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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.

International competition situation and research focus on Internet of Things for agricultural equipment

Jianxia Yuan, Qiuju Zhang, Xiaolu Hu, Zhonghua Miao, Zhenbo Wei, Long Qi, Haihua Wu

Article ID: 2086
Vol 2, Issue 2, 2021

DOI: https://doi.org/10.54517/ama.v2i2.2086
VIEWS - 1536 (Abstract)

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Abstract

Taking SCI papers related to the Internet of Things for Agricultural Equipment Research as the object, comprehensively using bibliometric methods, the paper content analysis methods and expert consultation, the methods, through analyses of paper output trends, hot research topics, leading countries, key research contents, etc. The development trend, international competition situation, and hotspot directions of the Internet of Things for agricultural equipment were revealed, with a view to providing decision support for optimizing research layout and project management.

Keywords

agricultural equipment; Internet of Things; smart agriculture; sensor; SCI papers

References

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2. Zhao G. Analysis of agricultural Internet of Things technology to improve the transformation of agricultural mechanization to digital (Chinese). Agricultural Engineering Technology 2020; 40(33): 47–48.

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10. Li Z, Du X, Mao T, et al. Pig dimension detection system based on depth image. Transactions of the Chinese Society of Agricultural Machinery 2016; 47(3) : 311–318．

11. Liu L, Shen M, BO G, et al. Sows parturition detection method based on machine vision. Transactions of the Chinese Society of Agricultural Machinery 2014; 45(3): 237–242.

12. Zhao K, He D, Wang E. Detection of breathing rate and abnormity of dairy cattle based on video analysis. Transactions of the Chinese Society of Agricultural Machinery 2014; 45(10): 258–263.

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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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