Organomineral foliar application modulates photosynthetic pigments and biochemical responses in black mung bean

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.

Organomineral foliar application modulates photosynthetic pigments and biochemical responses in black mung bean

Carolina Fedrigo Coneglian, Luana Thais Varize Marcusso, Graziele Freitas Silva Ferreira, Natieli Jenifer Mateus Corniani, Carlos Augusto Corniani da Silva, Willian Martire Marcusso, João Arthur dos Santos Oliveira

Article ID: 3596
Vol 6, Issue 2, 2025

DOI: https://doi.org/10.54517/ama3596

Received: 11 April 2025; Accepted: 29 May 2025; Available online: 13 June 2025; Issue release: 30 June 2025

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Abstract

The use of organomineral products in agriculture offers promising solutions for crop productivity and sustainability. This study examines the effects of foliar application of a biostimulant containing Ascophyllum nodosum algae extract and a blend of amino acids on black mung bean (Vigna radiata L.), focusing on its potential to modulate beneficial biochemical responses. Under greenhouse conditions, total chlorophyll content was 20% higher (198 µg/g) in treated plants than in untreated controls (167 µg/g). Carotenoid concentrations were also higher (115 µg/g) in treated plants compared to 98 µg/g in the control plants. Considering redox system enzymes, catalase (4180 nmol/µg), peroxidase (6656 nmol/µg), and superoxide dismutase (5546 nmol/µg) activities were lower in treated plants compared to untreated ones (6982 nmol/µg for catalase, 9635 nmol/µg for peroxidase, and 10,403 nmol/µg for superoxide dismutase). However, given the higher levels of photosynthetic pigments observed in treated plants, antioxidant activity may be primarily attributed to non-enzymatic mechanisms, such as carotenoids, which were also elevated relative to controls. These findings demonstrate the potential of the organomineral as a tool for the physiological management of agriculturally important crops, particularly black mung bean. In this context, its adoption holds the potential to significantly improve agricultural productivity and crop resilience.

Keywords

Vigna radiata; chlorophyll; antioxidant system; physiological management

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