Estimation of CO₂ emissions in a swine barn based on age, body weight gain and different activities of swine

( Nibas Chandra Deb ),( Jayanta Kumar Basak ),( Bhola Paudel ),( Sijan Karki ),( Daeyeong Kang ),( Junghoo Kook ),( Myeongyong Kang ),( Seongwoo Jeon ),( Hyeon Tae Kim ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.1

In the modern world, global warming is a serious problem that is predominantly caused by greenhouse gases (GHGs). However, due to the demand of pork, carbon dioxide (CO₂) emissions are increasing dramatically from swine burns, which have a significant impact on increasing GHGs in the atmosphere. Therefore, the objectives of this study were to measure the CO₂ emissions based on age, body weight gain and different activities of swine. The experiment was conducted in an experimental swine barn from September to December, 2022. A load cell and a livestock environment management system (LEMS) were used to measure the body weight of swine’s and CO₂ emissions level inside the barn, respectively. A 2d camera was used to record the swine’s different activities on a daily basis. The findings of the study showed that the CO₂ emissions were strongly correlated with body weight (r = 0.83) and age of swine (r = 0.86). In this study, we also found that the CO₂ emissions were highest at sleeping time (1-2 PM) and lowest at feeding time (5-6 PM). Moreover, the CO₂ emissions during sleeping (1-2 PM) and feeding (5-6 PM) time were significantly different from other activities (P < 0.05). In conclusion, this study recommends additional research need to be conducted in the different seasons to estimate the CO₂ emissions in concern to swine’s age, body weight and different activities by providing different additives of diets.

Machine learning approaches in modeling of digestible energy demand in mature phase of swine

( Nibas Chandra Deb ),( Jayanta Kumar Basak ),( Sijan Karki ),( Daeyeong Kang ),( Niraj Tamrakar ),( Eun-wan Seo ),( Seong Woo Jeon ),( Hyeon Tae Kim ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.2

Digestible energy (DE) is an essential component of swine production, required to maintain proper growth, and reproducibility of swine. Therefore, the objective of this study was to evaluate the DE demand in mature phase of pigs by proving different types of feed in different pig barns during 2021 and 2022. A load cell and a livestock environment management system (LEMS) were used to measure the body weight of swine’s, room temperature, and humidity level inside the barn, respectively. A total of four ML models (MLR, SVR, RFR, and MLP) were used to forecast the DE demand, with the input parameters of room temperature, humidity, age, and body mass of swine's. The result of the study showed that age and body mass were positively correlated with DE (r>0.90). It was revealed that RFR model provided the best result (R2>0.94) compared to other models. However, MLR model provided the worst result (R2<0.94) compared to other models. Additionally, sensitivity analysis indicated that body mass had the greatest impact on forecasting DE compared to other input parameters. Finally, the study colcluded that RFR model can predict DE accurately, which can assist farmer's to proper management of DE in mature phase of swine production.

Classification of Pig Behaviour Experiencing Impaired Air Quality Due to Elevated GHG Concentration

( Niraj Tamrakar ),( Jayanta Kumar Basak ),( Nibas Chandra Deb ),( Sijan Karki ),( Myeongyong Kang ),( Daeyeong Kang ),( Seong Woo Jeon ),( Hyeon Tae Kim ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.2

Pig welfare and health are the primary concerns in livestock management. Pig behaviors serve as important early indicators of pig stress. This research utilizes AI-based computer vision with a top-view RGB camera to monitor the behavior of pigs. The pigs were detected in an experimental pig barn using a CNN-based deep learning model, and pig motions were tracked in real-time using the Deep SORT algorithm. This approach helps segment different pig postures and assigns an activity score based on activity tracking. Various CNN-based deep learning models (Yolov7, Faster R-CNN, SSD ResNet101) were implemented to detect pigs in different postures. When comparing different pig identification and posture detection models, Yolov7 was found to be the fastest and most accurate, with a mean average precision (MAP) of 97.84%. Similarly, the accuracy of multiple object tracking was 93.2%, and the precision was 81.4% for the tracking algorithm. The study observed behavioral changes in both groups and individuals due to the natural elevation of GHG concentration in the experimental pig barn. Higher GHG concentrations were found to have a negative correlation with pig postures like standing, walking, and sternal lying activities, whereas lateral lying had a positive correlation.

