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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 irrigation methods on transpiration and water use efficiency of tomato

Jun Yan, Meili Sun, Zizhu Lu, Lingwei Kong, Weixuan Wang, Weiwei Zhou, Bin Liang, Junliang Li, Haofeng Lv

Article ID: 2048
Vol 3, Issue 1, 2022

DOI: https://doi.org/10.54517/ama.v3i1.2048
VIEWS - 3007 (Abstract)

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Abstract

Objective: To explore the effect of high-frequency drip irrigation on the physiological water savings of facility tomatoes. Methods: A pot experiment was conducted to study the effects and mechanisms of conventional flood irrigation (CFI) and high-frequency drip irrigation (HDI) on the transpiration and water use efficiency of tomatoes. Results: a. During the whole growth period, the irrigation times of high-frequency drip irrigation were 44 times, which was 3.4 times of traditional flood irrigation, and the irrigation amount was only 71.0% of flood irrigation. However, there was no significant difference in fruit, stem, or leaf dry weight between the two treatments (p > 0.05). b. The average transpiration of tomatoes under high-frequency drip irrigation was 28.95 L/plant, which was significantly lower than that under traditional flood irrigation by 24.6%. c. Compared with conventional flood irrigation, high-frequency drip irrigation treatment significantly increased irrigation water use efficiency (biomass/irrigation volume) and water physiological use efficiency (biomass/transpiration volume) by 21.2% and 14.2%, respectively (p < 0.05). d. Compared with conventional flood irrigation, high-frequency drip irrigation significantly decreased tomato leaf area, stomatal conductance, and transpiration rate by 32.6%, 69.8%, and 54.3%, respectively, and significantly increased leaf δ¹³C value (p < 0.05). Conclusion: High-frequency drip irrigation can not only reduce the amount of irrigation, and improve the utilization efficiency of irrigation water, but also put the plant in a certain state of water deficit, reduce leaf area, stomatal conductance, and transpiration rate, and then significantly reduce transpiration water consumption, improve the physiological water use efficiency of tomatoes, and realize physiological water savings.

Keywords

facility tomato; irrigation mode; transpiration consumes water; water use efficiency; stomatal conductance

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Zhejiang University, China

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