms.

Sustainable Palm Tree Farming: Leveraging IoT and Multi-Modal Data for Early Detection and Mapping of Red Palm Weevil

The Red Palm Weevil (RPW) is a highly destructive insect causing economic losses and impacting palm tree farming worldwide. This paper proposes an innovative approach for sustainable palm tree farming by utilizing advanced technologies for early detection and management of RPW. Our approach combines computer vision, deep learning (DL), the Internet of Things (IoT), and geospatial data to effectively detect and classify RPW-infested palm trees. The main phases include; (1) DL Classification using sound data from IoT devices, (2) palm tree detection using YOLOv8 on UAV images, and (3) RPW mapping using geospatial data. Our custom DL model achieves 100% precision and recall in detecting and localizing infested palm trees. The integration of geospatial data enables the creation of a comprehensive RPW distribution map for Efficient monitoring and targeted management strategies. This technology-driven approach benefits agricultural authorities, farmers, and researchers in managing RPW infestations, safeguarding palm tree plantations’ productivity.

Deep convolutional neural networks and Swin transformer-based frameworks for individual date palm tree detection and mapping from large-scale UAV images

Timely and reliable mapping of individual date palm trees is essential for their monitoring, health and risk assessment, pest control, and sustainable management of the date palm industry. This study presents an instance segmentation framework for large-scale detection and mapping of date palm trees using unmanned aerial vehicle (UAV)-based images. First, a data conversion framework is created to convert UAV image tiles and ground-truth vector data into annotation format of Common Objects in Context. Second, this study examines the efficacy of various instance segmentation models, namely, mask region convolutional neural network (Mask R-CNN), Mask Scoring R-CNN, You Only Look At CoefficientTs, Point-based Rendering, Segmenting Objects by Locations (SOLO), and SOLOv2) with varying residual learning networks (ResNets) in detecting and delineating individual date palm trees. Furthermore, the performance of two variants of Swin Transformer networks with a feature pyramid network (FPN) (Swin-small-FPN and Swin-tiny-FPN) as Mask R-CNN network backbones was also evaluated. Third, we assess the generalizability of the evaluated instance segmentation models and backbones on different testing datasets with varying spatial resolutions. Results show that Mask R-CNN models based on Swin Transformers backbones outperform those with ResNets in the detection and segmentation of date palm trees with mAP50 of 92% and 91% and F-measures of 94% and 93%. Moreover, the Mask scoring R-CNN-based ResNet-50 and Mask R-CNN with a Swin-small-FPN backbone outperform the evaluated models and demonstrate great generalizability in different datasets with diverse spatial resolutions. The proposed instance segmentation framework provides an efficient tool for date palm tree mapping from multi-scale UAV-based images and is valuable and suitable for individual tree crown delineations and other earth-related applications.

Recognition of Sago Palm Trees Based on Transfer Learning

Sago palm tree, known as Metroxylon Sagu Rottb, is one of the priority commodities in Indonesia. Based on our previous research, the potential habitat of the plant has been decreasing. On the other hand, while the use of remote sensing is now widely developed, it is rarely applied for detection and classification purposes, specifically in Indonesia. Considering the potential use of the plant, local farmers identify the harvest time by using human inspection, i.e., by identifying the bloom of the flower. Therefore, this study aims to detect sago palms based on their physical morphology from Unmanned Aerial Vehicle (UAV) RGB imagery. Specifically, this paper endeavors to apply the transfer learning approach using three deep pre-trained networks in sago palm tree detection, namely, SqueezeNet, AlexNet, and ResNet-50. The dataset was collected from nine different groups of plants based on the dominant physical features, i.e., leaves, flowers, fruits, and trunks by using a UAV. Typical classes of plants are randomly selected, like coconut and oil palm trees. As a result, the experiment shows that the ResNet-50 model becomes a preferred base model for sago palm classifiers, with a precision of 75%, 78%, and 83% for sago flowers (SF), sago leaves (SL), and sago trunk (ST), respectively. Generally, all of the models perform well for coconut trees, but they still tend to perform less effectively for sago palm and oil palm detection, which is explained by the similarity of the physical appearance of these two palms. Therefore, based our findings, we recommend improving the optimized parameters, thereby providing more varied sago datasets with the same substituted layers designed in this study.

