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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.

Development and evaluation of smart irrigation system to enhance the water use efficiency

Masoud Pourgholam-Amiji, Abdolmajid Liaghat, Farnaz Nozari

Article ID: 2359
Vol 4, Issue 2, 2023

DOI: https://doi.org/10.54517/ama.v4i2.2359
VIEWS - 972 (Abstract)

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Abstract

For this purpose, a moisture sensor device was designed and constructed in February and March 2019 to determine the appropriate time to stop irrigation in furrow irrigation. Testing the device in the laboratory and its application in the Farm of the Campus of Agriculture and Natural Resources, University of Tehran (Mohammad Shahr), Iran, from April to July 2019. The purpose of this study was to evaluate the performance of a smart sensor of soil moisture to determine the optimum depth of installation and recording of soil moisture at 10, 30, and 50 cm depths and different length ratios in furrow irrigation. Initially, calibration of the device was carried out on field soil, and based on the obtained validation, the device was transferred to the field. To achieve the goals of optimum depth of installation and optimum length, 36-meter furrows with a distance of 0.75 m were created in the field. Sensitive lengths in furrows with 0.5 L, 0.75 L, and 0.85 L ratios were selected as the starting points. The results showed that in the calibration and validation phases, the R² values were 0.93 and 0.95, respectively, and in the calibration and validation stages, the value of nRMSE was 80 and 13.81%, indicating good model training in the calibration stage. Also, the average RE parameter in estimating soil moisture was 2.74%, indicating the high accuracy of the device in estimating soil moisture. The results also showed that if the device was installed at a depth of 30 cm from the soil surface of the furrow and at 75% from the beginning of the field, the depth and runoff losses would be minimal and irrigation adequacy would be best compared to other depths and lengths. It is expected that with optimal water consumption and timely interruption of irrigation, deep losses and runoff will be avoided, and with low water consumption, the productivity of crops will increase.

Keywords

surface irrigation; deep percolation; sensor; smart irrigation; calibration and validation

References

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Copyright (c) 2023 Masoud Pourgholam-Amiji, Abdolmajid Liaghat, Farnaz Nozari

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

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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

Processing Speed (2023)

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  - - 56 days: received to online

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2024-09-11

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