Theoretical Working Speed Analysis of a 1.54 kW One-row Biodegradable Potted Seedling Transplanting Mechanism

( Md Razob Ali ),( Samsuzzaman ),( Eliezel Habineza ),( Md Shaha Nur Kabir ),( Mohammod Ali ),( Beom-seon Kang ),( Sun-ok Chung ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.2

Plastic seedling pots have been widely used due to their light weight and durable nature, but they hinder root establishment efficiency. However, recent studies have demonstrated that biodegradable potted seedlings could improve seedling resilience, while also being eco-friendly through natural decomposition. In this research, a transplanting mechanism for biodegradable potted vegetable seedlings was designed using commercial software, incorporating working speed analysis to enhance smooth collection and plantation of leafy vegetable seedlings. Theoretical analysis of the vegetable transplanting mechanism for biodegradable seedling pots was conducted, including calculations of position, velocity, acceleration, and input driving torque. Additionally, the selection of appropriate link combinations within the mechanism was explored to ensure smooth transplantation of potted seedlings in optimum depths and spacings. The kinematic model of the transplanting mechanism was simulated using commercial mechanical design and simulation software. The transplanter was comprised of a 4-bar mechanism: a driving link, a driven link, a connecting link, and a supporting bar. In order to enable better hopper motion, a spring was affixed between the driven link and the ground. The movement of the mechanism was primarily controlled through a crank-rocker mechanism, where the arm lengths play a crucial role in determining the planting trajectory. The mathematical model analysis and simulation revealed that a forward speed of the transplanter of 300 mm/s and a rotating speed of the dibbling mechanism of 40 rpm were favored of 48 seedlings/min in order to obtain a high degree of seedling uprightness. The simulated velocities and accelerations of the end hopper in ‘X’ and ‘Y’ directions for suitable link combination were found to be 430mm/s,530mm/s,and 975 mm/s2,2091mm/s2,respectively.The required driving torque was observed to be 603N-mm,and the vertical linear displacement of the hopper was 281mm.

Evaluating the Accuracy of FOV Alignment for Micasense Multispectral Imagery in VI Calculation

( Md Asrakul Haque ),( Md Rejaul Karim ),( Md Razob Ali ),( Shaha Nur Kabir ),( Keong Do Lee ),( Yeong Ho Kang ),( Sun-ok Chung ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.2

Multispectral imagery is pivotal for vegetation index (VI) analysis, shaping crop nutritional management strategies and advancing precision agriculture. Yet, the efficacy of image enhancement techniques in VI calculation remains a critical inquiry. This study addresses this gap by evaluating various image enhancement methods for multispectral imagery, focusing on the widely accepted Normalized Differential Vegetation Index (NDVI). We employed a multispectral sensor, the MicaSense RedEdge MX, alongside an active sensor, the Crop-circle ACS-435, to assess NDVI calculation performance. Our objective was to assess the accuracy of the Field of View (FOV) alignment of MicaSense with the active sensor. Data collection occurred across four distinct wheat growth stages (GS1, GS2, GS3, and GS4) utilizing a handheld structure equipped with Crop Circle ACS 435, MicaSense RedEdge MX, and a Topcon Hiper VR GNSS rover. This setup maintained a consistent 90cm canopy height based on the plot width. Python programming facilitated GPS location processing and image segmentation based on pixel coordinates, mirroring the Crop-circle FOV. We extracted reflectance data from the segmented portion of each band and calculated NDVI using Red and NIR reflectance data. Data enhancement techniques were assessed by comparing enhanced and raw image data against standardized data from the Crop-circle sensor. Regression analysis, including the coefficient of determination (R2) and root mean square error (RMSE), was utilized for evaluation. The application of the FOV enhancement technique to MicaSense images yielded significant improvements in regression metrics (R2 and RMSE) across GS1, GS2, GS3, and GS4. Notably, FOV enhancement resulted in R2 increases of 50%, 18%, 16%, and 4% and RMSE values of0.06, 0.05, 0.06, and 0.03, respectively. The most substantial accuracy enhancements were observed in GS1 (50%), indicating varying effectiveness based on vegetation growth stage and density. This study underscores the critical role of multispectral imagery and the efficacy of FOV alignment in improving NDVI calculation accuracy. These findings hold valuable implications for future research and precision agriculture practices.

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.

