Working speed optimization of the fully automated vegetable seedling transplanter

The purpose of this study was to determine the optimal operating speeds for a low-speed automated vegetable transplanter that utilized a modified linkage cum hopper-type planting unit. A biodegradable seedling plug-tray feeding mechanism is employed by the transplanter. Using kinematic simulation software, the planter unit’s movement was simulated under various operating conditions. The resulting trajectories were compared based on variables like plant spacing, soil intrusion area, soil intrusion perimeter, and horizontal hopper displacement in the soil. It was discovered that the best results occurred at 200, 250, and 300 mm/s and 40, 50, and 60 rpm combinations. Following testing in a soil bin facility, it was discovered that the ideal operating speeds performed well when transplanting pepper seedlings, with measured plant spacing that was nearly identical to the theoretical spacing. While the planting angle in various speed combinations was found to be significantly different, but still within acceptable bounds, the planting depth in each case did not differ statistically. The optimal speed combinations that were chosen resulted in minimal damage to the mulch film. The best speeds for the transplanter were found through this investigation, and these speeds can be used as a foundation for refining the other mechanisms in the transplanter. © the Author(s), 2024

Aneurysmal Bone Cyst of Talus

Aneurysmal bone cyst (ABC) of talus is rare benign, expansile and osteolytic bone growth. Cyst contains bloody fluid lined with variable amount of osteolytic giant cells. This is common in epiphyseal ends of long bone and rare in small bones like talus. Here a 20 years’ male with aneurysmal bone cyst of talus managed with wide intralesional curettage with autologous bone graft mixed with synthetic bone graft been presented

Development and field testing of biodegradable seedling plug-tray cutting mechanism for automated vegetable transplanter

Removing seedlings from plug-trays to transplant in the field poses transplanting shocks to the seedlings and may reduce the survival rate. Therefore, this study designed biodegradable plug-tray cutting mechanism (SPCM) that separates seedlings with plug-cells from plug-trays and eliminates a complex clamping mechanism. SPCM consists of three sub-mechanisms that align the plug-cell at the seedling discharge point to cut and separate the plug-cell from the plug-tray, allowing the seedling to fall into the transplanting hopper. The SPCM separated around 82% of the plug-cell and delivered it to the planting unit. Furthermore, the SPCM-equipped transplanter achieved a transplanting performance of 74% with pepper and cabbage seedlings, with an average field efficiency of 68%, field capacity of 0.032-0.035 ha h-1 and required 73% less labour than manual seedling transplanting. The transplanting performance was satisfactory, with most pepper seedlings (85%) transplanted with a planting angle less than 10°, and 7% of cabbage seedlings were inclined and had sufficient planting depth of 48 mm for cabbage and 53 mm for pepper. In conclusion, the SPCM is a step towards sustainable and efficient vegetable seedling transplanting. Increasing efficiency, planting accuracy, and sustainability present exciting opportunities for further research and development in the field

Estimation of energy balance throughout the growing–finishing stage of pigs in an experimental pig barn

Monitoring the energy inputs and outputs in pig production systems is crucial for identifying potential imbalances and promoting energy efficiency. Therefore, the objective of this study was to measure the energy input, output, and losses during the growing–finishing phase of pigs from 1 September to 1 December 2023. A Livestock Environment Management System (LEMS) was used to measure the temperature, humidity, airflow, and water consumption levels inside the barn, and a load cell was used to measure the body weight of pigs. Furthermore, a bomb calorimetric test was conducted to measure the energy content of pigs’ manure. While calculating energy balance in the experimental barn, it was found that energy from feed and water contributed approximately 81% of the total input energy, while the remaining 19% of energy came from electrical energy. Regarding output energy, manure, and body weight accounted for about 69%, while around 31% was lost due to pig activities, maintaining barn temperature and airflow, and illuminating the barn. In conclusion, this study suggested methods to calculate energy balance in pig barns, offering valuable insights for pig farmers to enhance their understanding of input and output energy in pig production. © 2024 by the authors

Strawberry disease detection using transfer learning of deep convolutional neural networks

The impact of disease on strawberry quality and yield holds considerable significance, prompting researchers to explore effective methodologies for disease detection in strawberries. Among these, deep learning has emerged as a pivotal approach. In this regard, this research explored the utilization of transfer learning in deep convolutional neural networks (CNNs) to identify various strawberry diseases. Specifically, we utilized models pre-trained on the ImageNet dataset, namely VGG19, Inception V3, ResNet50, and DenseNet121 architectures, employing both fine-tuning and feature extraction techniques of transfer learning and consequently compared to the models without transfer learning. The target diseases for identification included angular leaf spot, anthracnose, gray mold, and powdery mildew on both fruit and leaves. The study outcomes revealed that Resnet-50 consistently achieved the highest accuracy across all three configurations, achieving its peak accuracy at 94.4 %, followed by Densenet-121 with an accuracy of 94.1 % attained through fine-tuning. These results highlighted the superior performance of fine-tuned models over using these models solely as feature extractors for identifying strawberry diseases. Furthermore, this study revealed that the application of transfer learning substantially reduced training time and resulted in a lower count of trainable parameters than models trained without transfer learning. These outcomes strongly endorse the practicality and effectiveness of employing transfer learning techniques for precise strawberry disease identification. Additionally, further research can explore the application of transfer learning to a broader range of crops and diseases, potentially enhancing agricultural disease detection methodologies. © 2024 Elsevier B.V