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

J

Jaelani, Lalu Muhamad, Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember (Indonesia)
Ji, Qin, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environmental and Resources, CAS (China)
Jia, Xiaofeng, Chinese Academy of Agricultural Mechanization Sciences Group Co.，Ltd.; State Key Laboratory of Soil Plant Machinery System Technology (China)
Jiang, Yue, Jiangsu University the Research Center for Fluid Mechanical Engineering Technology (China)
Jiao, Bo, Chinese Academy of Agricultural Mechanization Sciences (China)
Jin, Meijuan, Suzhou Academy of Agricultural Sciences;National Agricultural Experimental Station for Soil Quality (China)
Josselyn, Moreno-Quinto, Faculty of Natural Sciences and Agriculture, Universidad Estatal del Sur de Manabí (UNESUM) (Ecuador)
Julio, Gabriel-Ortega, Faculty of Natural Sciences and Agriculture, Universidad Estatal del Sur de Manabí (UNESUM) (Ecuador)

K

Kadah, Taher, Central Laboratory of Residue analyses of Pesticides and Heavy Metals in Food (QCAP), Agricultural Research Center (ARC) (Egypt)
Karamvand, Mojtaba Abdi, Applied Research Center, Debel Khozaei Agro-industrial Co. (Iran)
Karen, Quijije-Quiroz, Faculty of Natural Sciences and Agriculture, Universidad Estatal del Sur de Manabí (UNESUM) (Ecuador)
Kaur, Jagjeet, Department of Climate Change and Agricultural Meteorology, Punjab Agricultural University (India)
Kaur, Prabhjyot, Department of Climate Change and Agricultural Meteorology, Punjab Agricultural University (India)
Kaur, Prabhjyot-, Department of Climate Change and Agricultural Meteorology, Punjab Agricultural University (India)
Khan, Mahmudul Hasan, Oilseed & Legume Breeding, Oilseed Research Centre, Regional Agricultural Research Station (RARS), Bangladesh Agricultural Research Institute (BARI) (Bangladesh)
Khan, Urooj, MBBS, Khyber Medical College (KMC) (Pakistan)
Kizito, Simon Savio, Department of Forestry, Biodiversity and Tourism, College of Agricultural and Environmental Sciences, Makerere University (Uganda)
Kong, Lingwei, College of Resources and Environmental Sciences, Qingdao Agricultural University (China)
Kong, Xiaoping, Xining Vegetable Technology Service Center (China)
Krishna, Agatambidi Bala, Department of Climate Change and Agricultural Meteorology, Punjab Agricultural University (India)
Krishnamurthy, Laksmi Reddiar, Agroforestry Center for Sustainable Development, Autonomous University of Chapingo (Mexico)
Krishnamurthy, Sumithra, University of Sussex, Sussex House, Falmer Brighton (United Kingdom)
Kundu, B. C., Varietal Improvement Program, Horticulture Research Centre, RARS (Bangladesh)
Kusoiry, Moch Rafli, Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember (Indonesia)

L

Landini, F, River Plate Basin University, The National Scientific and Technical Research Council (Argentina)

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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

- - - ＜5 days from submission to initial review decision;
  - 64% acceptance rate

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...

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