Image-Based Measurement of Leaf Area Index and Radiation Interception for Modelling of Oil Palm

Leaf area index (LAI) is an important parameter for precise characterization of the plant canopy structure. M I describes a fundamental property of the plant canopy that has often been used as a critical variable to simulate the growth and yield models. The present conventional method used in determining LA1 is laborious, difficult and time consuming. Thus an imagebased measurement using camera system with fish eye lens offers an alternative means for accurate indirect measurement of LA1 in oil palm. In this study leaf area index was determined by direct and indirect methods. The LAI-2000 plant canopy analyser (PCA), fish eye lens with charge couple device (CCD) camera and radiation sensor were used as indirect methods. Results show that the LA1 value was overestimated (30.8%-153%) for immature palm and underestimated (24%-52%) for mature palm as compared to direct measurement. The MI-2000 PCA reading varies according to the condition of sky, measuring technique, view cap, height of the measuring point and shade. Four models (leaflet shape factor model,frond area model, leaflet dry weight model and leaflet area model) were tested for accurate estimation of leaf area. Results show that the leaflet dry weight was strongly correlated (r = 0.98) with leaflet area. Light interception by a canopy is a fundamental requirement for crop growth and important for biomass production and plant growth modelling. In this study, two operational methods for estimating the amount of photosynthetically active radiation intercepted by a canopy of the oil palm were developed, i.e. "Triangular" method and "Circular" method. Results show that both methods were suitable for oil palm PAR measurement. A nonlinear relationship was found between radiation interception and LAI. Results show that the radiation interception decreased with increasing distance from the frond base to frond tip. Hemispherical photography was used in this study to estimate the leaf area index and gap fraction in oil palm plantation. Photographs were taken from different palm ages i.e. 2, 3, 6, 7, 9, 13 and 16-year old after field planting. The average LAI values obtained from photography method were 0.68 to 1.71 for 2 to 16-year old palms. The average LA1 value was underestimated as compared to destructive method. The LA1 values need to be multiplied by a conversion factor to get the accurate LA1 as obtained from the photography method. For palms less than 5-year olds, the photographic method gave the accurate MI value. The gap fractions obtained from photographic method ranged from 0.51 to 0.18 for palm age range from 2 to 16-year old palms. The gap fraction was linearly correlated (r = 0.99) with leaf area index.Computer simulation models have become powerful tools to enhance information derived from costly and laborious field experiments. Particularly in oil palms where field experiments are expensive, time consuming and labours intensive. A computer simulation model was developed using Visual C++ computer language for simulation of leaf area index and yield of oil palm. The simulated results were reasonably comparable to the field data for both LA1 and yield. The LA1 data was collected by field experiment for 2 to16- year old palms, whereas yield data was obtained from Malaysian Palm Oil Board (MPOB). A strong linear relationship was found between the measured LA1 and the simulated LA1 with correlation coefficient r of 0.96. A good linear relationship (r = 0.95) was found between the simulated LA1 and the simulated yield. Also a strong relationship (r = 0.98) was found between the simulated yield and observed yield. The proposed photographic method for LA1 estimation, different regression models, methodology for PAR measurement as well as computer program for LA1 and yield simulation have potential application in oil palm sector, oil palm R&D and also as teaching tools

An Empirical Study on the Effectiveness of Testing Metrics to Test Deep Learning Models

In recent years, Deep Learning (DL) models have widely been applied to develop safety and security critical systems. The recent evolvement of Deep Neural Networks (DNNs) is the key reason behind the unprecedented achievements in image classification, object detection, medical image analysis, speech recog nition, and autonomous driving. However, DL models often remain a black box for their practitioners due to the lack of interpretability and explainability. DL practitioners generally use standard metrics such as Precision, Recall, and F1 score to evaluate the performance of DL models on the test dataset. However, as high-quality test data is not frequently accessed, the expected level of accuracy of these standard metrics on test datasets cannot justify the trustworthiness of testing adequacy, generality and robustness of DL models. The way we ensure the quality of DL models is still in its infancy; hence, a scalable DL model testing frame work is highly demanded in the context of software testing. The existing techniques for testing traditional software systems could not be directly applicable to DL models because of the fundamental difference in pro gramming paradigm, systems development methodologies, and processes. However, several testing metrics (e.g., Neuron Coverage (NC), Confusion and Bias error metrics, and Multi-granularity metrics) have been proposed leveraging the concept of test coverage in traditional software testing to measure the robustness of DL models and the quality of the test datasets. Although test coverage is highly effective to test traditional software systems, the effectiveness of DL coverage metrics must be evaluated in testing the robustness of DL models and measuring the quality of the test datasets. In addition, the selected testing metrics work on the activated neurons of a DL model. In our study, we consider the neuron count of a DL model differently than the existing studies. For example, according to our calculation the LeNet-5 model has 6508 neurons whereas other studies consider the LeNet-5 model contains 268 neurons only. Therefore, it is also important to in vestigate how neurons’ concept (e.g., the idea of having neurons in the DL model and the way we calculate the number of neurons a DL model does have) impact the testing metrics. In this thesis, we thus conduct an exploratory study for evaluating the effectiveness of the testing metrics to test DL models not only in measuring their robustness but also in assessing the quality of the test datasets. Furthermore, since selected testing metrics work on the activated neurons of a DL model, we also investigate the impact of the neurons’ concepts on the testing metrics. To conduct our experiments, we select popular publicly available datasets (e.g., MNIST, Fashion MNIST, CIFAR-10, ImageNet and so on) and train DL models on them. We also select sate-of-the-art DL models (e.g., VGG-16, VGG-19, ResNet-50, ResNet-101 and so on) trained on the ImageNet dataset. Our experimental results demonstrate that whatever the neuron’s concepts are, NC and Multi-granularity testing metrics are ineffective in evaluating the robustness of DL models and in assessing the quality of the test datasets. In addition, the selection of threshold values has a negligible impact on the NC metric. Increasing the coverage values of the Multi-granularity testing metrics can not separate regular test data from adversarial test data. Our exploratory study also shows that the DL models still make accurate predictions with higher coverage values of Multi-granularity metrics than the false predictions. Therefore, it is not always true that increasing coverage values of the Multi-granularity testing metrics find more defects of DL models. Finally, the Precision and Recall scores show that the Confusion and Bias error metrics are adequate to detect class-level violations of the DL models

