Optimal 3D UAV Base Station Placement by Considering Autonomous Coverage Hole Detection, Wireless Backhaul and User Demand

Shahriar Abdullah Al-Ahmed,Muhammad Zeeshan Shakir,Syed Ali Raza Zaidi 한국통신학회 2020 Journal of communications and networks Vol.22 No.6

The rising number of technological advanced devicesmaking network coverage planning very challenging tasks for network operators. The transmission quality between the transmitterand the end users has to be optimum for the best performance outof any device. Besides, the presence of coverage hole is also an ongoing issue for operators which cannot be ignored throughout thewhole operational stage. Any coverage hole in network operators’coverage region will hamper the communication applications anddegrade the reputation of the operator’s services. Presently, thereare techniques to detect coverage holes such as drive test or minimization of drive test. However, these approaches have many limitations. The extreme costs, outdated information about the radioenvironment and high time consumption do not allow to meet therequirement competently. To overcome these problems, we take advantage of Unmanned aerial vehicle (UAV) and Q-learning to autonomously detect coverage hole in a given area and then deployUAV based base station (UAV-BS) by considering wireless backhaul with the core network and users demand. This machine learning mechanism will help the UAV to eliminate human-in-the-loop(HiTL) model. Later, we formulate an optimisation problem for3D UAV-BS placement at various angular positions to maximisethe number of users associated with the UAV-BS. In summary, wehave illustrated a cost-effective as well as time saving approach ofdetecting coverage hole and providing on-demand coverage in thisarticle.

Design of an ICT-based monitoring system of soil water content and irrigation control operation for orchards

( Shahriar Ahmed ),( Nasim Reza ),( Rejaul Karim ),( Kayoung Lee ),( Heetae Kim ),( Sun-ok Chung ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.1

Insufficient or excessive water can impede root growth and lead to the leaching of minerals from the root zone, resulting in a nutritional deficiency. In addition, excessive water supply encourages crown and collar rot. Irrigation scheduling can be regulated based on soil water stress. A malfunctioning irrigation scheduling system can also compromise environmental control and inhibit crop growth, so it is crucial that components used in the system work accurately. The goal of this project was to create an automatic irrigation control system, based on real-time soil water content monitoring in orchard soil, and could be accessible remotely via the internet, while also detecting control failures. Experiments were conducted inside a greenhouse, in a 9-m2 soil bin. A Python program was coded to operate the irrigation pump and solenoid valves through a micro-controller utilizing water content information from sensors, as well as to transfer sensor data to a database system to observe the operation through the internet. The experiment showed that all sensor data could be collected and sent to the database system. The average water content values for the two channels were 18.3% and 16.6% during the irrigation period and 24.6% and 27.1% during the non-irrigation period, respectively. During the control operation, it was found that the first solenoid valve consumed 20.2 watts in average for “ON” state and 11.12 watts for “OFF” state. However, there was no significant change in power consumption of the second solenoid valve between the “ON” and “OFF” states. Therefore, the actuator used in the system showed a faulty condition, which can be corrected by further investigation.

Design of an ICT-based Irrigation Control System for Orchard Soil Water Content Monitoring

아흐메드샤리아르 ( Shahriar Ahmed ),알리모하마드 ( Mohammad Ali ),하비네자엘리에젤 ( Eliezel Habineza ),정선옥 ( Sun-ok Chung ) 한국농업기계학회 2022 한국농업기계학회 학술발표논문집 Vol.27 No.2

Deficient or excessive irrigation inhibits root growth and increases the risk of essential minerals being leached from the root zone, causing a nutritional deficiency. Irrigation scheduling can be managed based on soil water stress. The purpose of this study was to design an automatic control system for irrigation based on real-time soil water content monitoring in orchard soil which was capable of being accessed remotely via the internet. A test bench was fabricated in a soil bin. Soil water content sensors were installed in separate channels across the test bin to maintain the average water content for different circumstances. A python-based program was created to control the irrigation pump and solenoid valves using water content values from sensors via a micro-controller. Additionally, a python program was developed to transfer all the data from water content, water flow and pressure sensors to a database system to monitor the irrigation operation using a WI-FI network. The system was able to sense analog values from water content sensors, water flow and pressure sensors. The water content values after reaching 25% and 30%, the system sent signals to the irrigation pump and solenoid valves and terminated the irrigation process accordingly. Besides the system sent all the sensors values to the database system every 1-minute interval. With the proposed low-cost real-time monitoring system, it could help to increase production efficiency, use less labor, reduce water loss, and optimize water consumption.