A Comparison of Recurrent Neural Network for Forecasting Short Term Solar Irradiance

( Niraj Tamrakar ),( Jayanta Kumar Basak ),( Bhola Paudel ),( Nibas Chandra Deb ),( Hyeon Tae Kim ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.1

Efficient and timely supply of renewable energy relies heavily on accurate solar irradiance forecasting. The study aims to develop reliable short-term solar irradiance prediction models with a 5-minute time interval, using five different variants of recurrent neural networks (RNN). These models include long-short term memory (LSTM), gated recurrent unit (GRU), simple RNN, bidirectional LSTM (Bi-LSTM), and bidirectional GRU (Bi-GRU). The first three models are unidirectional, while the last two are bidirectional RNNs. The dataset used in this study spans 26 months of highly volatile weather conditions in Jinju city, South Korea. To achieve effective results, careful experimentation and selection of five hyper-parameters for each model were conducted. Additionally, the models were tested with varying levels of depth and width, and evaluated using a 9-fold cross-validation method to account for the high variability in the seasonal time-series dataset. Notably, the Bi-GRU model produced the lowest root mean square error (RMSE) and the highest R2 values of 46.1 and 0.958, respectively, and also incurred the lowest computational cost at 5.25*105 seconds per trainable parameter per epoch. In the 9-fold cross-validation test, all five models showed different performances across the four seasons, but on average, the bidirectional RNNs and the simple RNN model demonstrated high robustness with less data and high temporal data variability. However, the stronger architectures of the bidirectional models make their results more reliable.

Applicability of Deep Learning Network on Gray Mold Disease Detection on Strawberry Leaves

( Sijan Karki ),( Jayanta Kumar Basak ),( Bhola Paudel ),( Na Eun Kim ),( Nibas Chandra Deb ),( Hyeon Tae Kim ) 한국농업기계학회 2022 한국농업기계학회 학술발표논문집 Vol.27 No.2

Early and accurate disease detection in the plant is crucial to mitigate its effect and maximize the yield. Gray mold is considered the most devastating strawberry disease, leading to complete plant death. Various machine learning and deep learning-based models were developed in the past. However, most studies used a controlled environment to capture the images and trained a model whose performance decreased when the models were tested images captured in the field. Therefore, there has been a need for a model that can detect and quantify plant disease accurately, especially in the natural environment. Therefore, this study developed an image segmentation model based on deep learning to distinguish the gray mold disease in strawberry plants. Three groups of strawberry plants (ten plants in each group) were inoculated with different concentrations of necrotrophic fungus pathogen (Botrytis cinerea) and observed the resulting disease. The deep learning model (Unet) was trained with images captured in a natural environment non-destructively. Model performance was assessed using evaluation metrics like intersection over union (IoU), pixel accuracy, and dice accuracy. Furthermore, two machine learning-based models (K-means and XGBoost) were also trained with the same images, and the performance of these models was compared. The deep learning-based model had an average IoU accuracy of 82.12%, dice accuracy of 89.71%, and pixel accuracy of 98.24%, surpassing both machine learning models in multiple aspects. The XGBoost model had an average IoU accuracy of 80.89%, dice accuracy of 85.40%, and pixel accuracy of 98.16%, which performed consistently well in identifying the disease following the deep learning-based model. In conclusion, the developed model could be a valuable tool for strawberry farmers with a simple computational setup in gray mold disease detection.

채소정식기의 절삭날에 의한 생분해성 포트의 낙하속도 측정

전성우 ( Seong-woo Jeon ),이건호 ( Gun-ho Lee ),볼라 ( Bhola-paudel ),파우델 ( Nibas-chandra Deb ),니바스찬드라뎁 ( Hyeon-tae Kim ),김현태 한국농업기계학회 2022 한국농업기계학회 학술발표논문집 Vol.27 No.2

농업의 기계화가 진행됨에 따라 벼농사의 기계화율은 97%를 넘어가는 것에 비하여 밭농업 기계화율은 50%에 머물러 있다. 밭농업의 기계화를 위한 작물에 맞는 밭작물 기계를 연구에 관심이 높아지고 있지만, 여전히 농민의 요구에 비해 부족한 상황이다. 밭농업 기계 중 채소 정식기는 기존의 인력 작업속도 보다는 빠르지만 취출시 작물의 뿌리에 손상을 주거나 복토의 파괴 등 문제점이 나타났다. 본 연구의 목적은 생분해성 포트용 채소 정식기의 이식 과정 최적화로 컷팅에 의한 포트의 낙하 속도를 측정하는 실험을 진행하였다. 본 실험에는 임의로 선택된 생분해성 포트를 함수율을 다르게 하여 절삭날의 충격량에 따라 변하는 낙하 속도를 초고속카메라로 촬영하였다. 샘플 포트를 무게측정 한 후 증류수에 5분간 담가둔 뒤 물기를 털고 다시 5분간 담가두는 과정을 30분간 반복 하였다. 이 후 침지 시간을 15분으로 늘리고 이 과정을 240분간 16번 반복하였다. 24시간 25℃에서 침지 하여 최대 수분 흡수를 결정하였다. 생분해성 포트를 건조 시간을 날짜순으로 하여 낙하실험을 진행하였다. 초고속카메라를 사용하여 프레임당 포트가 변하는 위치를 계산해서 낙하 속도를 계산하였다.

