Open Journal Systems

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.

Study on health evaluation model of modern irrigation district project based on AHP-FCE

Yao Zhang, Huaizhu Yao, Kai Fang, Jihui Ding, Jiahua Zhou, Shuangen Yu, Xiaoqin Sun

Article ID: 2049
Vol 3, Issue 2, 2022

DOI: https://doi.org/10.54517/ama.v3i2.2049
VIEWS - 5435 (Abstract)

Download PDF

Abstract

Objective: The modernization of irrigation areas is an important guarantee of national food security. Make clear the health status of irrigation projects, put forward the renovation plan for modern irrigation areas, guide the construction of modern irrigation areas, and promote agricultural modernization. Method: Taking Lailong Irrigation District in Jiangsu Province as an example, the health evaluation index system of key projects in the irrigation district was constructed, and the weight of each evaluation index was calculated by AHP and a fuzzy comprehensive evaluation model (AHP-FCE) to evaluate the health status of key projects. Results: The proportions of “good”, “medium” and “poor” in the comprehensive evaluation of the health status of the pump station project and the sluice project in Lailong Irrigation District were 16.72%, 53.33%, 30%, 13.51%, 48.65%, and 37.84%, respectively. In the evaluation of the criterion level, the proportion of “poor” information level evaluation level reached 63.33% and 64.86%. This paper analyzes the key problems and weak links of the modernization of the irrigation project in the Lailong irrigation area and puts forward the requirements for the modernization of the Lailong irrigation area according to the basic characteristics of the modern irrigation area. Conclusion: Water-saving reconstruction projects in irrigated areas basically meet the requirements of irrigation and drainage, but information technology has not been applied in the reconstruction of irrigated areas, and the construction of modern irrigated areas should pay more attention to information construction. This paper provides a theoretical basis and technical support for the modernization of irrigation areas in Jiangsu Province.

Keywords

modern irrigation area; AHP-FCE evaluation model; assessment of health status; retrofit scheme

References

1. Zhang F. Research on ERP Selection of Construction Enterprises Based on AHP-Grey Fuzzy Evaluation (Chinese) [Master’s thesis]. Beijing Jiaotong University; 2012.

2. Li F, Jia P, Zhang Y, Qi Q. Study on evaluation index system and evaluation method of regional water resources sustainable use (Chinese). Water Conservancy Science and Technology and Economy 2007; 11: 826–828.

3. Yang L, Wang N, Xie J, Jing X. Optimization of eco-environmental evaluation index in irrigation area based on multi-source information fusion decision (Chinese). Transactions of the Chinese Society of Agricultural Engineering 2015; 31(14): 225–231.

4. Li L, Yu J, Li H, et al. Study on comprehensive evaluation system of modernization of irrigation area based on AHP: A case study of Dongfengqu Irrigation area in Hubei Province (Chinese). Water Saving Irrigation 2020; 10 :96–100.

5. Chen J, Tang D. Evaluation of water demand in Tailan River irrigation area based on ANP-FCE method (Chinese). China Rural Water and Hydropower 2014; 3: 159–161+165.

6. Wang D. Study on Structure Composition Analysis and Modernization Evaluation of Large-Scale Irrigation Areas in China (Chinese) [Master’s thesis]. Hebei Agricultural University; 2014.

7. Xu X, Yang L, Wu Q, et al. Research on evaluation method of modern irrigation area based on variable fuzzy set (Chinese). China Rural Water and Hydropower 2018; 6: 128–132.

8. Wang X, Yuan X, Shi S. Situation and countermeasures of water saving and emission reduction in southern China: A case study of Jiangxi Province (Chinese). Yangtze River 2016; 47(S1): 9–12.

Refbacks

There are currently no refbacks.

This work is licensed under a Creative Commons Attribution 4.0 International License.

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

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)

- - - ＜7 days: submission to initial review decision;
  - - 56 days: received to online

News & Announcements

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.

2024-05-01

Notice on the prohibition of using AI techniques to generate papers!

This journal values human initiative and intelligence, and the employment of AI technologies to write papers that replace the human mind is expressly prohibited. When there is a suspicious submission that uses AI tools to quickly piece together and generate research results, the editorial board of the journal will reject the article, and all journals under the publisher's umbrella will prohibit all authors from submitting their articles.

Readers and authors are asked to exercise caution and strictly adhere to the journal's policy regarding the usage of Artificial Intelligence Generated Content (AIGC) tools.

2024-01-14

We are recruiting Editorial Team Members

Advances in Modern Agriculture (AMA) is looking for members of the Editorial Board.

2023-12-28

Notice on the change of publication frequency

The publication frequency of Advances in Modern Agriculture is Quarterly since 2024, that means the journal will publish 4 issues per year from 2024 onwards.

More Announcements...

Journal Center

Asia Pacific Academy of Science Pte. Ltd. (APACSCI) specializes in international journal publishing. APACSCI adopts the open access publishing model and provides an important communication bridge for academic groups whose interest fields include engineering, technology, medicine, computer, mathematics, agriculture and forestry, and environment.

more

Article Tools

Indexing metadata

How to cite item

Review policy