Detecting BSR-infected oil palm seedlings using thermal imaging technique

Basal Stem Rot (BSR) is the most destructive disease instigated by a white wood rotting fungus called Ganoderma boninense, which cause great economic setback in oil palm productivity. It attacks the basal stem of oil palm trees, causing them to slowly rot. It also affects the xylem tissues that eventually interrupt water transportation to the upper part of the oil palm, turning the leaves at the frond become yellow. This problem should be prevented during nursery stage by separating between healthy and BSR-infected seedling. Therefore, this study focuses on the potential use of thermal imaging for detecting BSR in oil palm at seedling. Thermal images of oil palm seedling from healthy and BSR-infected were captured and processed to extract several thermal properties of the seedling, i.e., maximum, minimum, mean, and standard deviation of pixel intensity value. These values were then undergone statistical analysis to identify its significant different in differentiating healthy and BSR-infected seedling. Several classification models were tested including Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM) and k-Nearest Neighbour (kNN). Principal Component Analysis (PCA) was used to reduce the dimensionality of the dataset. The results demonstrated that the highest accuracy achieved at 80.0 % using SVM (fine gaussian) classification model with PC1 and PC3 as the input parameter. This summarizes the potential of thermal imaging in detecting BSR-infected oil palm trees at seedling stage

Identification of dorsal and vertical surface of rubber seeds using image processing approach

Natural rubber tree or known as Hevea brasiliensis is an important economic resource for the world and one of major plantation crops in Malaysia. To increase rubber production, planting method of rubber seeds must be improvised. Proper placement of seeds are important in order to increase the germination rate of rubber seeds. Rubber has two different surfaces which are dorsal and vertical. There are numerous darker mottles on the dorsal surface whereas only a straight valley at the centre of vertical surface. Vertical surface needs to be placed downward, attaching to the soil and dorsal surface needs to be placed on the top, facing to the sky. Current method of planting rubber seed is by growing the seedlings in a nursery. It needs many labors to plant the seeds one by one in a polybags. This caused high cost of production due to high labor intensity. To reduce the labor intensity and improving the production efficiency, it is necessary to use an automatic detection technology. This study was conducted to identify the dorsal and vertical surface of rubber seeds using image processing approach. There were 1600 images of dorsal and vertical surfaces at different positions acquired using SM-P605 of Samsung Galaxy Tab in RGB color format. Significant difference between dorsal and vertical surface can be seen clearly at the center of the seed. In this study, horizontal position of the rubber seed image was used as the reference. Therefore, after underwent all the image pre-processing steps, the orientation of the seed was identified. The seed was rotated into horizontal position based on the identified orientation. Then, canny edge detection was used to extract the important edge at the center of the seed in the horizontal based. From the center edge region, five features were extracted i.e. maximum length of x-axis, ratio of y-axis to x-axis, number of pixels inside edge region, maximum convolution and number of intersections. These features were used to develop a new prediction model using conditional statement method in identifying dorsal and vertical surface. Besides prediction model, support vector machine (SVM) and artificial neural network (ANN) were also used to classify dorsal and vertical surface. The result had shown that all the samples were successfully rotated into the horizontal positions with an average of error of 0.52%. The developed prediction model gave the most accurate result with 88.75% accuracy as compared to ANN (82.61%) and SVM (72.25%)

Automated rubber seed ventral surface identification using hue, saturation, value HSV image processing and a decision rule approach

Rubber seeds should be planted and handled correctly to boost the germination rate by placing the ventral surface facing down and adhering to the soil. Traditionally, this planting technique has been performed manually by labourers. Automation is not only the key to solving labour shortage issues but can also improve the production performance. Hence, this study was conducted to identify the dorsal and ventral surface of rubber seeds using image processing techniques of hue, saturation, value colour space and a decision rule approach. Five features were extracted at the centre of the seed based on the detected edge images, namely maximum length, ratio of major and minor axis, number of pixels, maximum convolution and number of intersections. These features were used as a dataset to develop new prediction models using a decision rule and an artificial neural network (ANN). Based on the results, it was found that the decision rule model performed better with a higher value of accuracy (88.75%), sensitivity (90%) and specificity (87.50%) compared to ANN. This was most likely due to the rules prepared by applying expert knowledge when developing a decision rule model. On the other hand, the development of the prediction model was created based on the analysis of each feature. This study could benefit the rubber industry, especially for the nursery application during the planting process, where it can potentially reduce time and labour intensity while increasing production efficiency at the same time

