We describe a finite-element (FE) model for simulating wheel-rolling tissue deformations using a rolling FE model (RFEM). A wheeled probe performing rolling tissue indentation has proven to be a promising approach for compensating for the loss of haptic and tactile feedback experienced during robotic-assisted minimally invasive surgery (H. Liu, D. P. Noonan, B. J. Challacombe, P. Dasgupta, L. D. Seneviratne, and K. Althoefer, “Rolling mechanical imaging for tissue abnormality localization during minimally invasive surgery, ” IEEE Trans. Biomed. Eng., vol. 57, no. 2, pp. 404-414, Feb. 2010; K. Sangpradit, H. Liu, L. Seneviratne, and K. Althoefer, “Tissue identification using inverse finite element analysis of rolling indentation,” in Proc. IEEE Int. Conf. Robot. Autom. , Kobe, Japan, 2009, pp. 1250-1255; H. Liu, D. Noonan, K. Althoefer, and L. Seneviratne, “The rolling approach for soft tissue modeling and mechanical imaging during robot-assisted minimally invasive surgery,” in Proc. IEEE Int. Conf. Robot. Autom., May 2008, pp. 845-850; H. Liu, P. Puangmali, D. Zbyszewski, O. Elhage, P. Dasgupta, J. S. Dai, L. Seneviratne, and K. Althoefer, “An indentation depth-force sensing wheeled probe for abnormality identification during minimally invasive surgery,” Proc. Inst. Mech. Eng., H, vol. 224, no. 6, pp. 751-63, 2010; D. Noonan, H. Liu, Y. Zweiri, K. Althoefer, and L. Seneviratne, “A dual-function wheeled probe for tissue viscoelastic property identification during minimally invasive surgery,” in Proc. IEEE Int. Conf. Robot. Autom. , 2008, pp. 2629-2634; H. Liu, J. Li, Q. I. Poon, L. D. Seneviratne, and K. Althoefer, “Miniaturized force indentation-depth sensor for tissue abnormality identification,” IEEE Int. Conf. Robot. Autom., May 2010, pp. 3654-3659). A sound understanding of wheel-tissue rolling interaction dynamics will facilitate the evaluation of signals from rolling indentation. In this paper, we model the dynamic interactions between a wheeled probe and a soft tissue sample using the ABAQUS FE software package. The aim of this work is to more precisely locate abnormalities within soft tissue organs using RFEM and hence aid surgeons to improve diagnostic ability. The soft tissue is modeled as a nonlinear hyperelastic material with geometrical nonlinearity. The proposed RFEM was validated on a silicone phantom and a porcine kidney sample. The results show that the proposed method can predict the wheel--tissue interaction forces of rolling indentation with good accuracy and can also accurately identify the location and depth of simulated tumors.

This research introduces the PEnsemble 4 model, a weighted ensemble prediction model that integrates multiple individual machine learning models to achieve accurate maize yield forecasting. The model incorporates unmanned aerial vehicle (UAV) imagery and Internet of Things (IoT)-based environmental data, providing a comprehensive and data-driven approach to yield prediction in maize cultivation. Considering the projected growth in global maize demand and the vulnerability of maize crops to weather conditions, improved prediction capabilities are of paramount importance. The PEnsemble 4 model addresses this need by leveraging comprehensive datasets encompassing soil attributes, nutrient composition, weather conditions, and UAV-captured vegetation imagery. By employing a combination of Huber and M estimates, the model effectively analyzes temporal patterns in vegetation indices, in particular CIre and NDRE, which serve as reliable indicators of canopy density and plant height. Notably, the PEnsemble 4 model demonstrates a remarkable accuracy rate of 91%. It advances the timeline for yield prediction from the conventional reproductive stage (R6) to the blister stage (R2), enabling earlier estimation and enhancing decision-making processes in farming operations. Moreover, the model extends its benefits beyond yield prediction, facilitating the detection of water and crop stress, as well as disease monitoring in broader agricultural contexts. By synergistically integrating IoT and machine learning technologies, the PEnsemble 4 model presents a novel and promising solution for maize yield prediction. Its application holds the potential to revolutionize crop management and protection, contributing to efficient and sustainable farming practices.

This paper presents the PEnsemble 4 model, a sophisticated machine learning framework that integrates IoT-based environmental data to accurately forecast maize yield. With the projected significant growth in global maize demand over the next decade, the inherent risks posed by the crop’s dependence on weather conditions necessitate improved prediction capabilities. The PEnsemble 4 model, developed with high accuracy, incorporates comprehensive datasets encompassing soil attributes, nutrient composition, weather conditions, and UAV-captured vegetation imagery. By employing a combination of Huber and M estimates, the PEnsemble 4 model effectively analyzes temporal patterns in vegetation indices, specifically CIre and NDRE, which serve as reliable indicators of canopy density and plant height. In addition, this research significantly contributes to precision agriculture by offering an efficient and sustainable alternative to conventional farming practices through precise yield predictions. Notably, the PEnsemble 4 model enables earlier estimation, advancing the timeline for yield prediction from the conventional day 100 in the R6 stage to day 79 in the R2 stage. This improvement enhances decision-making processes in farming operations. The remarkable accuracy rate of 91% underscores the importance of adopting a multifaceted data approach that harnesses IoT-derived environmental insights. Additionally, the PEnsemble 4 model extends its benefits beyond yield prediction, facilitating the detection of water and crop stress, as well as disease monitoring in broader agricultural contexts. Ultimately, the PEnsemble 4 model establishes a new standard in maize yield prediction, revolutionizing crop management and protection through the synergistic utilization of IoT and machine learning technologies.

