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Featured Articles

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.

Yield potency assessment and characters association of promising lines of mustard (Brassica rapa L.) in southern region of Bangladesh

Mahmudul Hasan Khan, B. C. Kundu, M. Shalim Uddin, M. Rafiqul Islam, M. Aktar-Uz-Zaman, M. Ahmed, Razi Uddin, M. Rashedul Islam, A. K. Das, M. G. Rahman

Article ID: 2216
Vol 4, Issue 2, 2023

DOI: https://doi.org/10.54517/ama.v4i2.2216
VIEWS - 91 (Abstract)

Abstract

Mustard (Brassica spp.) is a major oilseed crop and the world’s third largest source of vegetable oil. During the rabi season of 2022–2023, the experiment was carried out at the Regional Agricultural Research Station, Bangladesh Agricultural Research Institute (BARI), Barishal to evaluate the performance of 12 genotypes of Brassica rapa L., including the check variety BARI Sarisha-14. The experiment was conducted under randomized complete block design (RCBD) design with three replications. Analysis of variance demonstrated that a highly significant (p ≤ 0.01) differences for the traits viz., plant height, no. of primary branches, no. of secondary branches, and no. of seeds per siliqua whereas no. of siliqua per plant, 1000 seeds weight, yield kg per hectare had significant difference at p ≤ 0.05. Among the examined accessions, the unweighted pair group method with arithmetic mean (UPGMA) cluster analysis identified four different clusters. Cluster II (28.91%) with only one accession had the highest average yield, followed by Cluster I (26.25%) and Cluster IV (22.68%) with the best agronomic features. Furthermore, we observed 1.6% and 11.93% greater (+) mean yield compared to the grand mean yield (1502.33 kg ha⁻¹) for cluster I and cluster II, respectively. Correlation analysis examine the positive and highly significant association among the yield with its related traits. Principal component analysis (PCA) revealed the total proportion of variation 54.86% for PC1 and 16.89% for PC2. The genotypes BC-100614(8)-7 gave 7.20% higher yield over the check variety. Considering the statistical results, maturity period, yield and yield contributing characters of Brassica rapa L. genotypes BC-100614(8)-7 (1681 kg ha⁻¹), BC-100614(8)-1 (1594 kg ha⁻¹) and BC-100614(7)-3 (1565 kg ha⁻¹) were noted as promising lines among the evaluated genotypes and suggested for future breeding program.

Keywords

Brassica rapa L.; yield potency; phenotypic association; PCA analysis; promising breeding lines; trait selection

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References

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University of the Balearic Islands Spain

News & Announcements

2023-08-22

New version of Author Guidelines are update

Please follow the journal's author guideline and the required article template to prepare your manuscript

2022-09-10

The 2022 World Food Forum will be held in Italy

The 2022 World Food Forum (WFF) flagship event will take place from 17 to 21 October at the Headquarters of the Food and Agriculture Organization of the United Nations (FAO) in Rome, Italy and virtually.

2022-06-07

Italian researchers are protesting a law that may equate “biodynamic” methods with organic agriculture

One line in the draft law concerns scientists above all. Article I states that “for the purposes of this law, the biodynamic agriculture method […], when applied in compliance with the European Union’s dispositions on organic farming, is equated with organic farming”. A few articles later, the law requires one representative of biodynamic farmers to sit in a technical committee that will oversee the application of the law, including allocations of public funds.

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Deadline: 2024-12-31

Food Science

Keywords: food science; functional foods; food ingredients; nutrient fortification; crop biomass; sustainable food; processing technologies; cross-disciplinary approach

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Sustainability of modern agriculture

Keywords: sustainable agriculture, objectives of sustainable farming, involving devices, involving technology, pros and cons, etc.

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Technology in modern agriculture

Keywords: the agricultural device, the technology used in farming different plants, the impact of technology on agriculture, the evolution of agricultural technology, robotic systems, etc.