Relationship between soil moisture content in paddy field and its image texture

The aim of this study is to identify the relationship between soil moisture content and its image texture. Soil image was captured and converted into CIELUV color space. These images were later used to develop two dimensional gray level co-occurrence matrix. Eight texture features extracted from gray level co-occurrence matrix namely mean, variance, homogeneity, dissimilarity, entropy, contrast, second moment and correlation was used for the analysis. The results has shown that the image texture properties can be used to relate with soil moisture content, where variance, homogeneity, dissimilarity, entropy, contrast, second moment and correlation gave significant responds to the moisture content. The highest value of correlation was gathered from entropy with r = -0.522

Identification of bagworm (Metisa plana) instar stages using hyperspectral imaging and machine learning techniques

A serious outbreak of leaf-eating insects namely bagworm (Lepidoptera: Psychidae), especially Metisa plana species, may cause a 43% yield loss in oil palm production due to late proper control of bagworm populations. Identification of the bagworm instar stage is important to ensure proper control measures are applied in the infested area. This study aims to distinguish the bagworm larvae from second (S2) to fifth (S5) instar stages using hyperspectral imaging and machine learning technique. The capability of spectral reflectance and morphological features namely area, perimeter, major axis length, and minor axis length to classify the instar stage were studied. A total of 2000 sample points of larva were extracted from hyperspectral images. It was then followed by the identification of sensitive wavelengths of each stage using analysis of variance (ANOVA). Results show that seven wavelengths from the blue and green band (i.e., 470 nm, 490 nm, 502 nm, 506 nm, 526 nm, 538 nm, and 554 nm) gave the most significant difference in distinguishing the larval instar stages. To provide a more economical approach, only two wavelengths were used for model development. Later, the classifications models were developed separately using five different types of datasets: (A) significant morphological feature, (B) all significant wavelengths, (C) two wavelengths from the same spectral region, (D) two wavelengths from different spectral regions, and (E) two significant wavelengths and a significant morphological feature. Results have shown the dataset which used green bands at 506 nm and 538 nm with a weighted k-nearest neighbour classifier achieved the best value of accuracy (91% – 95%), precision (0.83 – 0.87), sensitivity (0.77 – 0.99), specificity (0.94 – 0.96) and F1-score (0.81 – 0.91). It was mainly due to green pigments which strongly correlates with the chlorophyll content of the frond leaves fed by the larvae to build and enlarge the case. The capability of the model to detect the young larval instar stages (S2 - S3) where an active feeding activity takes place allows quick decisions about outbreak control measures

Automatic Classification of Bagworm, <i>Metisa plana</i> (Walker) Instar Stages Using a Transfer Learning-Based Framework

Bagworms, particularly Metisa plana Walker (Lepidoptera: Psychidae), are one of the most destructive leaf-eating pests, especially in oil palm plantations, causing severe defoliation which reduces yield. Due to the delayed control of the bagworm population, it was discovered to be the most widespread oil palm pest in Peninsular Malaysia. Identification and classification of bagworm instar stages are critical for determining the current outbreak and taking appropriate control measures in the infested area. Therefore, this work proposes an automatic classification of bagworm larval instar stage starting from the second (S2) to the fifth (S5) instar stage using a transfer learning-based framework. Five different deep CNN architectures were used i.e., VGG16, ResNet50, ResNet152, DenseNet121 and DenseNet201 to categorize the larval instar stages. All the models were fine-tuned using two different optimizers, i.e., stochastic gradient descent (SGD) with momentum and adaptive moment estimation (Adam). Among the five models used, the DenseNet121 model, which used SGD with momentum (0.9) had the best classification accuracy of 96.18% with a testing time of 0.048 s per sample. Besides, all the instar stages from S2 to S5 can be identified with high value accuracy (94.52–97.57%), precision (89.71–95.87%), sensitivity (87.67–96.65%), specificity (96.51–98.61%) and the F1-score (88.89–96.18%). The presented transfer learning approach yields promising results, demonstrating its ability to classify bagworm instar stages