Sensing Technology for Rapid Detection of Phosphorus in Water: A Review

( Sumaiya Islam ),( Nasim Reza ),( Jin-tae Jeong ),( Kyeong-hwan Lee ) 한국농업기계학회 2016 바이오시스템공학 Vol.41 No.2

Purpose: Phosphorus is an essential element for water quality control. Excessive amounts of phosphorus causes algal bloom in water, which leads to eutrophication and a decline in water quality. It is necessary to maintain the optimum amount of phosphorus present. During the last decades, various studies have been conducted to determine phosphorus content in water. In this study, we present a comprehensive overview of colorimetric, electrochemical, fluorescence, microfluidic, and remote sensing technologies for the measurement of phosphorus in water, along with their working principles and limitations. Results: The colorimetric techniques determine the concentration of phosphorus through the use of colorgenerating reagents. This is specific to a single chemical species and inexpensive to use. The electrochemical techniques operate by using a reaction of the analyte of interest to generate an electrical signal that is proportional to the sample analyte concentration. They show a good linear output, good repeatability, and a high detection capacity. The fluorescence technique is a kind of spectroscopic analysis method. The particles in the sample are excited by irradiation at a specific wavelength, emitting radiation of a different wavelength. It is possible to use this for quantitative and qualitative analysis of the target analyte. The microfluidic techniques incorporate several features to control chemical reactions in a micro device of low sample volume and reagent consumption. They are cheap and rapid methods for the detection of phosphorus in water. The remote sensing technique analyzes the sample for the target analyte using an optical technique, but without direct contact. It can cover a wider area than the other techniques mentioned in this review. Conclusion: It is concluded that the sensing technologies reviewed in this study are promising for rapid detection of phosphorus in water. The measurement range and sensitivity of the sensors have been greatly improved recently.

Machine vision and artificial intelligence for plant growth stress detection and monitoring: A review

ISLAM SUMAIYA,REZA MD NASIM,Samsuzzaman,Ahmed Shahriar,Cho Yeon Jin,노동희,정선옥,홍순중 사단법인 한국정밀농업학회 2024 정밀농업과학기술지 Vol.6 No.1

The agricultural sector faces increasing challenges in ensuring food security and optimizing crop yield, necessitating innovative solutions for early detection and mitigation of plant growth stress. The integration of advanced imaging technologies with artificial intelligence (AI) has emerged as a powerful tool for non-invasive, real-time monitoring of plant health. The objective of this paper was to review the application of machine vision and AI in identifying and classifying plant growth stress, with a focus on stressors, datasets, and the use of intelligent algorithms. The significance of plant growth stress induced by environmental variables, including temperature, light, nutrient deficiencies, and water supply were addressed and the conventional stress detection methodologies, underscores their inherent limitations, and establishes the groundwork for the exploration of state-of-the-art technologies in stress assessment. Various sensor technologies were explored, encompassing traditional RGB cameras, multispectral and hyperspectral sensors, and thermal imaging, each capable of capturing distinct stress signatures. Machine vision, leveraging high-resolution imaging and spectroscopy, offers detailed insights into plant physiological responses. Coupled with AI approaches such as deep learning, neural networks, and pattern recognition, machine vision enables the automated analysis of vast datasets, enhancing the accuracy and speed of stress detection. The recent advancements in image processing techniques tailored for plant stress identification were focused and discussed the role of feature extraction, classification, and predictive modelling in achieving robust results. The potentials of AI in plant stress physiology and its role in overcoming the limitations of traditional methods, and the use of unsupervised identification of visual symptoms to quantify stress severity, allowing for the identification of different types of plant stress were studied. Moreover, the potentials of machine vision technology and AI for real-time monitoring and decision support systems in precision agriculture were discussed. The findings of this review would contribute to the growing field of agricultural technology, offering insights into the development of automated tools that could aid farmers and researchers in mitigating the impact of abiotic stressors on crop/plant health and productivity.