Applicability of Deep Learning Network on Gray Mold Disease Detection on Strawberry Leaves

( Sijan Karki ),( Jayanta Kumar Basak ),( Bhola Paudel ),( Na Eun Kim ),( Nibas Chandra Deb ),( Hyeon Tae Kim ) 한국농업기계학회 2022 한국농업기계학회 학술발표논문집 Vol.27 No.2

Early and accurate disease detection in the plant is crucial to mitigate its effect and maximize the yield. Gray mold is considered the most devastating strawberry disease, leading to complete plant death. Various machine learning and deep learning-based models were developed in the past. However, most studies used a controlled environment to capture the images and trained a model whose performance decreased when the models were tested images captured in the field. Therefore, there has been a need for a model that can detect and quantify plant disease accurately, especially in the natural environment. Therefore, this study developed an image segmentation model based on deep learning to distinguish the gray mold disease in strawberry plants. Three groups of strawberry plants (ten plants in each group) were inoculated with different concentrations of necrotrophic fungus pathogen (Botrytis cinerea) and observed the resulting disease. The deep learning model (Unet) was trained with images captured in a natural environment non-destructively. Model performance was assessed using evaluation metrics like intersection over union (IoU), pixel accuracy, and dice accuracy. Furthermore, two machine learning-based models (K-means and XGBoost) were also trained with the same images, and the performance of these models was compared. The deep learning-based model had an average IoU accuracy of 82.12%, dice accuracy of 89.71%, and pixel accuracy of 98.24%, surpassing both machine learning models in multiple aspects. The XGBoost model had an average IoU accuracy of 80.89%, dice accuracy of 85.40%, and pixel accuracy of 98.16%, which performed consistently well in identifying the disease following the deep learning-based model. In conclusion, the developed model could be a valuable tool for strawberry farmers with a simple computational setup in gray mold disease detection.

생분해성 육묘 플러그 트레이 자동 취출 정식기 개발

전성우 ( Seong Woo Jeon ),강대영 ( Dae Yeong Kang ),서은완 ( Eun Wan Seo ),파우델볼라 ( Bhola Paudel ),뎁니바스찬드라 ( Nibas Chandra Deb ),김현태 ( Hyeon Tae Kim ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.2

기존 플라스틱 모종 트레이에 비해 생분해성 플러그 트레이는 모종 취출 과정에서 작물 뿌리 손상을 완화시켜 줄 수 있는 잠재적인 해결책을 제공하며, 이로 인해 모종의 성장과 발달에 미치는 부정적인 영향을 최소화할 것으로 보인다. 따라서 이 연구에서는 생분해성 플러그 트레이에서 모종 플러그와 트레이 셀을 분리하여 동시에 이식할 수 있는 완전 자동화된 채소 이식기를 개발하고 분석하였다. 생분해성 플러그 트레이는 종이와 신문 폐기물에 강도 증강 첨가제를 첨가하여 제조했다. 생분해성 플러그 트레이 이식 장치의 메커니즘은 플러그 트레이 이송 메커니즘과 취출 메커니즘의 두 하부 메커니즘으로 구성되었다. 취출 메커니즘은 체인-스프로킷 기어를 사용하여 체인과 연결된 칼날에 의해 모종과 플러그 셀을 함께 트레이에서 분리하고, 이를 모종 플러그 배출 통로로 이동시킨다. 이송 메커니즘은 이중 나선형 홈을 갖춘 나사를 사용하여 플러그 트레이를 가로로 한 칸씩 이동시키고, 캠과 5절 링크의 왕복운동에 의해 연결된 트레이 이송 바가 생분해성 포트를 한 열씩 밀어내어, 모종 플러그를 모종 배출 지점에 정렬시킨다. 이 실험은 정식기 주행 속도에 따른 생분해성 플러그 트레이 추출 및 이송 장치의 성능을 평가하기 위해 실시되었다. 생분해성 포트 취출 성능은 80%의 성공률을 보였다. 제안된 메커니즘은 취출 시 작물 뿌리 손상에 대한 해결책으로서 잠재력을 가지고 있으며, 작업속도와 취출 성능을 개선함으로써 대규모 농업에 최적화될 수 있다.