Evaluation The Accuracy of Oil Palm Tree Detection Using Deep Learning and Support Vector Machine Classifiers

Oil palm is one of the important sources of vegetable oil that is mostly consumed by the Malaysian citizen. Because the demand of vegetable oil is high in Malaysia, the expansion of oil palm tree plantation has been increasing rapidly. Remote sensing application in monitoring and detecting oil palm trees has become a very useful tool to help minimize the human energy to monitor in large plantations. The aim of this study is to evaluate the accuracy of oil palm tree detection using deep learning and support vector machine (SVM) approaches using high-resolution remote sensing images. Deep learning is one of the base frameworks for oil palm tree detection using high-resolution remote sensing images. Deep learning also is a counting tool provided in newest technology software such as ArcGIS Pro, where the tools use the pattern recognition concept as a template in detecting objects in a high-resolution image. In machine learning, support vector machines are supervised learning models with associated machine learning algorithms that analyse data for classification analysis. Based on this study, 91% of the oil palm trees detected using deep learning approach gained higher accuracy than SVM classifier with 86%. The accuracy of oil palm tree detection using deep learning is higher than the accuracy of support vector machine classifier. Based on the findings, deep learning approach create better object interpretation than SVM. The oil palm tree detection using both classification approaches had also been displayed by using a spatial map distribution for easier analysed and observation.

Sustainable Oil Palm Resource Assessment Based on an Enhanced Deep Learning Method

Knowledge of the number and distribution of oil palm trees during the crop cycle is vital for sustainable management and predicting yields. The accuracy of the conventional image processing method is limited for the hand-crafted feature extraction method and the overfitting problem occurs due to the insufficient dataset. We propose a modification of the Faster Region-based Convolutional Neural Network (FRCNN) for palm tree detection to reduce the overfitting problem and improve the detection accuracy. The enhanced FRCNN (EFRCNN) leads to improved performance for detecting objects (in the same image) when they are of multiple sizes by using a feature concatenation method. Transfer learning based on a ResNet50 model is used to extract the features of the input image. High-resolution images of oil palm trees from a drone are used to form the data set, containing mature, young, and mixed oil palm tree regions. We train and test the EFRCNN, the FRCNN, a CNN used recently for oil palm image detection, and two standard methods, namely, the support vector machine (SVM) and template matching (TM). The results reveal an overall accuracy of ≥96.8% for the EFRCNN on the three test sets. The accuracy is higher than the CNN and FRCNN and substantially higher than SVM and TM. For large-scale plantations, the accuracy improvement is significant. This research provides a method for automatically counting the oil palm trees in large-scale plantations.

Leveraging Artificial Intelligence Techniques for Smart Palm Tree Detection: A Decade Systematic Review

Over the past few years, total financial investment in the agricultural sector has increased substantially. Palm tree is important for many countries’ economies, particularly in northern Africa and the Middle East. Monitoring in terms of detection and counting palm trees provides useful information for a variety of stakeholders; it helps in yield estimation and examination to ensure better crop quality and prevent pests, diseases, better irrigation and other potential threats. Despite their importance, these information still difficult to obtain. In this study, we systematically review research articles between 2011 and 2021 on artificial intelligence (AI) technology for smart palm tree detection. A systematic review (SR) was performed using the PRISMA approach based on a four-stage selection process. Twenty-two articles were included for the synthesis activity reached from the search strategy alongside the inclusion criteria in order to answer tow two main research questions. The study's findings reveal patterns, relationships, networks, and trends in the application of artificial intelligence in the palm tree detection over the last decade. Overall, despite the good results achieved in most of the studies, the effective and efficient management of large-scale palm plantations still a challenge. In addition, countries which their economy strongly related to intelligent palm services especially in North Africa should give more attention to this kind of studies. The results of this research could benefit both the research community and stakeholders.

Oil palm tree counting in drone images

When the images are captured by drones, the effect of oblique angles, distance variations and open environment are the main challenges for successful palm tree detection. This paper presents a method towards palm tree counting in Drone images using a novel idea of detecting dominant points by exploring Generalized Gradient Vector Flow, which defines symmetry based on gradient direction of the pixels. For each dominant point, we use angle information for classifying diagonal dominant points. It is intuition that the direction of the branches of tree converges at center of tree irrespective of the type of tree and plants. This observation motivated us to expand the direction of diagonal dominant points until it finds intersection point with another diagonal dominant point and this results in candidate points. For each candidate point, the proposed method constructs the ring by considering the distance between the intersection point and nearest neighbor candidate point as radius. This outputs region of interest and it includes center of each tree in the image. To ease the effect of complex background, we explore YOLOv5 architecture to remove false region of interests. This step results in counting oil palm trees in the mages irrespective of tree type of palm family. Experimental results on our dataset of the images captured by drones and standard dataset of coconut images captured by unmanned aerial vehicle of different trees show that the proposed method is effective and performs better than SOTA methods.

