Response of Eustoma Leaf Phenotype and Photosynthetic Performance to LED Light Quality

In a controlled environment, light from light-emitting diodes (LEDs) has been associated with affecting the leaf characteristics of Eustoma. LEDs help plant growth and development, yet little is known about photosynthetic performance and related anatomical features in the early growth stage of Eustoma leaves. In this study, we examined the effects of blue (B), red (R), and white (W) LEDs on the photosynthetic performance of Eustoma leaves, as well as leaf morphology and anatomy including epidermal layer thickness, palisade cells, and stomatal characteristics. Leaves grown under B LEDs were thicker and had a higher chlorophyll content than those grown under the R and W LEDs. Leaves under B LEDs had greater net photosynthetic rates (A), stomatal conductance (gs), and transpiration rates (E), especially at a higher photon flux density (PPFD), that resulted in a decrease in the intercellular CO2 concentration (Ci), than leaves under the W and R LEDs. B LEDs resulted in greater abaxial epidermal layer thickness and palisade cell length and width than the R and W LED treatments. The palisade cells also developed a more cylindrical shape in response to the B LEDs. B LED leaves also showed greater guard cell length, breadth, and area, and stomatal density, than W or R LEDs, which may contribute to increased A, gs and E at higher PPFDs

Response of Eustoma Leaf Phenotype and Photosynthetic Performance to LED Light Quality

In a controlled environment, light from light-emitting diodes (LEDs) has been associated with affecting the leaf characteristics of Eustoma. LEDs help plant growth and development, yet little is known about photosynthetic performance and related anatomical features in the early growth stage of Eustoma leaves. In this study, we examined the effects of blue (B), red (R), and white (W) LEDs on the photosynthetic performance of Eustoma leaves, as well as leaf morphology and anatomy including epidermal layer thickness, palisade cells, and stomatal characteristics. Leaves grown under B LEDs were thicker and had a higher chlorophyll content than those grown under the R and W LEDs. Leaves under B LEDs had greater net photosynthetic rates (A), stomatal conductance (gs), and transpiration rates (E), especially at a higher photon flux density (PPFD), that resulted in a decrease in the intercellular CO2 concentration (Ci), than leaves under the W and R LEDs. B LEDs resulted in greater abaxial epidermal layer thickness and palisade cell length and width than the R and W LED treatments. The palisade cells also developed a more cylindrical shape in response to the B LEDs. B LED leaves also showed greater guard cell length, breadth, and area, and stomatal density, than W or R LEDs, which may contribute to increased A, gs and E at higher PPFDs

Microbial Quality Assessment and Efficacy of Low-Cost Disinfectants on Fresh Fruits and Vegetables Collected from Urban Areas of Dhaka, Bangladesh

This study aimed to examine the total viable bacteria (TVBC); total coliform (TCC); fecal coliform (TFC); pathogenic Pseudomonas spp., Staphylococcus aureus, and total fungi (TF); and the effect of different low-cost disinfectants (sterile water, salt water, blanched, and vinegar) in decontamination of 12 types of fruit and 10 types of vegetables. In fruit samples, the lowest TVBC was enumerated at 3.18 +/- 0.27 log CFU/g in Indian gooseberry and the highest at 6.47 +/- 0.68 log CFU/g in guava. Staphylococci (2.04 +/- 0.53-5.10 +/- 0.02 log CFU/g), Pseudomonas (1.88 +/- 0.03-5.38 +/- 0.08 log CFU/g), and total fungi (2.60 +/- 0.18-7.50 +/- 0.15 log CFU/g) were found in all fruit samples; however, no Salmonella was detected in fruit samples. Similarly, the lowest TVBC recorded 5.67 +/- 0.49 log CFU/g in cucumber and the highest 7.37 +/- 0.06 log CFU/g in yard long bean. The Staphylococci (3.48 +/- 0.13-4.81 +/- 0.16 log CFU/g), Pseudomonas (3.57 +/- 0.21- 4.75 +/- 0.23 log CFU/g), TCC (1.85 +/- 1.11-56.50 +/- 37.14 MPN/g), TFC (1.76 +/- 0.87- 3.78 +/- 3.76 MPN/g), and TF (3.79 +/- 0.18-4.40 +/- 0.38 log CFU/g) were recorded in all vegetables samples, but no Salmonella was detected in yard long bean, pointed gourd, carrot, tomato, cucumber, or brinjal. However, vinegar showed the highest microbial load reduction of selected fruit and vegetables among the different treatments. With vinegar treatment, the highest reduction of TVBC (1.61-log) and TF (2.54-log) was observed for fruits, and TVBC (2.31-log) and TF (2.41-log) for vegetables. All the disinfectant treatments resulted in significant (p < 0.01) bacterial load reduction compared to control for the studied fruits and vegetable samples

A Review on UAV-Based Applications for Plant Disease Detection and Monitoring

peer reviewedRemote sensing technology is vital for precision agriculture, aiding in early issue detection, resource management, and environmentally friendly practices. Recent advances in remote sensing technology and data processing have propelled unmanned aerial vehicles (UAVs) into valuable tools for obtaining detailed data on plant diseases with high spatial, temporal, and spectral resolution. Given the growing body of scholarly research centered on UAV-based disease detection, a comprehensive review and analysis of current studies becomes imperative to provide a panoramic view of evolving methodologies in plant disease monitoring and to strategically evaluate the potential and limitations of such strategies. This study undertakes a systematic quantitative literature review to summarize existing literature and discern current research trends in UAV-based applications for plant disease detection and monitoring. Results reveal a global disparity in research on the topic, with Asian countries being the top contributing countries (43 out of 103 papers). World regions such as Oceania and Africa exhibit comparatively lesser representation. To date, research has largely focused on diseases affecting wheat, sugar beet, potato, maize, and grapevine. Multispectral, reg-green-blue, and hyperspectral sensors were most often used to detect and identify disease symptoms, with current trends pointing to approaches integrating multiple sensors and the use of machine learning and deep learning techniques. Future research should prioritize (i) development of cost-effective and user-friendly UAVs, (ii) integration with emerging agricultural technologies, (iii) improved data acquisition and processing efficiency (iv) diverse testing scenarios, and (v) ethical considerations through proper regulations.European Project FoodLan