Development of SNP Markers to Distinguish Various Watermelon Traits and Validation Using Fluidigm Genotyping Assay

Sang-Min Yeo(Sang-Min Yeo),Jeong-Eui Hong(Jeong-Eui Hong),Md Abdur Rahim(Md Abdur Rahim ),Saleh Ahmed Shahriar(Saleh Ahmed Shahriar ),Phillip Choe(Phillip Choe),Sun-Kyun Jung(Sun-Kyun Jung),Ill-Sup No 한국육종학회 2023 Plant Breeding and Biotechnology Vol.11 No.2

Watermelon [Citrullus lanatus (Thunb.) Matsum and Nakai] is one of the economically most important fruit crops of the Cucurbitaceae family. Among different watermelon traits, disease resistance and fruit quality are the important traits for growers and consumers. The single nucleotide polymorphism (SNP) markers similar to those traits can potentially and cost-effectively distinguish the genetic variations among these traits. Consequently, we developed 33 SNP makers linked to different watermelon traits associated with fruit quality and disease resistance, and validated in the genetic resources of watermelon and F1 breeding lines using ‘Fluidigm SNP Genotyping’ assay. Most of the SNP markers distinguished the alleles into three different types such as reference allele, alternative allele and heterozygous from watermelon genotypes for various traits. The SNP markers ‘ZymFL-T81P’ (ZYMVresistance), ‘FON1-U161’ and ‘FON1-S075’ (Fusarium wilt-resistance), ‘Pmr21-Cla831’ (PM-resistance), and ‘ClGBS-J168’ and ‘GBS-GC230’ (GSB-resistance) can successfully differentiate resistant (R), susceptible (S) and heterozygous watermelon genotypes. Similarly, the SNP marker associated with sugar content, citrulline content, arginine content, rind hardness, flesh firmness, fruit shape, rind strip pattern of watermelon fruit and seed coat colour can successfully distinguished the watermelon genetic resources and F1 breeding lines as reference allele (A) type, alternative allele (B) type and heterozygous (H). These SNP markers could be utilized for marker assisted selection as well as screening of a large number of watermelon germplasm for fruit quality and disease resistance. However, further validation like artificial inoculation of pathogens for the traits related to disease resistance is required in watermelon crops.

LoRa-based video data transmission for real-time monitoring of pig farm

( Nasim Reza ),( Shahriar Ahmed ),( Sumaiya Islam ),( Shaha Nur Kabir ),( Minho Song ),( Gookhwan Kim ),( Sun-ok Chung ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.1

This paper proposed a LoRa-based video data transmission system for real-time monitoring in a pig farm. This approach eliminates the need for complex and costly infrastructure, making it a cost-effective solution for real-time monitoring in pig farm. The system architecture included the Raspberry Pi 4B microcontroller, RGB cameras, LoRa transceivers, gateway, and cloud-based platform for data analysis and visualization. The video data was captured using the RGB cameras and stored into an external memory through the microcontroller. Then the video was segmented into small chunks and compressed as an H.265 codec, which reduced the size of the video data and made it easier to transmit using the LoRa. Each compressed video chunk was then sent by the LoRa transceiver with a low data rate and a low transmit power. This allows the transmission to reach long distances, while consuming very low power levels. At the receiving end, the video chunks were received by another LoRa transceiver and re-assembled into the original video stream. The system performance was evaluated through a series of tests, including transmission range, video quality, and power consumption. The results showed that the LoRa-based system could transmit video data over a long range (2 km) with low power consumption (less than 1 W), while maintaining good video quality (720p resolution). The findings showed a great potential for real-time monitoring in pig farms, providing valuable insights into the pigs behavior, health, and productivity.