Deep neural network based date palm tree detection in drone imagery

Date palm trees are an important economic crop in the Arabian Peninsula, Middle East, and North Africa. Counting the numbers and determining the locations of date palm trees are important for predicting the date production and plantation management. In this paper, we exploit the effective use of the state-of-the-art CNN, YOLO-V5, in detecting date palm trees in images captured by a camera onboard of a drone flying 122 m above farmlands in the Northern Emirates of the United Arab Emirates (UAE). In the dataset preparation process, we randomly selected 125 captured images and divided them into three datasets: training (60%), validation (20%), and testing (20%). The images of date palm trees in the training and validation datasets were manually annotated and those in the training dataset were used to train the four sub-versions of YOLO-V5 CNNs. The validation dataset was used during the training process to assess how well the network was performing during training. Finally, the images in the test dataset were used to evaluate the performance of the trained models. The results of using YOLO-V5 for date palm tree detection in drone imagery are compared with those obtainable with other popular CNN architectures, YOLO-V3, YOLO-V4, and SSD300, both quantitatively and qualitatively. The results show that for the amount of training data used, YOLO-V5m (medium depth) model records the highest accuracy, resulting in a mean average precision of 92.34%. Further it provides the ability to detect and localize date palm trees of different sizes, in crowded, overlapped environments and areas where the date palm tree distribution is sparse. Therefore, it is concluded that the method can be a useful component of an automated plantation management system and help forecast the quantities of date production and condition monitoring of the date palm trees.

Multisource-Domain Generalization-Based Oil Palm Tree Detection Using Very-High-Resolution (VHR) Satellite Images

Providing accurate and timely oil palm information on a large scale is essential for both economic development and ecological significance. However, owing to different sensors, photograph acquisition conditions, and environmental heterogeneity, the large volume and the variety of the data make it extremely challenging for large-scale and cross-regional oil palm tree detection. It is computationally expensive to train a model from images covering large heterogeneous regions and all environmental conditions for continuously accumulated multisource remote sensing data. In this letter, we propose a new multisource domain generalization (DG) method, Maximum Mean Discrepancy Deep Reconstruction Classification Network (MMD-DRCN). It learns representations from multiple source domains and obtains inspiring performance in an unknown and “unseen” target domain. Besides classification loss, our MMD-DRCN distills more representative features through reconstruction loss and aligns multisource latent features by MMD loss, both of which effectively enhance the capacity of generalization. MMD-DRCN achieves an average F1-score of 82.70% in all transfer tasks, attaining a 5.83% gain compared to Baseline (a straightforward convolutional neural network (CNN) model). Experimental results demonstrate DG poses a promising potential for large-scale and cross-regional oil palm tree detection without any information of the target domain.

Oil Palm Tree Detection and Health Classification on High-Resolution Imagery Using Deep Learning

Combining modern technology and agriculture is an important consideration for the effective management of oil palm trees. In this study, an alternative method for oil palm tree management is proposed by applying high-resolution imagery, combined with Faster-RCNN, for automatic detection and health classification of oil palm trees. This study used a total of 4172 bounding boxes of healthy and unhealthy palm trees, constructed from 2000 pixel × 2000 pixel images. Of the total dataset, 90% was used for training and 10% was prepared for testing using Resnet-50 and VGG-16. Three techniques were used to assess the models’ performance: model training evaluation, evaluation using visual interpretation, and ground sampling inspections. The study identified three characteristics needed for detection and health classification: crown size, color, and density. The optimal altitude to capture images for detection and classification was determined to be 100 m, although the model showed satisfactory performance up to 140 m. For oil palm tree detection, healthy tree identification, and unhealthy tree identification, Resnet-50 obtained F1-scores of 95.09%, 92.07%, and 86.96%, respectively, with respect to visual interpretation ground truth and 97.67%, 95.30%, and 57.14%, respectively, with respect to ground sampling inspection ground truth. Resnet-50 yielded better F1-scores than VGG-16 in both evaluations. Therefore, the proposed method is well suited for the effective management of crops.