Image based algorithm for growth prediction of pennywort plant grown in a plant factory

이슬람수마이야 ( Sumaiya Islam ),레자나심 ( Nasim Reza ),초두리밀론 ( Milon Chowdhury ),키라가샤피크 ( Shafik Kiraga ),정선옥 ( Sun-ok Chung ) 한국농업기계학회 2021 한국농업기계학회 학술발표논문집 Vol.26 No.2

Plant growth prediction typically relies on the estimation of changes in plant structure and size. The leaf is one of the visual structures of plants, which has a significant impact on growth. The objective of this study was to predict pennywort plant growth using an image processing algorithm. Pennywort plant was grown in the plant factory, where ambient environmental variables were maintained precisely. The experiment was carried out for four weeks. RGB images of the plant were captured by a digital camera from the top of the plants everyday. In the image processing algorithm, the images were converted to grayscale and then binary masking was applied to classify each pixel as belonging to the region of interest. The masked images were segmented from the background. Then the region filling technique was applied to fill out the leaf region. We calculated the total pixel number in the image leaf area and calculated the leaf area using reference object. Actual plant leaf area was also continuously measured by a leaf area meter with specific time intervals without hindering plant growth. Our proposed algorithm demonstrated a high correlation of 0.954 between the actual and image-based leaf area measurements. A linear regression curve was found and growth was predicted using the desired cultivation period on the regression equation. Growth prediction model showed the potentiality to estimate plant growth cultivated in controlled environment.

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.

Sensing Technology for Rapid Detection of Phosphorus in Water: A Review

Islam, Sumaiya,Reza, Md Nasim,Jeong, Jin-Tae,Lee, Kyeong-Hwan Korean Society for Agricultural Machinery 2016 바이오시스템공학 Vol.41 No.2

Purpose: Phosphorus is an essential element for water quality control. Excessive amounts of phosphorus causes algal bloom in water, which leads to eutrophication and a decline in water quality. It is necessary to maintain the optimum amount of phosphorus present. During the last decades, various studies have been conducted to determine phosphorus content in water. In this study, we present a comprehensive overview of colorimetric, electrochemical, fluorescence, microfluidic, and remote sensing technologies for the measurement of phosphorus in water, along with their working principles and limitations. Results: The colorimetric techniques determine the concentration of phosphorus through the use of color-generating reagents. This is specific to a single chemical species and inexpensive to use. The electrochemical techniques operate by using a reaction of the analyte of interest to generate an electrical signal that is proportional to the sample analyte concentration. They show a good linear output, good repeatability, and a high detection capacity. The fluorescence technique is a kind of spectroscopic analysis method. The particles in the sample are excited by irradiation at a specific wavelength, emitting radiation of a different wavelength. It is possible to use this for quantitative and qualitative analysis of the target analyte. The microfluidic techniques incorporate several features to control chemical reactions in a micro device of low sample volume and reagent consumption. They are cheap and rapid methods for the detection of phosphorus in water. The remote sensing technique analyzes the sample for the target analyte using an optical technique, but without direct contact. It can cover a wider area than the other techniques mentioned in this review. Conclusion: It is concluded that the sensing technologies reviewed in this study are promising for rapid detection of phosphorus in water. The measurement range and sensitivity of the sensors have been greatly improved recently.

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.

