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.

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.

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.

A review on stereo vision for feature characterization of upland crops and orchard fruit trees

Md Rejaul Karim,Shahriar Ahmed,Md Nasim Reza,이규호,HongBin Jin,ALI MOHAMMOD,정선옥,Joonjea Sung 사단법인 한국정밀농업학회 2024 정밀농업과학기술지 Vol.6 No.2

Characterization of plant features is essential for effective management of plant growth monitoring, and precise management practices in crop production. Stereo vision captures multiple perspective images to create a threedimensional (3D) representation, enabling thorough analysis of crop structure and morphology. The objective of this study was to review the application of stereo vision in feature characterization of plants and fruit trees. Various features of plants such as height, canopy volume, plant spacing, intra-row spacing, and leaf area were surveyed for their characterization potential along with several data acquisition and data processing algorithms consisting of image segmentation, 3D image reconstruction, depth mapping, and disparity mapping. The study found out some results regarding the feature characterization of plants and fruit trees using stereovision. The tree canopy estimation results showed 6-7% error for elliptical and 2-3% error for conical shaped trees as well as for corn plants detection with an accuracy of 96.7% under natural light conditions. From a maximum distance of 5 cm and 1 cm, the errors were observed with the detection accuracy of 74.6% and 62.3%, respectively. The plant height of cabbage, potato, sesame, radish, and soybean were estimated with a R2 value of 0.78 to 0.84 and with an error less than 5%. Stereo vision achieved 97% precision with an RMSE of 0.016 m in wheat height measurement and distance of 20 m with errors below 5% for hazelnut trees. By addressing challenges and exploring various techniques, the paper concluded by summarizing key findings and suggesting directions for further research in plant growth and crop production practices.

Effects of Environmental Conditions on Vegetation Indices from Multispectral Images: A Review

Md Asrakul Haque,Md Nasim Reza,Mohammod Ali,Md Rejaul Karim,Shahriar Ahmed,Kyung-Do Lee,Young Ho Khang,Sun-Ok Chung Korean Society of Remote Sensing 2024 대한원격탐사학회지 Vol.40 No.4

The utilization of multispectral imaging systems (MIS) in remote sensing has become crucial for large-scale agricultural operations, particularly for diagnosing plant health, monitoring crop growth, and estimating plant phenotypic traits through vegetation indices (VIs). However, environmental factors can significantly affect the accuracy of multispectral reflectance data, leading to potential errors in VIs and crop status assessments. This paper reviewed the complex interactions between environmental conditions and multispectral sensors emphasizing the importance of accounting for these factors to enhance the reliability of reflectance data in agricultural applications.An overview of the fundamentals of multispectral sensors and the operational principles behind vegetation index (VI) computation was reviewed. The review highlights the impact of environmental conditions, particularly solar zenith angle (SZA), on reflectance data quality. Higher SZA values increase cloud optical thickness and droplet concentration by 40-70%, affecting reflectance in the red (-0.01 to 0.02) and near-infrared (NIR) bands (-0.03 to 0.06), crucial for VI accuracy. An SZA of 45° is optimal for data collection, while atmospheric conditions, such as water vapor and aerosols, greatly influence reflectance data, affecting forest biomass estimates and agricultural assessments. During the COVID-19 lockdown,reduced atmospheric interference improved the accuracy of satellite image reflectance consistency. The NIR/Red edge ratio and water index emerged as the most stable indices, providing consistent measurements across different lighting conditions. Additionally, a simulated environment demonstrated that MIS surface reflectance can vary 10-20% with changes in aerosol optical thickness, 15-30% with water vapor levels, and up to 25% in NIR reflectance due to high wind speeds. Seasonal factors like temperature and humidity can cause up to a 15% change, highlighting the complexity of environmental impacts on remote sensing data. This review indicated the importance of precisely managing environmental factors to maintain the integrity of VIs calculations. Explaining the relationship between environmental variables and multispectral sensors offers valuable insights for optimizing the accuracy and reliability of remote sensing data in various agricultural applications.

Evaluation of gear reduction ratio for a 1.6 kW multi-purpose agricultural electric vehicle platform based on the workload data

ALI MOHAMMOD,Md Rejaul Karim,HABINEZA ELIEZEL,Md Ashrafuzzaman Gulandaz,Md Razob Ali,이현석,정선옥,홍순중 충남대학교 농업과학연구소 2024 Korean Journal of Agricultural Science Vol.51 No.2

Selection of gear reduction ratio is essential for machine design to ensure suitable power and speed during agricultural operations. The goal of the study was to evaluate the gear reduction ratio for a 1.6 kW four-wheel-drive (4WD) multi-purpose agricultural electric vehicle platform using workload data under different off-road conditions. A data acquisition system was fabricated to collect workload (torque) of the vehicle acting on the gear shaft. Field tests were performed under three driving surfaces (asphalt, concrete, and grassland), payload operations (981, 2,942, and 4,903 N), and slope conditions (0 - 4°, 4 - 8°, and 8 - 12°), respectively. Commercial speed reduction gear phases were attached to the input shaft of the vehicle powertrain. The maximum required torque was recorded as 37.5 Nm at a 4,903 N load with 8 - 12° slope levels, and the minimum torque was 12.32 Nm at 0 - 4° slope levels with a 981 Nm load for a 4 km/h speed on asphalt, concrete, and grassland roads. Based on the operating load condition and motor torque and rotational speed (TN) curve, the minimum and maximum gear reduction ratios were chosen as 1 : 50 and 1 : 64, respectively. The selected motor satisfied power requirements by meeting all working torque criteria with the gear reduction ratios. The chosen motor with a gear reduction ratio of 1 : 50 was suitable to fit with the motor T-N curve, and produced the maximum speeds and loads needed for driving and off-road activities. The findings of the study would assist in choosing a suitable gear reduction ratio for electric vehicle multi-purpose field operations.