Assessment of Precision and Accuracy of Cropcircle and MicaSense Sensors for Monitoring Crop Growth Dynamics During NDVI Calculation

( Asrakul Haque ),( Rejaul Karim ),( Shahriar Ahmed ),( Nasim Reza ),( Ka Young Lee ),( Yeong Ho Kang ),( Keong Do Lee ),( Sun-ok Chung ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.1

The Normalized Difference Vegetation Index (NDVI) is a widely used index for monitoring crop growth and diagnosing nitrogen status. This study aimed to evaluate the precision and accuracy of Cropcircle and MicaSense sensors for monitoring crop growth dynamics using NDVI. The assessment was conducted under varying vegetative conditions and growth stages of the crops. In this experiment, a GPS unit and a combination of an active (Cropcircle ACS-435) and a passive sensor (MicaSense RedEdge MX) mounted on a frame were used. The data was taken at a height of 1m to cover the canopy area of wheat crop. Two plots with low and high vegetation levels were examined during Feekes 1 and Feekes 2-3. The coefficients of determination (R2) between Cropcircle and MicaSense sensors for low and high vegetation plots were 1E-6 and 0.29, respectively, for Feekes 1 and increased to 0.43 and 0.73, respectively, in Feekes 2-3. For Feekes 1, the average mean for the crop-circle and MicaSense NDVI values were 0.13 ± 0.02 and 0.18 ± 0.02, respectively, for low vegetation and 0.23 ± 0.08 and 0.25 ± 0.04, respectively, for the high vegetation plots. For Feekes 2-3, the mean for the crop-circle and MicaSense NDVI values were 0.13 ± 0.04 and 0.19 ± 0.03, respectively, for low vegetation and 0.23 ± 0.01 and 0.27 ± 0.07, respectively, for the high vegetation plots. The results showed that the high vegetative plot had a significant impact on NDVI calculation using crop canopy sensors than the low vegetative plot. Therefore, it is suggested to consider vegetative state of crops for accurate and precise monitoring of crop growth using crop canopy sensors.

Short-range sensing for fruit tree water stress detection and monitoring in orchards: a review

Sumaiya Islam,Nasim Reza,Shahriar Ahmed,Shaha Nur Kabir,정선옥,김희태 충남대학교 농업과학연구소 2023 Korean Journal of Agricultural Science Vol.50 No.4

Water is critical to the health and productivity of fruit trees. Efficient monitoring of water stress is essential for optimizing irrigation practices and ensuring sustainable fruit production. Shortrange sensing can be reliable, rapid, inexpensive, and used for applications based on welldeveloped and validated algorithms. This paper reviews the recent advancement in fruit tree water stress detection via short-range sensing, which can be used for irrigation scheduling in orchards. Thermal imagery, near-infrared, and shortwave infrared methods are widely used for crop water stress detection. This review also presents research demonstrating the efficacy of short-range sensing in detecting water stress indicators in different fruit tree species. These indicators include changes in leaf temperature, stomatal conductance, chlorophyll content, and canopy reflectance. Short-range sensing enables precision irrigation strategies by utilizing real-time data to customize water applications for individual fruit trees or specific orchard areas. This approach leads to benefits, such as water conservation, optimized resource utilization, and improved fruit quality and yield. Short-range sensing shows great promise for potentially changing water stress monitoring in fruit trees. It could become a useful tool for effective fruit tree water stress management through continued research and development.

Nutrient deficiency detection in early growth stage of tomato seedlings using feature extraction from digital imagery

잇림수마이아 ( Sumaiya Islam ),아흐메드샤리아르 ( Shahriar Ahmed ),하케아스라쿨 ( Asrakul Haque ),조연진 ( Yeon Jin Cho ),노동희 ( Dong-hee Noh ),정선옥 ( Sun-ok Chung ) 한국농업기계학회 2022 한국농업기계학회 학술발표논문집 Vol.27 No.2

Detection and management of nutritional stress in tomato seedlings is the key to growing high-yield, high-quality tomatoes. Canopy level image based plant stress monitoring may limit the stressed condition in plants. Prior to visual stress detection by human eyes, the primary goal in this study was to identify nutrient stress in tomato seedlings using image based plant feature extraction. Tomato seedlings were grown under three different levels of electrical conductivity (EC) of 0.0, 3.0, and 6.0 dS/m, with the optimum growth conditions. Images were captured of tomato seedlings and the top projected canopy area (TPCA) was calculated from the white pixels of the image, extracted from the image background. Morphological and textural parameters were collected, including homogeneity, energy, entropy, and contrast. A statistical study based on dual-segmented regression analysis was carried out to find out the stressed condition. With a confidence interval of 97.0% and a coefficient of determination (R2) of 96.7%, day 4.2 was predicted as the change point for the parameters. The method identified nutritional stress on tomato seedlings one day earlier than ocular detection. Color and texture features need further investigation to detect typical stress symptoms.