Robust statistical approaches for feature extraction in laser scanning 3D point cloud data

Three dimensional point cloud data acquired from mobile laser scanning system commonly contain outliers and/or noise. The presence of outliers and noise means most of the frequently used methods for feature extraction produce inaccurate and non-robust results. We investigate the problems of outliers and how to accommodate them for automatic robust feature extraction. This thesis develops algorithms for outlier detection, point cloud denoising, robust feature extraction, segmentation and ground surface extraction

The Unethical Practices of the Pharmaceutical Industries in Bangladesh in their Drug Promotion and its Impacts

This article highlights the unethical practices of pharmaceutical industries in Bangladesh in their drug promotion and its impacts. It is evident that pharmaceutical industry is one of the promising sectors in the industrial field of Bangladesh. Now, the pharmaceutical industries of Bangladesh are exporting their products to more than 100 countries in the world after meeting the local demand. This study also represents the growth and market share of pharmaceutical industries, total exports of last five years, unethical practices in promotion of pharmaceutical industries and impacts of unethical practices. To conduct the study both secondary and primary data have been used. The target population of this study was medical professional men including physicians of Phultala Thana in Khulna District. Finally the study provides some recommendations to overcome the situation. There are a lot of rooms for Bangladesh to develop in this sector. In this study, the unethical practices in promotion of pharmaceutical industries that can make obstacle in the sustainable growth of pharmaceutical business in Bangladesh are discussed meticulously

Crops diseases detection and solution system

The technology based modern agriculture industries are today’s requirement in every part of agriculture in Bangladesh. In this technology, the disease of plants is precisely controlled. Due to the variable atmospheric circumstances these conditions sometimes the farmer doesn’t know what type of disease on the plant and which type of medicine provide them to avoid diseases. This research developed for crops diseases detection and to provide solutions by using image processing techniques. We have used Android Studio to develop the system. The crops diseases detection and solution system is compared the image of affected crops with database of CDDASS (Crops Diseases Detection and Solution system). If CDDASS detect any disease symptom, then provide suggestion so that farmers can take proper decision to provide medicine to the affected crops. The application has developed with user friendly features so that farmers can use it easily

Green Revolution in Ready Made Garments in Bangladesh: An Analytical Study

Bangladesh, a small south Asian country, holds the second position in the world of exporting readymade garments (RMG). Here locate the highest number of green RMG factories in the world. Green industrialization is the positive symbol of sustainable development. This paper represents an impressive illustration of the scenario of RMG industries in Bangladesh especially the growth, contribution to export and the success of LEED (Leadership in Energy and Environmental Design) recognized Platinum, Gold and Silver factories.  This paper also represents the success of green revolution in RMG industries in Bangladesh and recommends some suggestions to convert the traditional industries to green industries where the industries are not only for making profit but also committed to provide good working environment for employee, eco-friendly and ethical business practice

Development of a camera-vision guided automatic sprayer

This study describes the design and development of a camera-vision guided unmanned mover sprayer for the purpose of automatic weed control. The sprayer system was mounted on the mover. Modifications were carried out for both sprayer and mover systems, so that it can be operated remotely. The automated system was developed using the electromechanical system and controllers. It is capable of directing the mover sprayer to the target location given by the user. The electromechanical system was developed to control the ignition, the accelerator and the spraying systems. The controllers consist of an I/O module (ICPCON I-87057) and also a pair of radio modems (SST-2400) for data transmission. The graphical user interface (GUI) software to control the automatic system was developed by using Visual Basic Programming. The GUI has features which enable the user to perform desired tasks using the computer instead of going directly to the sprayer/mover. The combination of the multi controllers and developed control software in the development of the camera-vision-guided unmanned mover sprayer can reduce drudgery and increase safety

粟とエンドウのポリアミン代謝

博士(理学)大阪市立大