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

Abstract

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.

A New Learning Resource Retrieval Method Based on Multi-knowledge Association Mining

“…Ever since the human society has entered the era of big data, the quantity and type of digital learning resources on the Internet are increasing exponentially [1][2][3][4][5]. In front of such massive amount of learning resources, inevitably, students might get lost in the sea of knowledge or drift from the main topic from time to time, so the requirement of students for learning resource retrieval is growing and changing [6][7][8][9][10][11][12][13][14].…”

Hole Detection in Plastic Mulch Using Template Matching and Machine Learning Algorithms

“…Tao et al [11] conducted a study on the spatial distribution of dead pine trees in mountainous areas for cleaning diseased wood and predicting pine wilt with a maximum accuracy of 65%. Liu et al [12] conducted a study with a modification of the Faster Region-based Convolutional Neural Network (FRCNN) algorithm for palm tree detection to reduce overfitting problems and increase detection accuracy resulting in an accuracy of 76%. Irsanti et al [13] conducted a study to analyze the results of manual and automatic identification and counting of oil palm trees, resulting in a maximum accuracy of 94%.…”