Browse Author Index

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.

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All

S

Shary, P. A., All-Russian Research Institute of Agrochemistry named after D.N. Pryanishnikov; Institute of Physicochemical and Biological Problems in Soil Science RAS (Russia)
Shen, Mingxing, Suzhou Country Cadre Institute (China)
Shen, Yuan, Suzhou Academy of Agricultural Sciences;National Agricultural Experimental Station for Soil Quality (China)
Shi, Linlin, Suzhou Academy of Agricultural Sciences;National Agricultural Experimental Station for Soil Quality (China)
Shrestha, Shobha, Central Departments of Geography, Tribhuvan University (Nepal)
Sinh, Sreytoch, Faculty of Agricultural Biosystems Engineering, Royal University of Agriculture (Cambodia)
Solare, Arturo Peralta, Agroforestry Center for Sustainable Development, Autonomous University of Chapingo (Mexico)
Song, Cheng, Chinese Academy of Agricultural Mechanization Sciences (China)
Song, Jiannong, College of Engineering, China Agricultural University; Key Laboratory of Soil-Machine-Plant System Technology, Ministry of Agriculture and Rural Affairs (China)
Song, Jiaxing, Beijing Research Center of Intelligent Equipment for Agriculture; National Research Center of Intelligent Equipment for Agriculture; National Center for International Research on Agricultural Aerial Application Technology; Beijing Engineering Laborator (China)
Su, Jiajia, Hohhot Branch of Chinese Academy of Agricultural Mechanization Sciences Co.，Ltd. (China)
Su, Renzhong, Center for Agriculture Mechanization Technology Extension of Hubei Province (China)
Sun, Meili, College of Resources and Environmental Sciences, Qingdao Agricultural University (China)
Sun, Xiaoqin, College of Agricultural Science and Engineering, Hohai University (China)

T

Taha, Hanan Salah El-Din, Central Agricultural Pesticide Laboratory, Agricultural Research Center (Egypt)
Tang, Qing, Beijing Research Center of Intelligent Equipment for Agriculture; National Research Center of Intelligent Equipment for Agriculture; National Center for International Research on Agricultural Aerial Application Technology; Beijing Engineering Laborator (China)
Tao, Yueyue, Suzhou Academy of Agricultural Sciences;National Agricultural Experimental Station for Soil Quality (China)
Teng, Yingzi, Beijing Key Laboratory of New Technology in Agricultural Application/National Demonstration Center for Experimental Plant Production Education/College of Plant Science and Technology, Beijing University of Agriculture (China)
Thy, Sopheabunnarith, Faculty of Agricultural Biosystems Engineering, Royal University of Agriculture (Cambodia)
Tim-Ashama, A. C., Department of Agricultural Science, Alvan Ikoku Federal College of Education (Nigeria)
To, Thi Tuyet, Basic Faculty, Ho Chi Minh City Central Pedagogical College (Viet Nam)
Toca, Cosme E. Santiesteban, Centro de investigación de Bioplantas, Universidad de Ciego de Ávila (Cuba)
Toro-Sánchez, Carmen Lizette Del, Department of Research and Graduate Studies in Food, University of Sonora (Mexico)
Toshpulatova, Gulnoza, Department of biology, National University of Uzbekistan named after Mirzo Ulugbek (Uzbekistan)
Tran, Thi Ngoc Anh, Department of Biology, Faculty of Science, Nong Nam University (Viet Nam)

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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

Indexing & Archiving

News & Announcements

2025-02-01

Highlight article: Innovations in Modern Agriculture: UAVs and CNNs Transform Cotton Water Stress Monitoring

In the realm of modern agriculture, the integration of cutting-edge technologies is revolutionizing the way we approach sustainable farming practices. A recent study published in Advances in Modern Agriculture titled "Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery" has garnered significant attention for its innovative approach to precision irrigation management. Conducted by researchers from Institute of Data Science and the AgriLife Research and Extension Center of Texas A&M University (authors's information is below). This study introduces a novel method for classifying cotton water stress using unmanned aerial vehicles (UAVs) and convolutional neural networks (CNNs), offering a powerful solution for optimizing water use in agriculture.

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.

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