Thermal imaging and computer vision technologies for the enhancement of pig husbandry: a review

Md Nasim Reza,Md Razob Ali,Samsuzzaman,Md Shaha Nur Kabir,Md Rejaul Karim,Shahriar Ahmed,Hyunjin Kyoung,김국환,Sun-Ok Chung 한국축산학회 2024 한국축산학회지 Vol.66 No.1

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

Development of a sandy soil water content monitoring system for greenhouses using Internet of Things

Mohammod Ali,Md Razob Ali,Md Ashrafuzzaman Gulandaz,Md Asrakul Haque,Md Sazzadul Kabir,Sun-Ok Chung 사단법인 한국정밀농업학회 2023 정밀농업과학기술지 Vol.5 No.3

Precision water management is crucial for greenhouse agriculture to maximize crop yields in sandy soil. Due to the low water holding capacity, it is necessary to monitor the water movement in different depths of sandy soil to ensure effective irrigation. Therefore, this study aimed to develop a data acquisition (DAQ) system for sandy soil water content monitoring in an experimental soil bin inside a greenhouse, utilizing the capabilities of the Internet of Things (IoT). A drip irrigation system was implemented, arranged in four pipelines, spaced 60 cm apart, with drippers placed at 30 cm intervals along the pipeline. The overall system was installed in a sandy soil testing bin. A DAQ system was comprised of three basic units: sensor interfacing and circuit board, programming and sensor data acquisition, and data storage and monitoring. A microprocessor was used by interfacing a set of soil water content sensors, ambient temperature, and humidity sensors. The water content sensors were placed in the soil at different depths of 10, 20, 30, 40, and 50 cm, respectively. A microcontroller was used to collect and send the sensor data to monitor and store in memory. During the test, the maximum and minimum average of soil water content, ambient temperature, and humidity values were observed at 33.91±2.5 to 26.95±1.3%, 21.39±2.1 to 42.84±1.7°C, and 48.73±2.3 to 99.90±0.3%, respectively. The water content percentages were varied at different depths of sandy soil due to low water holding capacity. The developed automatic DAQ system would help with remote monitoring and control of greenhouse irrigation, considering the different crop characteristics and environmental conditions.

An loT-Based Drip Irrigation System for Precise Water Management in Sandy Soil Greenhouses

( Mohammad Ali ),( Bicamumakuba Emmanuel ),( Md Ashrafuzzaman Gulandaz ),( Md Razob Ali ),( Sun-ok Chung ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.2

To enhance crop yields in greenhouse agriculture, precise water management is important, especially in sandy soil where water retention is challenging. Therefore, the study aimed to develop a data acquisition (DAQ) system to monitor the water content of sandy soil using the Internet of Things (IoT) in an experimental soil bin inside a greenhouse. The system used drip irrigation with four pipelines spaced 60 cm apart, each having drippers at 30 cm intervals along the pipeline. The whole setup was placed inside a sandy soil testing bin (3 × 3 m). The design of the DAQ system revolved around three essential components: sensor interface and electronics, programming for sensor data collection, and data storage and monitoring. The device utilized a microprocessor to establish communication with a set of sensors responsible for measuring soil water content, ambient temperature, and humidity. The soil water content sensors were placed in the soil at depths ranging from 0 to 50 cm. A microcontroller gathered and transmitted the sensor data for real-time monitoring and storage. The system indicated variations in soil water content during the experiment, with maximum and minimum average values ranging from 33.91±2.5 to 26.95±1.3%. Ambient temperature and humidity levels varied similarly, ranging from 21.39±2.1 to 42.84±1.7 ℃ and 48.73±2.3 to 99.90±0.3%, respectively. The developed DAQ system offers potential for greenhouse irrigation management, incorporating remote monitoring and control capabilities, considering various crop traits and climatic conditions.

Technology development and industrialization trends of circulating nutrient solution supply systems: a review

Hyo-Jeong Kwon,Md Razob Ali,Ka Young Lee,Md Nasim Reza,Mohammod Ali,Md. Shaha Nur Kabir,Sun-Ok Chung,Kanghee Jeong 사단법인 한국정밀농업학회 2023 정밀농업과학기술지 Vol.5 No.3

Hydroponics, a soil-free plant cultivation technique, delivers nutrients directly to roots through a nutrient-rich solution. This method offers advantages over traditional soil-based approaches and has gained attention for its potential to revolutionize controlled agriculture. The review aimed to offer a summary of technological advancements and industrialization patterns in systems supplying circulating nutrient solutions. An intelligent nutrient management system enhances plant growth and productivity. The utilization of a circulating hydroponic cultivation setup can reduce environmental pollution and lower production expenses. As a result, circulating nutrient solution management systems are gaining global popularity, such as in the Netherlands, circular hydroponic cultivation has been advanced to over 95%. A limitation of circulation-type nutrient solution cultivation is the potential transmission and rapid formulation of pathogens during the recycling of discharged nutrient solutions. Addressing this concern, filtration and sterilization processes can offer viable solutions. To accelerate hydroponic farming, an integrated approach could be pursued, strengthen nutrient circulation management technology. This approach could aid in the implementation of such systems in countries like the Republic of Korea, where adoption of circulating hydroponic systems remains under 5%. The trend of technological advancement and industrial growth has been conducted through patent analysis and resources that have subsequently lead the way for the advancement of extensive hydroponic farming establishments. The cyclic hydroponic cultivation in the Republic of Korea was introduced in 2010, and based on the patent information, this endeavor gained momentum from 2020 onward. Furthermore, the analysis underscores the considerable potential of circulating nutrient solution supply systems as viable approaches for promoting sustainable and efficient food production. As a result, forthcoming research and innovation need to be tailored to the local context and prioritize user-centered methodologies, ultimately facilitating the integration and establishment of hydroponic systems in the Republic of Korea.

