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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.

Exploring the antifungal potential of Allium sativum and Ocimum gratissimum against post-harvest fungal pathogens in mango fruits

Okon Godwin Okon, Uwaidem Yakubu Ismaila, Ukponobong Efiong Antia, Muhammad Saqlain Zaheer, Hafiz Haider Ali, Abdelhak Rhouma

Article ID: 2363
Vol 4, Issue 2, 2023

DOI: https://doi.org/10.54517/ama.v4i2.2363
VIEWS - 5653 (Abstract)

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Abstract

Post-harvest spoilage of fruits and vegetables caused by fungal pathogens is a serious challenge to fruit production in many parts of the world. The study was conducted to evaluate the sensitivity of fungal pathogens associated with post-harvest rot of mango fruits to crude extracts from two edible plants, Allium sativum and Ocimum gratissimum, in the study area. Five different fungal isolates were isolated from diseased mango fruits collected from fruit stores in the study area and identified as Aspergillus spp. (M₁), Rhizopus spp. (M₂), Fusarium spp. (M₃), Penicillium spp. (M₄), Fusarium spp. (M₅), Penicillium spp. (M₆), Aspergillus spp. (M₇), and Colletotrichum spp. (M₈) using radial growth rate and morphological features of the mycelia. A constant concentration of each of the crude extracts was applied to the growth media containing the growing cultures of the fungal isolates. The radial extension of the colonies for each isolate was measured along pre-marked perpendicular axes on the base of the petri dish after 24 h, and this continued for 10–14 days. It was observed that Rhizopus spp., Fusarium spp., Penicillium spp., and Colletotrichum spp. had the least growth rate when treated with the extracts.

Keywords

Allium sativum; Ocimum gratissimum; fungal pathogens; mango fruits

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Copyright (c) 2023 Okon Godwin Okon, Uwaidem Yakubu Ismaila, Ukponobong Efiong Antia, Muhammad Saqlain Zaheer, Hafiz Haider Ali, Abdelhak Rhouma

License URL: https://creativecommons.org/licenses/by/4.0/

This site is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

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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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