KDT-SPSO: A multimodal particle swarm optimisation algorithm based on k-d trees for palm tree detection

Palm tree detection, as any other problem of object localisation in an image, can be formulated mathematically as a multimodal optimisation problem that seeks for the best set of positions and scales that maximise the classification score. In recent decades, many bio-inspired multimodal optimisation algorithms using different niching techniques have been proven effective and robust in solving optimisation problems. In many niching-based algorithms, e.g., species-based particle swarm optimisation (SPSO), repetitive distance evaluations to search for nearest neighbours are unavoidable to find multiple optima in the search space, which are computationally expensive. In this paper, we propose an improved SPSO named KDT-SPSO to speed up the nearest neighbour search using a special tree-based structure, called k-d tree. KDT-SPSO reduces the computation complexity by only visiting the subtrees that most likely contain the neighbours. We statistically tested KDT-SPSO on a number of benchmark functions and the results showed that it was up to 48% faster than the original SPSO. After proving its effectiveness, we optimised the parameters of KDT-SPSO for palm tree detection and introduced a restart mechanism to speed up its convergence and to improve the recall rate. The local binary pattern (LBP) feature extractor and modified support vector machine (SVM) classifier with radial basis function (RBF) kernel were used to compute the fitness value of palm trees. The optimised KDT-SPSO was able to achieve on average 91.80% palm tree detection accuracy and 5 times faster than the traditional sliding window approach.

Growing status observation for oil palm trees using Unmanned Aerial Vehicle (UAV) images

For both the positive economic benefit and the negative ecological impact of the rapid expansion of oil palm plantations in tropical developing countries, it is significant to achieve accurate detection for oil palm trees in large-scale areas. Especially, growing status observation and smart oil palm plantation management enabled by such accurate detections would improve plantation planning, oil palm yield, and reduce manpower and consumption of fertilizer. Although existing studies have already reached a high accuracy in oil palm tree detection, rare attention has been paid to automated observation of each single oil palm tree’s growing status. Nowadays, with its high spatial resolution and low cost, Unmanned Aerial Vehicle (UAV) has become a promising tool for monitoring the growing status of individual oil palms. However, the accuracy is still a challenging issue because of the extreme imbalance and high similarity between different classes. In this paper, we propose a Multi-class Oil PAlm Detection approach (MOPAD) to reap both accurate detection of oil palm trees and accurate monitoring of their growing status. Based on Faster RCNN, MOPAD combines a Refined Pyramid Feature (RPF) module and a hybrid class-balanced loss module to achieve satisfying observation of the growing status for individual oil palms. The former takes advantage of multi-level features to distinguish similar classes and detect small oil palms, and the latter effectively resolves the problem of extremely imbalanced samples. Moreover, we elaborately analyze the distribution of different kinds of oil palms, and propose a practical workflow for detecting oil palm vacancy. We evaluate MOPAD using three large-scale UAV images photographed in two sites in Indonesia (denoted by Site 1 and Site 2), containing 363,877 oil palms of five categories: healthy palms, dead palms, mismanaged palms, smallish palms and yellowish palms. Our proposed MOPAD achieves an F1-score of 87.91% (Site 1) and 99.04% (Site 2) for overall oil palm tree detection, and outperforms other state-of-the-art object detection methods by a remarkable margin of 10.37–17.09% and 8.14%-21.32% with respect to the average F1-score for multi-class oil palm detection in Site 1 and Site 2, respectively. Our method demonstrates excellent potential for individual oil palm tree detection and observation of growing status from UAV images, leading to more precise and efficient management of oil palm plantations.