Theoretical analysis of a wheel-driven dibbling mechanism for a single-row automatic pepper transplanter

( Eliezel Habineza ),( Md Nasim Reza ),( Shahriar Ahmed ),( Md Razob Ali ),( Emmanuel Bicamumakuba ),( Seok-ho Park ),( Sun-ok Chung ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.2

Mechanized pepper seedlings transplanting can increase pepper yield and the dibbling mechanism is a vital components of the transplanter that influences the efficiency. This study aimed at theoretical analysis of a wheel-driven dibbling mechanism through 3D modeling to optimize seedling planting speed and intervals based on agronomic requirements. Planting trajectory dimension was used to maintain inter-row distance and planting interval which were depending on wheel diameter and rotational speed. The wheel-driven dibbling mechanism comprised five key components : wheel, wheel support, roller, roller-guide , and transplanting hopper. Five static simulation trials determined the wheel diameters and planting trajectory dimensions , while five dynamic simulation trials selected the optimal operating speed and planting spacing based on forward speed velocity and acceleration. The optimal wheel diameter and planting trajectory dimension were 250 mm at 10 rpm. The optimal forward speed was 0.15 m/s for 39 seedlings/min. With a planting spacing of 150 mm, the velocity and acceleration along the x- and y-axis were 0.131 m/s, and 0.137 m/s², respectively. The findings can enhance wheel-driven dibbling mechanism design and would need validation through field tests.

Deep learning based identification of Pepper (Capsicum annuum L.) diseases: A review

Chan Ho Kim,Samsuzzaman,Md Nasim Reza,Ka Young Lee,Md Razob Ali,Sun-Ok Chung,Md. Shaha Nur Kabir 사단법인 한국정밀농업학회 2023 정밀농업과학기술지 Vol.5 No.2

Recent advancements in plant disease identification have leveraged image processing and deep learning techniques for automated detection. Visual deep learning systems are employed to identify diseases accurately in the agricultural sector. This study focuses on reviewing the use of image processing and deep learning approaches in the accurate identification of pepper (Capsicum annuum L.) plant diseases. In most cases, it is quite difficult to classify the infected bacterial spots on pepper plants that affect productivity and quality, leading to substantial economic losses in the agricultural industry. To manage the issues, image processing and deep learning techniques have been applied to diagnose bacterial spots in pepper plants from the symptoms found on the leaves. Various methodologies for data augmentation and deep learning methods of embedding, multitask learning, transfer learning, and meta-learning are also discussed. It summarized how models are optimized for performance with reference to existing studies and potential challenges for AI applications in plant disease recognition. Finally, the review concludes with key findings and future directions and highlights the immense potential of deep learning as a valuable tool for accurate and automated identification and practical applications in pepper disease management.

PID Control for Greenhouse Climate Regulation: A Review

Tae Ho Kim,Ka Young Lee,Md Razob Ali,Md Nasim Reza,Sun-Ok Chung,강나래 사단법인 한국정밀농업학회 2023 정밀농업과학기술지 Vol.5 No.2

The increasing demand for sustainable agricultural practices has spurred a significant interest in optimizing greenhouse climate regulation to enhance crop yield, quality, and energy efficiency. Proportional-Integral-Derivative (PID) control, a widely used feedback control technique, has emerged as a promising solution for maintaining optimal greenhouse conditions. This paper presents a comprehensive review of the application of PID control in greenhouse climate regulation. A detailed analysis of the PID control algorithm's components, namely Proportional, Integral, and Derivative terms, highlights their respective roles in achieving precise regulation of temperature, humidity, CO2 levels, and ventilation. Moreover, the review explores the challenges associated with parameter tuning and controller stability in greenhouse settings. The review discusses sensor technologies, and communication protocols, which enable real-time data feedback and enhance the performance of PID controllers. Furthermore, the review presents a comprehensive analysis of recent studies and field applications showcasing the effectiveness of PID control in greenhouse environments. Comparative studies with other control strategies are also discussed, highlighting the advantages and limitations of PID control in addressing varying climate conditions and cropspecific requirements. Additionally, recent advancements in machine learning-based PID tuning techniques are explored, offering promising alternatives for optimizing greenhouse climate management. The review shows that PID control is a promising approach for greenhouse climate regulation. However, there are still some challenges that need to be addressed, such as the uncertainty of the greenhouse climate model and the non-linearity of the greenhouse system. Future research should focus on addressing these challenges and developing more advanced PID control techniques for greenhouse applications.