Effects of LED light intensity on purple leaf lettuce seedlings

Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.

A new approach to measure spatial variability of soil parameters and field technique to test-value specific fertilizer

Information on the distribution of soil properties is important to know the status of nutrients in the soils based on which fertilizer nutrients are recommended. Given the variability of nutrients in the soils, making a site-specific fertilizer recommendation seems to be a compelling work. To determine the spatial variability of soil nutrients and to make judicious and precise fertilizer recommendations, new measures are designed with this study. These measures are tested against the soil samples (n = 43) for total nitrogen (N), organic matter (OM), phosphorus (P2O5), and potassium (K2O) in the study area. The descriptive statistical analysis indicated an average of low nitrogen and organic matter, while phosphorus was found to be very high and the level of potassium was high. The spread of nutrients across the data sets, however, included low, medium, high, and very high levels of ratings. The Deviation Square Index was developed and applied for the variability measurement and found that the largest variation was with phosphorus distribution, followed by potassium, nitrogen, and organic matter. The coefficient of variation (CV%) analysis also exhibited similar trends in nutrient distributions. Nitrogen was the main determinant explaining the variations in rice yield, while phosphorus and potash were negatively related to the yield. An index of fertilizer nutrient recommendation called Test-Value Specific Dose (TVSD) was developed and used to calculate the nutrient recommendation for each sampled location. This new method gave easy and more accurate doses of fertilizer over the blanket recommendation to fit the variations across the soil samples.

Effects of LED light intensity on purple leaf lettuce seedlings

Lvye Dai, Xin Zhang, Yingzi Teng, Yiyin Gu, Li Chen, Xiaoping Kong, Yingyan Han

Article ID: 2109
Vol 3, Issue 2, 2022

DOI: https://doi.org/10.54517/ama.v3i2.2109
VIEWS - 6709 (Abstract)

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Abstract

To explore the light intensity suitable for the growth of purple leaf lettuce seedlings in plant factories and greenhouses, the effects of light intensity of 6000, 12,000, 18,000, 24,000, and 30,000 lx on growth morphology, nutritional quality, photosynthetic pigment, and photosynthetic characteristics of purple leaf lettuce seedlings were studied with ‘Beizisheng 3’ as the experimental material under the condition of white light quality unchanged. The results showed that under a light intensity of 30,000 lx, purple leaf lettuce seedlings grew well, and the biomass, chlorophyll a content, chlorophyll b content, carotenoid content, total chlorophyll, soluble protein content, soluble sugar content, vitamin C content, anthocyanin content, net photosynthetic rate, transpiration rate, and stomatal conductance were the largest, while the nitrate content and intercellular CO₂ concentration were the lowest. However, chlorophyll b content, carotenoid content, soluble protein content, and transpiration rate had no significant difference compared with those under 24000 lx light intensity, and the plant height was the highest under 24,000 lx light intensity. Under the condition of white light quality, the most suitable light intensity for the growth of purple leaf lettuce seedlings is 30,000 lx.

Keywords

light intensity; purple leaf lettuce seedlings; growth morphology; nutritional quality; photosynthetic pigment; photosynthetic characteristics

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Zhejiang University, China

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