Editorial for Advances in Modern Agriculture (Volume 4, Issue 1)

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.

Editorial for Advances in Modern Agriculture (Volume 4, Issue 1)

Riaz Ahmad

Article ID: 2703
Vol 4, Issue 1, 2023

DOI: https://doi.org/10.54517/ama.v4i1.2703
VIEWS - 2099 (Abstract)

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Abstract

As a technology-intensive sector, modern agriculture is becoming more and more dependent on the ongoing development of new technological inputs, which serve as both its foundation and its engine. These new technologies include biotechnology, information technology, cultivation technology, water-saving irrigation technology, etc. The application of agricultural technologies has many advantages: firstly, it can increase the yield of agricultural products per unit; secondly, it can improve the quality of agricultural products; thirdly, it can reduce labor intensity; and fourthly, it can save energy and improve the ecological environment. Agriculture has developed quickly as a result of those new technology, which is quite advantageous to the general public and practitioners that are involved. This issue offers readers a discussion of the implications of numerous new technologies being applied to various connections in agriculture, along with interesting facts about these applications. Those findings suggest that appropriate use of modern technologies in agriculture could provide economic benefits without endangering the natural environment. More specifically, the use of irrigation technology, the promotion of crop growth, and the impact of technology on agriculture are the three key topics of this issue.

References

1. Liu J, Wan J, Li H, Jiang Y. Distribution of water droplets of non-circular sprinkler nozzles. Advances in Modern Agriculture 2023; 4(1): 2057. doi: 10.54517/ama.v4i1.2057

2. Maldonado EA, Barreiro LEC, Toca CES. Intelligent fertigation, pillar of sustainable agriculture. Advances in Modern Agriculture 2023; 4(1): 2017. doi: 10.54517/ama.v4i1.2017

3. Yi X, Cheng L. Research on the influence of Internet technology popularization on the green development of agriculture in the Yangtze River Economic Belt. Advances in Modern Agriculture 2023; 4(1): 2081. doi: 10.54517/ama.v4i1.2081

4. Cuéllar Saavedra JE. Transgenic agriculture. A bioethical assessment of the Colombian case. Advances in Modern Agriculture 2023; 4(1): 2092. doi: 10.54517/ama.v4i1.2092

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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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2024-09-11

Smart agriculture is becoming a reality!

Modern agricultural technology is evolving rapidly, with scientists collaborating with leading agricultural enterprises to develop intelligent management practices. These practices utilize advanced systems that provide tailored fertilization and treatment options for large-scale land management.

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