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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.

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 - 942 (Abstract)

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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 a randomized complete block design (RCBD) design with three replications. Analysis of variance demonstrated 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 seed weight, and yield kg per hectare had a 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 examines the positive and highly significant association among the yield and its related traits. Principal component analysis (PCA) revealed a total proportion of variation of 54.86% for PC1 and 16.89% for PC2. The genotypes BC-100614(8)–7 gave a 7.20% higher yield than 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 programs.

Keywords

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

References

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Copyright (c) 2023 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

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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

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