The integration of a real-time image processing system using multiple webcams with a variable rate spraying system mounted on the back of an unmanned tractor presents an effective solution to the labor shortage in agriculture. This research aims to design and fabricate a low-cost, variable-rate, remote-controlled sprayer specifically for use in sugarcane fields. The primary method involves the modification of a 15-horsepower tractor, which will be equipped with a remote-control system to manage both the driving and steering functions. A foldable remote-controlled spraying arm is installed at the rear of the unmanned tractor. The system operates by using a webcam mounted on the spraying arm to capture high-angle images above the sugarcane canopy. These images are recorded and processed, and the data is relayed to the spraying control system. As a result, chemicals can be sprayed on the sugarcane accurately and efficiently based on the insights gained from image processing. Tests were conducted at various nozzle heights of 0.25 m, 0.5 m, and 0.75 m. The average system efficiency was found to be 85.30% at a pressure of 1 bar, with a chemical spraying rate of 36 L per hour and a working capacity of 0.975 hectares per hour. The energy consumption recorded was 0.161 kWh, while fuel consumption was measured at 6.807 L per hour. In conclusion, the development of the remote-controlled variable rate sprayer mounted on an unmanned tractor enables immediate and precise chemical application through remote control. This results in high-precision spraying and uniform distribution, ultimately leading to cost savings, particularly by allowing for adjustments in nozzle height from a minimum of 0.25 m to a maximum of 0.75 m from the target.

Background and Aim: Postpartum endometritis is a major cause of infertility in dairy cattle, impacting herd productivity and economic sustainability. Although traditional diagnostic methods are available, there remains a need for simple, rapid, and accurate cow-side diagnostic tools to facilitate early detection and management of clinical endometritis (CE) and subclinical endometritis (SCE). This study aimed to (i) evaluate the diagnostic performance of vaginal discharge parameters - pH, total dissolved solids (TDS), salinity, and electrical conductivity (EC) - using a portable flat-surface electrode probe, and pH and leukocyte esterase (LE) activity using reagent strips and (ii) establish optimal cut-off values for these parameters to support practical on-farm screening of endometritis. Materials and Methods: Fifty-eight postpartum Holstein Friesian cows were enrolled. Vaginal discharge was collected using a Metricheck device and analyzed for pH, TDS, salinity, and EC. Concurrently, pH and LE activity were assessed using reagent strips. Endometrial cytology was performed to diagnose CE and SCE. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. Results: pH values measured by both the meter and strip were significantly higher in cows with normal uterine health compared to those with CE (p = 0.010 and p = 0.008, respectively). LE activity was significantly elevated in cows with CE (p = 0.001). ROC analysis identified optimal cut-off values: pH meter ≤8.35 (area under the curve [AUC] = 0.768) and LE strip ≥2 (AUC = 0.835) for diagnosing CE. Combining pH and LE strip results improved diagnostic performance (AUC = 0.801), achieving 65.22% sensitivity, 87.5% specificity, and 76.6% accuracy. TDS, salinity, and EC were not significantly associated with uterine health status (p > 0.05). Conclusion: The combined evaluation of vaginal discharge pH and LE activity offers a practical, cost-effective cow-side screening method for diagnosing endometritis in dairy cattle. In contrast, TDS, salinity, and EC measurements were not diagnostically informative. The proposed approach may enhance herd health management by enabling timely identification and treatment of endometritis. Keywords: bovine endometritis, cow-side diagnosis, leukocyte esterase strip, pH meter, postpartum reproductive health, vaginal discharge analysis.

The integration of a centrifugal extraction and pipeline transportation system to improve efficiency in shrimp harvesting management into closed-pond aquaculture systems represents a significant advancement in aquaculture technology. This study introduces and assesses the efficiency of the shrimp harvester compared to manual harvesting methods, examining key parameters such as shrimp harvester quality, shrimp harvester loss rates, and shrimp harvester speed. A particularly noteworthy aspect is the innovative transportation of shrimp through a pipeline, which enhances the transformative potential of this technology. Results indicate that the centrifugal shrimp harvester outperforms manual methods, achieving an impressive yield rate of 3338 kg/h with a minimal loss rate of 0.01%; the trend values for harvester capacity ranged from 0.501 to 1.884 tons of shrimp per hour at 240 rpm, 2.391 to 3.081 tons per hour at 270 rpm, and 3.338 to 3.816 tons per hour at 300 rpm. While this technology shows promise for increasing productivity and minimizing shrimp damage, further investigation is needed to evaluate its economic viability, including operational costs and labor expenses. The study highlights the transformative potential of the centrifugal shrimp harvester and emphasizes the need for ongoing research to ensure its practical application in real-world aquaculture settings. Overall, the centrifugal shrimp harvester is poised to revolutionize shrimp harvesting practices, contributing to more sustainable and efficient aquaculture production.