농업 4륜 전기 자동차 설계를 위한 환감 기어의 부하 분석

알리모하마드 ( Mohammod Ali ),이슬람나피울 ( Md. Nafiul Islam ),레자나심 ( Md Nasim Reza ),초두리밀론 ( Milon Chowdhury ),이슬람수마이야 ( Sumaiya Islam ),이현석 ( Hyun-seok Lee ),정선옥 ( Sun-ok Chung ) 한국농업기계학회 2021 한국농업기계학회 학술발표논문집 Vol.26 No.1

The load analysis of gears is a major challenge to ensure the reliability of the power transmission system of the all-wheel-drive electric vehicle. It is necessary to select the proper material and face width for design the reduction gears to avoid its failure during field operations. Therefore, this study aimed to investigate the suitable materials and dimensions, to evaluate the fatigue life regarding the level of damage. A field experiment was conducted on the off-road conditions following the driving speeds. A load (torque) measurement method was established to collect the torque data using torque sensors and data acquisition systems. A load duration distribution (LDD) method was used to analyze the torque data to examine the cyclic load characteristics. The Palmgren-Miner cumulative damage model was used to determine the fatigue damage level of the reduction gears. The hypothetic fatigue life was recorded up to 2500 hours that satisfied the actual service life of the agricultural vehicle. In order to the analyses, the steel material ‘SCr420H’ with a 5 mm face width gear was suitable for a 10-year vehicle service life. The results presented in this study can suggest the service life of a four-wheeled electric vehicle for agricultural use.

AI-Enabled Real-Time Pig Disease Detection and Management

( Md Nasim Reza ),( Sumaiya Islam ),( Md Razob Ali ),( Samsuzzaman ),( Md Shaha Nur Kabir ),( Minho Song ),( Gookhwan Kim ),( Sun-ok Chung ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.2

Surveillance cameras are becoming crucial tools for early livestock disease detection, offering the potential to reduce the negative impact on animal health and the economy in livestock production. This study focused on detecting pig disease symptoms, serving as an initial exploration for practical implementation on pig farms. The aim was to develop an AI-based approach using various video and acoustic sensors in real farm environments. The setup includes two RGB cameras for top and side views, a thermal sensor, and a sound sensor, all controlled by a microcontroller. The collected audio, video, and temperature data are processed in real-time. Using RGB and infrared camera feeds, along with audio analysis, we developed a system to recognize pigs and identify illness states in the video stream. We employed a single-shot multibox (SSD) architecture with MobileNet V2 for video stream processing, achieving an accuracy of 93.6% for pig recognition. The system demonstrated an 89.6% mean average accuracy (mAP) with a frame rate of 21 for disease detection. When tested on sound data, it achieved an average F1-score of 83.7%, with recognition accuracies of 67.5% for snoozing, 74.8% for coughs, 72.9% for crushing sounds, and 82.3% for screaming. Detection accuracy was affected by blurry video and background noises. This research advances precision livestock farming for pig health and disease prevention.

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.

Fabrication and field performance test of a tractor-mounted 6-row cabbage collector

한광민,ALI MOHAMMOD,KHINE MYAT SWE,Sumaiya Islam,정선옥,김대건 충남대학교 농업과학연구소 2021 Korean Journal of Agricultural Science Vol.48 No.1

The cultivation area for domestic cabbage increased by 26.3% from 10,968 ha in 2019 to 13,854 ha in 2020, and among leafy vegetables, the cabbage cultivation area was 62%, and production was 78.9%. Demand for field crop production of cabbage, which has a relatively high-income level compared to rice farming, is increasing, and mechanization of the field operation is urgently needed due to the insufficient development of related farming machinery. In this study, a prototype fabrication and performance test of a tractor-attached cabbage collector was carried out. The transport section was divided into two parts, one for the feeding and transportation and the other for the screening and packaging to selectively collect cabbages in bulk bags or boxes. The length of the primary collecting conveyor was designed to meet the field conditions of the Korean cabbage cultivation standards so that six cultivation rows could be worked simultaneously. Power was controlled by a hydraulic transmission line of the tractor and was easily mounted onto the 3-point hitch links behind the tractor. When the performance was evaluated, the transfer rate, loss rate, damage rate, and work performance were 100, 0, 1.2%, and 1.9 h·10 a-1. Final improvement and commercialization of the prototype would considerably contribute to the mechanization of harvesting cabbage, the main ingredient of Kimchi.