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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.

Screening of Vigna subterranean L. Verdc. accessions for waterlogging stress tolerance

Okon Godwin Okon, Augustine Effiong Archibong, Ofonime Raphael Akata, Imikan Anyieokpon Nyong, Ekomobong Etinam Akpan

Article ID: 2365
Vol 4, Issue 2, 2023

DOI: https://doi.org/10.54517/ama.v4i2.2365
VIEWS - 2555 (Abstract)

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Abstract

This study evaluated the influence of waterlogging stress on the growth of six (6) accessions (TvSu-1, TvSu-2, TvSu-3, TvSu-4, TvSu-5, and TvSu-10) of Vigna subterranean in a pot experiment. The experiment was setup in a complete block design (CBD) with 3 replicates per treatment. Results of growth parameters of V. subterranean accessions under waterlogging stress, such as plant height, leaf area, petiole length, and number of nodes, were significantly (p = 0.05) decreased when compared to their controls after 8 weeks of planting. For shoot length, TvSu-2 (1.60 ± 0.20 cm) and TvSu-4 (1.60 ± 0.20 cm) recorded the highest values, while TvSu-5 (1.23 ± 0.03) and TvSu-10 (1.23 ± 0.03) had the lowest values, respectively. TvSu-5 (19.03 ± 0.59 cm²) and TvSu-10 (19.03 ± 0.59 cm²) recorded the highest values in leaf area (LA), while TvSu-3 (14.40 ± 0.51 cm²) had the lowest LA. For total photosynthetic pigment (TPP), TvSu-2, TvSu-4, and TvSu-10 had the highest values, with 57.35 ± 1.82 mg/kg, 55.80 ± 2.70 mg/kg, and 55.77 ± 1.90 mg/kg, respectively. TvSu-3 (41.50 ± 8.29 mg/kg) maintained the lowest value. In petiole length, TvSu-5 (15.23 ± 0.33 cm) and TvSu-4 (14.20 ± 0.66cm) had the highest values, while TvSu-3 (8.97 ± 0.33 cm) had the lowest. For the number of nodes, TvSu-2 (15.00 ± 1.76) and TvSu-4 (12.00 ± 1.73) recorded the highest values, while TvSu-10 (10.00 ± 1.00) had the lowest value. Biomass yield analysis of the stressed V. subterranean showed that total fresh weight (TFW), root length (RL), root fresh weight (RFW), shoot fresh weight (SFW), leaf fresh weight (LFW), leaf turgid weight (LTW), total dry weight (TDW), root dry weight (RDW), shoot dry weight (SDW), and leaf dry weight (LDW) of the six accessions were significantly (p = 0.05) decreased when compared to their control. Tvsu-5 had a better biomass yield when compared to other accessions, recording the highest values in SDW (1.62 g), RDW (0.55 g), LFW (0.75 g), SFW (10.31 g), and RL (18.53 ± 0.66). Conclusively, waterlogging stress negatively impacted V. subterranean accessions, but Tvsu-5 had a better waterlogging stress tolerance than other accessions, especially TvSu-3, which was generally poor.

Keywords

Bambara groundnut; cowpea; stress tolerance; Vigna subterranean; waterlogging

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Copyright (c) 2023 Okon Godwin Okon, Augustine Effiong Archibong, Ofonime Raphael Akata, Imikan Anyieokpon Nyong, Ekomobong Etinam Akpan

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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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