Estimation of apple tree canopy height and area coverage using 3D LiDAR point clouds

( Rejaul Karim ),( Mohammod Ali ),( Shahriar Ahmed ),( Ashrafuzzaman Gulandaz ),( Nasim Reza ),( Justin Sung ),( Sun-ok Chung ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.1

Phenotyping characteristics (Tree height and canopy area) are crucial factors for growth and yield monitoring. This study aimed to use 3D LiDAR point clouds to estimate the apple tree canopy height and area using different digital models. The traditional method of manually measuring trees at various heights and canopies is time-consuming and labor-intensive, so the study aimed to automate the process using 3D LiDAR (VLP-16) to collect point clouds of orchard apple trees. The data was pre-processed using a 3D point cloud data processing software, and automatic segmentation methods were applied to calculate the canopy height and area for selected orchard tree samples. The processed 3D point cloud data was converted into raster images for visualization and estimation of orchard tree canopy height and area coverage using digital surface model (DSM), digital elevation model (DEM), and canopy height model (CHM). Python program was also used for visualization and reconstruction of trees from the preprocessed data. The accuracy of the sensor-based measuring method was compared to manually-acquired ground truth data, but the accuracy was worse by 15%. The study found that the proposed system could efficiently segment and measure tree canopy height and area coverage. The proposed models showed comparatively lower result than manual measurement, with an sensor based average tree canopy height and area of 2.1 m and 5.83 m², respectively, where as measured values were 2.4±0.2 m and 6.0±0.21 m², respectively. However, the findings of this study can still contribute to further horticultural crops research particularly for orchard fruits production and yield monitoring.

3D LiDAR-based Quantification of Phenotypic Traits and Land Characteristics in Rice Farming

( Md Rejaul Karim ),( Mohammod Ali ),( Shahriar Ahmed ),( Md Shaha Nur Kabir ),( Md Nasim Reza ),( Justin Sung ),( Sun-ok Chung ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.2

Phenotypic and land characteristics information plays a crucial role in effective management of rice farming. The utilization of LiDAR based object recognition as well as visualization provides a rapid and precise assessment of the phenotypic traits of rice plants. This study aimed to quantify the rice plant phenotypic and land characteristics using a 3D LiDAR. A data collection structure made of aluminum profile and a LiDAR sensor (i.e., VLP-16) mounted on the structure was used to collect 3D point cloud data from rice field. A rice field of RDA at Iksan in Korea was selected for data acquisition. Ten numbers of small plots considering the area of LiDAR data frames exhibiting diverse plant height, shapes, and sizes were randomly selected. From each LiDAR scanned data frame, a region of interest (RoI) segmented for sensor based processing and measurements. Commercial software utilized for segmentation and python-based programing codes also applied to process the collected data for visualization and measurements. The accuracy of the estimated outputs from the point cloud was evaluated by comparison with measured values collected randomly from ten spots remaining in the sensor-based data frame. The estimated plant heights from the point cloud were 0.84±0.03 m, while the measured heights were 0.77±0.03 m. The root mean square error (RMSE) for plant height estimation was 0.08 m, and the simple linear coefficient of determination (r²) was 0.88. Regarding the segment wise canopy volume, point cloud estimations were 1.01±0.06 m³, compared to the measured volume of 1.18±0.03 m³. The RMSE for canopy volume estimation was 0.18 m³, with r² of 0.87, indicating a high level of accuracy. For hill-to-hill distance and intra-row spacing, the point cloud measurements were 0.35±0.01 m, and 0.34±0.02 m, respectively, while the measurements were 0.30±0.03 m, and 0.30±0.03 m, respectively. The RMSE and r² for hill to hill distance were 0.04 m and 0.92, respectively, and for row distance, 0.03 m and 0.87, respectively. Despite minor differences, there was a strong relationship and close agreement between the estimation using point cloud data and measurements. The findings highlight the reliability and efficiency of the 3D LiDAR technology for accurately measuring phenotypic traits and land characteristics for maximizing rice cultivation.