The application of digital technologies to facilitate farming activities has been on the rise in recent years. Among different tasks, the classification of weeds is a prerequisite for smart farming, and various techniques have been proposed to automatically detect weeds from images. However, many studies deal with weed images collected in the laboratory settings, and this might not be applicable to real-world scenarios. In this sense, there is still the need for robust classification systems that can be deployed in the field. In this work, we propose a practical solution to recognition of weeds exploiting two versions of EfficientNet as the recommendation engine. More importantly, to make the learning more effective, we also utilize different transfer learning strategies. The final aim is to build an expert system capable of accurately detecting weeds from lively captured images. We evaluate the approach’s performance using DeepWeeds, a real-world dataset with 17,509 images. The experimental results show that the application of EfficientNet and transfer learning on the considered dataset substantially improves the overall prediction accuracy in various settings. Through the evaluation, we also demonstrate that the conceived tool outperforms various state-of-the-art baselines. We expect that the proposed framework can be installed in robots to work on rice fields in Vietnam, allowing farmers to find and eliminate weeds in an automatic manner.

Background: The pandemic caused by coronavirus in recent months is having a devastating global effect, which puts the world under the most ever unprecedented emergency. Currently, since there are not effective antiviral treatments for Covid-19 yet, it is crucial to early detect and monitor the progression of the disease, thus helping to reduce mortality. While a corresponding vaccine is being developed, and different measures are being used to combat the virus, medical imaging techniques have also been investigated to assist doctors in diagnosing this disease. Objective: This paper presents a practical solution for the detection of Covid-19 from chest X-ray (CXR) images, exploiting cutting-edge Machine Learning techniques. Methods: We employ EfficientNet and MixNet, two recently developed families of deep neural networks, as the main classification engine. Furthermore, we also apply different transfer learning strategies, aiming at making the training process more accurate and efficient. The proposed approach has been validated by means of two real datasets, the former consists of 13,511 training images and 1,489 testing images, the latter has 14,324 and 3,581 images for training and testing, respectively. Results: The results are promising: by all the experimental configurations considered in the evaluation, our approach always yields an accuracy larger than 95.0%, with the maximum accuracy obtained being 96.64%. Conclusions: As a comparison with various existing studies, we can thus conclude that our performance improvement is significant.

Purpose: To evaluate the cumulative radiobiological impact of daily megavoltage cone‐beam computed tomography (MV‐CBCT) imaging dose based on normal tissue complication probability (NTCP) and excess absolute risk (EAR) of secondary malignancies among radiotherapy patients treated for breast, pelvic, and head & neck cancers. This study investigated whether MV‐CBCT imaging dose warrants protocol personalization according to patient age, anatomical treatment site, and organ‐specific radiosensitivity. Methods: This retrospective study included cohorts of breast ( n = 30), pelvic ( n = 17), and head & neck ( n = 20) cancer patients undergoing radiotherapy with daily MV‐CBCT. Imaging dose distributions employing two common MV‐CBCT protocols (5 and 10 MU per fraction) were analyzed. NTCP values were estimated using logistic models, while EAR were calculated using Schneider's organ equivalent dose (OED)‐based model, integrating organ‐specific dose, patient age, and established tissue‐specific risk coefficients. Comparative statistical analyses were conducted using paired t ‐tests, and results were further stratified by patient age (< 40, 40–60, > 60 years). Results: In breast cancer patients, NTCP values increased significantly for lung tissue when comparing the 10 MU protocol to the 5 MU one ( p < 0.001), while those for heart and breast tissues showed minimal and insignificant differences. EAR estimations revealed substantial risk increases among younger breast cancer patients (< 40 years), with some exceeding 15 cases per 10 000 person‐years under the 10 MU protocol. Conversely, pelvic and head & neck cohorts demonstrated consistently low NTCP and EAR values (< 1%), with no meaningful differences observed between the two imaging protocols. Across all cancer sites, younger age consistently correlated with higher secondary cancer risks. Conclusion: Routine daily MV‐CBCT imaging at the 10 MU protocol possesses minimal additional risk in pelvic and head & neck radiotherapy. However, among breast cancer patients, particularly those under 40 years, the 10 MU protocol significantly elevates the theoretical secondary cancer risk estimates and lung NTCP. These findings support transitioning from conventional uniform imaging approach toward personalized MV‐CBCT protocols, tailored according to patient age, anatomical site, and organ radiosensitivity. A stratified imaging framework is proposed to optimize clinical outcomes, balancing treatment accuracy, and long‐term patient safety.

Purpose: To evaluate the cumulative radiobiological impact of daily Megavoltage Cone-Beam Computed Tomography (MV-CBCT) imaging dose based on Normal Tissue Complication Probability (NTCP) and Excess Absolute Risk (EAR) of secondary malignancies among radiotherapy patients treated for breast, pelvic, and head and neck cancers. This study investigated whether MV-CBCT imaging dose warrants protocol personalization according to patient age, anatomical treatment site, and organ-specific radiosensitivity. Methods: This retrospective study included cohorts of breast (n=30), pelvic (n=17), and head and neck (n=20) cancer patients undergoing radiotherapy with daily MV-CBCT. Imaging plans using two common protocols (5 MU and 10 MU per fraction) were analyzed. NTCP values were estimated using logistic and Lyman-Kutcher-Burman (LKB) models, while EAR was calculated using Schneider's Organ Equivalent Dose (OED)-based model. Statistical analysis used paired t-tests, and results were further stratified by age (under 40, 40 to 60, over 60 years). Results: In breast cancer patients, NTCP for lung increased significantly under the 10 MU protocol (p<0.001). EAR was elevated in younger breast patients (under 40 years), with some exceeding 15 cases per 10,000 person-years. In pelvic and head and neck groups, NTCP and EAR remained low (under 1 percent), with no clinically meaningful differences between protocols. Across all sites, younger age correlated with higher secondary cancer risk. Conclusion: Daily 10 MU MV-CBCT presents minimal additional risk in pelvic and head and neck radiotherapy. For breast cancer patients under 40, however, it significantly increases secondary cancer risk and lung NTCP. Personalized imaging protocols are recommended based on age, treatment site, and radiosensitivity.