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Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery

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.

A novel, validated, eco-friendly, and cost-effective rp-HPLC method for the determination of alginic acid content in seaweed, seaweed formulations, and other higher plant extracts

Tanaji Shivaji Bhagat

Article ID: 2390
Vol 4, Issue 1, 2023

DOI: https://doi.org/10.54517/ama.v4i1.2390
VIEWS - 66 (Abstract)

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Abstract

Alginic acid products are widely used in all fields, and the main source of alginic acid is seaweed. Alginic acid varies in various varieties of seaweed, plant parts, season to season, and the temperature of the water in which it is grown. The content of alginic acid also varies from manufacturer to manufacturer, as it depends on the process of extraction. No full-proof method for quantification is available. As alginic acid became a point of concern for regulators, a robust method for quantifying alginic acid became a prime requirement. As seaweed is only considered a source of alginic acid, essential amino acids, and nonessential amino acids, other sources need to be identified to overcome future needs and maintain biodiversity. The author succeeds in identifying another source of alginic acid, nutrients, and amino acids, i.e., Chromolaena odorata leaf extract, which contains about 5 to 6% alginic acid and 30 to 40% amino acids and proteins. A novel RP-HPLC method was found to be simple, suitable, and robust for the determination of alginic acid content in seaweed extract, its formulations, and also in phytoextracts (Chromolaena odorata leaf extract). The alginic acid content in seaweed and the formulated product quantified shows 10.05 ± 0.1% and 1.0 ± 0.03%, respectively, and is in close agreement with the declared content. Various authors have used gravimetric, colorimetric, titrimetric, and HPLC methods, each with their own limitations. The novel method is simple in terms of sensitivity, eco-friendly mobile phase, regular HPLC column and detector, lowest retention time (rapid analysis), and no internal standard required.

Keywords

alginic acid; seaweed; Chromolaena odorata; novel RP-HPLC method; essential and nones-sential amino acids; process of extraction g.

References

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Editor-in-Chief

Prof. Zhengjun Qiu

Zhejiang University, China

Honorary Editor-in-Chief

Cheng Sun

Academician of World Academy of Productivity Science; Executive Chairman, World Confederation of Productivity Science China Chapter, China

News & Announcements

2024-09-11

Smart agriculture is becoming a reality!

Modern agricultural technology is evolving rapidly, with scientists collaborating with leading agricultural enterprises to develop intelligent management practices. These practices utilize advanced systems that provide tailored fertilization and treatment options for large-scale land management.

2024-05-01

Notice on the prohibition of using AI techniques to generate papers!

This journal values human initiative and intelligence, and the employment of AI technologies to write papers that replace the human mind is expressly prohibited. When there is a suspicious submission that uses AI tools to quickly piece together and generate research results, the editorial board of the journal will reject the article, and all journals under the publisher's umbrella will prohibit all authors from submitting their articles.

Readers and authors are asked to exercise caution and strictly adhere to the journal's policy regarding the usage of Artificial Intelligence Generated Content (AIGC) tools.

2024-01-14

We are recruiting Editorial Team Members

Advances in Modern Agriculture (AMA) is looking for members of the Editorial Board.

2023-12-28

Notice on the change of publication frequency

The publication frequency of Advances in Modern Agriculture is Quarterly since 2024, that means the journal will publish 4 issues per year from 2024 onwards.

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