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BACKGROUND: Low recovery of conventional fertilizers remains a significant bottleneck for maize production globally. In particular, with phosphate fertilization, zinc (Zn) is prone to precipitation in soil, reducing recovery of both phosphorus (P) and Zn by maize. RESULTS: The present study was designed to investigate the synergistic effect of zinc oxide (ZnO) nanoparticles (NPs) and P on maize crop growth, yield, and nutrient uptake under ZnO seed coating and foliar application in a randomized complete block design. However, plants were subjected to two ZnO NPs levels (0.5 and 12 kg ha−1) amended with two P levels (45 and 90 kg ha−1). ZnO NPs, especially in the form of foliar application, with a P dose of 90 Kg ha−1 significantly (P < 0.05) improved maize crop growth, yield, and nutrient uptake compared with control. In comparison with the control group, plants grown in these conditions absorbed higher levels of Zn and P. Zn uptake rose to 16.34 g ha−1, 137.88 g ha−1, and 166.89 g ha−1 in roots, grains, and stover respectively, and P uptake increased to 0.80 mg kg−1, 10.066 mg kg−1, and 12.17 mg kg−1 respectively. Additionally, seed emergence rate, plant height, and cob length increased by up to 2%, 1177 cm2, and 3.3 cm respectively compared with control. Furthermore, Zn use efficiency was increased up to 38.55% in ZnO NPs foliar application. CONCLUSIONS: Application of ZnO NPs at 0.5 kg ha−1 in the form of foliar application with 90 kg ha−1 P dose produced a more pronounced increment in the parameters studied than ZnO NPs seed coating did. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Smart agriculture is steadily progressing towards automation and heightened efficacy. The rapid ascent of deep learning technology provides a robust foundation for this trajectory. Leveraging computer vision and the depths of deep learning techniques enables real-time monitoring and management within agriculture, facilitating swift detection of plant growth and autonomous assessment of ripeness. In response to the demands of smart agriculture, this exposition delves into automated citrus harvesting, presenting an ATT-MRCNN target detection model that seamlessly integrates channel attention and spatial attention mechanisms for discerning and identifying citrus images. This framework commences by subjecting diverse citrus image classifications to Mask Region-based CNN's (Mask RCNN's) discerning scrutiny, enhancing the model's efficacy through the incorporation of attention mechanisms. During the model's training phase, transfer learning is utilized to expand data performance and optimize training efficiency, culminating in parameter initialization. Empirical results notably demonstrate that this method achieves a recognition rate surpassing the 95% threshold across the three sensory recognition tasks. This provides invaluable algorithmic support and essential guidance for the imminent era of intelligent harvesting. [ABSTRACT FROM AUTHOR]

Background: Invasive fungal infections are not only responsible for the increased morbidity and mortality in human population but are also a major concern throughout the world because of the increased antifungal resistance. Objective: The study was conducted to find out the prevalence of Aspergillus species endemic in our area along with the comorbidities in suspected cases of Pulmonary Aspergillosis. Materials and methods: In this cross-sectional study, 73 cases of Pulmonary Aspergillosis were included. Aspergillus species were isolated from the pulmonary specimens of those patients who were visiting the Out Patient Department (OPD) or were admitted to Lahore General Hospital, Lahore. Results: In this study, the most prevalent specie of Aspergillus to be isolated from the pulmonary specimens was found to be Aspergillus flavus 39 (53.4%) followed by Aspergillus niger 21 (28.8%), Aspergillus fumigatus 12 (16.4%) and Aspergillus terreus 1 (1.4%). Co-morbidities of the patients were greatest with Asthma 29 (40%), followed by Tuberculosis 22 (30.1%) and Chronic Obstructive Pulmonary Disease (COPD) 22 (30.1%) . Conclusion: The data of the present study can serve as a basis for future surveillance of clinically significant and prevalent Aspergillus species. Correlation with co-morbidities could play a valuable role in better understanding of the disease pathophysiology. [ABSTRACT FROM AUTHOR]