Analysis of Oil Palm Tree Recognition using Drone-Based Remote Sensing Images

The oil palm tree, or scientifically called as Elaeis guineensis is native to West Africa, where it grows in the wild, transformed into a crop that later was introduced to Malaysian industry. The cultivation of oil palm improved rapidly under the agricultural sector causes degradation, particularly when the oil palm plantation goes uncontrolled. Tree plantation identification is very important for plantation management, environmental management, biodiversity monitoring and many other applications. Accurate inventories and monitoring oil palm estates can be a challenge and critical towards the plantation management and plant area expansion. Managing oil palm estate manually can almost be impossible, so do the tree counting. Manual field-based tree counting is time-consuming and high cost. Conventional method for tree counting can be carried out by manually marked on images or carry out field surveying using GPS to collect the positions of oil palm trees and display their position on image. Developing easier, simpler and cheaper method for tree counting is needed. The aim of this study is to analyse oil palm trees using drone-based remote sensing images. The algorithms used in this research study including Gray-Level Co-occurrence Matrix (GLCM), wavelet transform and template matching. The database of oil palm tree been developed with a total of 131 oil palm trees and 161 of non-oil palm trees have been collected. The window size of oil palm tree been analysed where 250 x 250 pixels which GLCM showed the best overall accuracy of 73.10% for both oil palm and non-oil palm. In this specific window, the oil palm crown can be covered and the result given is more accurate compared to other window sizes. The resulting analysis shows that wavelet transform algorithm gives the highest overall accuracy value which is 82.07%. The other eight statistic parameters can also used to modify the GLCM in order to observe the accuracy and identify which give the best classification accuracy. The availability and ubiquity of drone technologies with high resolution images and regular basis monitoring, new techniques in image and pattern recognition using drone-based remote sensing images let the idea of high accuracy oil palm tree detection become a reality.

Automatic Detection of Oil Palm Tree from UAV Images Based on the Deep Learning Method

ABSTRACT Palm oil is a major contributor to Malaysia’s GDP in the agriculture sector. The sheer vastness of oil palm plantations requires a huge effort to administer. An oil palm plantation in regards to the irrigation process, fertilization, and planning for planting new trees require an audit process to correctly count the oil palm trees. Currently, the audit is done manually using aerial view images. Therefore, an effective and efficient method is imperative. This paper proposes a new automatic end-to-end method based on deep learning (DL) for detection and counting oil palm trees from images obtained from unmanned aerial vehicle (UAV) drone. The acquired images were first cropped and sampled into small size of sub-images, which were divided into a training set, a validation set, and a testing set. A DL algorithm based on Faster-RCNN was employed to build the model, extracts features from the images and identifies the oil palm trees, and gives information on the respective locations. The model was then trained and used to detect individual oil palm tree based on data from the testing set. The overall accuracy of oil palm tree detection was measured from three different sites with 97.06%, 96.58%, and 97.79% correct oil palm detection. The results show that the proposed method is more effective, accurate detection, and correctly counts the number of oil palm trees from the UAV images.

Cross-regional oil palm tree counting and detection via a multi-level attention domain adaptation network

Providing an accurate evaluation of palm tree plantation in a large region can bring meaningful impacts in both economic and ecological aspects. However, the enormous spatial scale and the variety of geological features across regions has made it a grand challenge with limited solutions based on manual human monitoring efforts. Although deep learning based algorithms have demonstrated potential in forming an automated approach in recent years, the labelling efforts needed for covering different features in different regions largely constrain its effectiveness in large-scale problems. In this paper, we propose a novel domain adaptive oil palm tree detection method, i.e., a Multi-level Attention Domain Adaptation Network (MADAN) to reap cross-regional oil palm tree counting and detection. MADAN consists of 4 procedures: First, we adopted a batch-instance normalization network (BIN) based feature extractor for improving the generalization ability of the model, integrating batch normalization and instance normalization. Second, we embedded a multi-level attention mechanism (MLA) into our architecture for enhancing the transferability, including a feature level attention and an entropy level attention. Then we designed a minimum entropy regularization (MER) to increase the confidence of the classifier predictions through assigning the entropy level attention value to the entropy penalty. Finally, we employed a sliding window-based prediction and an IOU based post-processing approach to attain the final detection results. We conducted comprehensive ablation experiments using three different satellite images of large-scale oil palm plantation area with six transfer tasks. MADAN improves the detection accuracy by 14.98% in terms of average F1-score compared with the Baseline method (without DA), and performs 3.55–14.49% better than existing domain adaptation methods. Experimental results demonstrate the great potential of our MADAN for large-scale and cross-regional oil palm tree counting and detection, guaranteeing a high detection accuracy as well as saving the manual annotation efforts.

Improved Fast R-CNN with Fusion of Optical and 3D Data for Robust Palm Tree Detection in High Resolution UAV Images

Improved Fast R-CNN with Fusion of Optical and 3D Data for Robust Palm Tree Detection in High Resolution UAV Images