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

Liu, Xinni; Ghazali, Kamarul Hawari; Han, Fang; Mohamed, Izzeldin Ibrahim

doi:10.1080/08839514.2020.1831226

Cited by 44 publications

(10 citation statements)

References 20 publications

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“…For the TM method, the 1890 positive samples were used as the template dataset, along with a sliding window of 40 × 40 pixels. The CV_TM_SQDIFF_NORMED from In order to evaluate the performance of the proposed EFRCNN approach, the results are compared to the LeNet CNN method used by Mubin et al [15], the traditional support vector machine (SVM) with a linear kernel and the template match (TM) method [30,31], and the original FRCNN method [21]. The CNN LeNet contains four convolutional layers with kernel size 5 × 5, max-pooling layers with a pool size of 2 × 2 and dropout layers with a rate of 0.5.…”

Section: Resultsmentioning

confidence: 99%

“…Et al. [21], the of which is the original FRCNN. The EFRCNN is far superior to the SVM and TM, exhibiting small but noticeable improvements compared to the CNN method, which gests significant advantages for large plantations.…”

Section: Datasetmentioning

confidence: 99%

“…The results are compared with an SVM (with separate feature extraction), template matching (TM), and the recent methods of Mubin et al [15] and Liu. et al [21], the latter of which is the original FRCNN. The EFRCNN is far superior to the SVM and TM, while exhibiting small but noticeable improvements compared to the CNN method, which suggests significant advantages for large plantations.…”

Section: Introductionmentioning

confidence: 99%

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

2022

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2022

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“…In 2019, Aravind et al developed deep learning-based eggplant disease detection [7]. In a recent effort, Liu et al successfully developed palm tree detection based on faster R-CNN [8]. However, no researcher has yet developed a USB detection system that oil palm mills operators need.…”

Section: Related Workmentioning

confidence: 99%

Oil palm unstripped bunch detector using modified faster regional convolutional neural network

Aji

Ghazali

Akbar

2022

IJ-AI

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The palm oil processing industry in Malaysia and Indonesia is significant and plays a vital role in the community's welfare. The efficiency of palm oil mills is characterized by the low number of unstripped bunch (USBs), so USB detection is essential in the palm oil production process. So far, USB detection is done manually and is often ignored because it is labor-intensive. We developed a USB detector based on faster regional convolutional neural network with a modified visual geometry group 16 (VGG16) backbone to solve this problem. To see the performance of our proposed USB detector, we compared it to the faster region based convolutional neural networks (R-CNN) USB detector with the VGG16 standard backbone. Based on the validation test, the USB faster R-CNN detector with modified VGG16 can improve the performance of the USB faster R-CNN detection system based on the original VGG 16 backbone. The proposed system can work faster (100% faster) with an mAP value of 0.782 (7.42% more precise) than the USB Detector with the original VGG16. In the training process, the proposed system on the speed parameter has better training parameters, which is 58.9% faster, the total loss is smaller (43.4% smaller), and the proposed system has better best accuracy (98%) than the previous system (93%). Still, it has a smaller overlap bounding box (23.91% less).

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“…The mapping and detection of individual tree crowns, tree/plant/vegetation species, crops, and wetlands from UAV-based images are achieved by diverse CNN architectures, which are used to perform different tasks, including path-based classification [78][79][80][81][82][83][84][85][86][87], object detection [88][89][90][91][92][93][94][95][96][97], and semantic segmentation [98][99][100][101][102][103][104][105][106][107]. Recently, semantic segmentation, a commonly used term in computer vision where each pixel within the input imagery is assigned to a particular class, has been a widely used technique in diverse earth-related applications [108].…”

Section: Related Workmentioning

confidence: 99%

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

et al. 2021

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Large-scale mapping of date palm trees is vital for their consistent monitoring and sustainable management, considering their substantial commercial, environmental, and cultural value. This study presents an automatic approach for the large-scale mapping of date palm trees from very-high-spatial-resolution (VHSR) unmanned aerial vehicle (UAV) datasets, based on a deep learning approach. A U-Shape convolutional neural network (U-Net), based on a deep residual learning framework, was developed for the semantic segmentation of date palm trees. A comprehensive set of labeled data was established to enable the training and evaluation of the proposed segmentation model and increase its generalization capability. The performance of the proposed approach was compared with those of various state-of-the-art fully convolutional networks (FCNs) with different encoder architectures, including U-Net (based on VGG-16 backbone), pyramid scene parsing network, and two variants of DeepLab V3+. Experimental results showed that the proposed model outperformed other FCNs in the validation and testing datasets. The generalizability evaluation of the proposed approach on a comprehensive and complex testing dataset exhibited higher classification accuracy and showed that date palm trees could be automatically mapped from VHSR UAV images with an F-score, mean intersection over union, precision, and recall of 91%, 85%, 0.91, and 0.92, respectively. The proposed approach provides an efficient deep learning architecture for the automatic mapping of date palm trees from VHSR UAV-based images.

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

Cited by 44 publications

References 20 publications

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

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

Oil palm unstripped bunch detector using modified faster regional convolutional neural network

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

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