Sirindhorn International Institute of Technology

About: Sirindhorn International Institute of Technology is a based out in . It is known for research contribution in the topics: Supply chain & Combustion. The organization has 1048 authors who have published 1678 publications receiving 30067 citations.

Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV).

Abstract: The production of banana—one of the highly consumed fruits—is highly affected due to loss of certain number of banana plants in an early phase of vegetation. This affects the ability of farmers to forecast and estimate the production of banana. In this paper, we propose a deep learning (DL) based method to precisely detect and count banana plants on a farm exclusive of other plants, using high resolution RGB aerial images collected from Unmanned Aerial Vehicle (UAV). An attempt to detect the plants on the normal RGB images resulted less than 78.8% recall for our sample images of a commercial banana farm in Thailand. To improve this result, we use three image processing methods—Linear Contrast Stretch, Synthetic Color Transform and Triangular Greenness Index—to enhance the vegetative properties of orthomosaic, generating multiple variants of orthomosaic. Then we separately train a parameter-optimized Convolutional Neural Network (CNN) on manually interpreted banana plant samples seen on each image variants, to produce multiple results of detection on our region of interest. 96.4%, 85.1% and 75.8% of plants were correctly detected on three of our dataset collected from multiple altitude of 40, 50 and 60 meters, of same farm. Further discussion on results obtained from combination of multiple altitude variants are also discussed later in the research, in an attempt to find better altitude combination for data collection from UAV for the detection of banana plants. The results showed that merging the detection results of 40 and 50 meter dataset could detect the plants missed by each other, increasing recall upto 99%.

Verification and modification of breaker height formulas

Abstract: This study is undertaken to find out the most reliable breaker height formulas that predict well for a wide range of hydraulic conditions. The applicability of 24 existing formulas, for computing b...

Sustainable energy development strategies: implications of energy demand management and renewable energy in Thailand

Abstract: A brief review of energy use patterns in three economic sectors; namely, residential, industrial and transport sectors is provided in this paper. The transport sector is the largest energy-consuming sector in Thailand, followed by the industrial and residential sectors, respectively. In order to reduce both imported energy and environmental emissions, energy conservation programs would be implemented. This paper forecasts the growth in energy demand and corresponding emissions to the year 2020 for those three sectors by using a model based on the end-use approach. The energy savings from the energy conservation strategies, such as energy efficiency improvement and energy demand management, are assessed and also the implications on electricity generation expansion planning are examined. The integrated resource planning (IRP) model is used to find the least-cost electricity generation expansion plans. Energy conservation options, including energy efficiency improvement programs, are introduced in the residential and industrial sectors. Public transportation and engine technology improvements are introduced in the transport sector. The effects of energy conservation options are analyzed using a scenario-based approach. The results of analysis reveal that the improvement of public transportation can reduce future energy requirements and CO2 emissions in 2020 by 635 thousand ton of oil equivalent (toe) and 2024 thousand ton of CO2 equivalent, respectively. If all options are simultaneously implemented, the potential of energy savings and CO2 mitigation in 2020 are estimated to be 1240 thousand toe and 3622 thousand ton of CO2 equivalent, respectively.