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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.

Comprehensive evaluation of modern agricultural development level in contiguous destitute area of Lvliang Mountain, Shanxi Province

Qin Ji, Jianping Yang, Manhou Xu

Article ID: 1989
Vol 1, Issue 1, 2020

DOI: https://doi.org/10.54517/ama.v1i1.1989
VIEWS - 6886 (Abstract)

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Abstract

An evaluation model was established using a combination of AHP and multi-objective linear weighted function methods to comprehensively evaluate the development level of modern agriculture in the contiguous poverty-stricken areas of Lvliang Mountain in Shanxi Province. The results show that: (1) As a whole, the contiguous poverty-stricken areas have a low degree of agricultural modernization and are in the initial stage of development. The degree of modernization of agricultural production conditions, agricultural production results, and farmers’ living standards is gradually decreasing; at the municipal level, the degree of agricultural modernization in the three cities is the same. Not high, the order is Xinzhou City > Linfen City > Lvliang City; at the county level, the degree of agricultural modernization varies greatly. The counties in Xinzhou City are in the high-level sub-stage of the initial stage, and the counties in Lvliang City are in the low-level sub-stage of the initial stage. The overall degree of agricultural modernization in counties shows a spatial pattern of low in the middle and high in the north and south; (2) The contributions of agricultural production conditions, production results and farmers’ living standards to modern agriculture are 16.4%, 29.7% and 53.9% respectively. The low level of agricultural electrification and mechanization and backward production conditions are bottlenecks in the development of modern agriculture. The backward agricultural production conditions lead to inefficient production results, low disposable income of farmers, and low living standards of farmers, which are the root causes of the slow development of modern agriculture in the region.

Keywords

modern agriculture; degree of modernization; Lvliang Mountain contiguous destitute area; Shanxi Province

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Zhejiang University, China

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Academician of World Academy of Productivity Science; Executive Chairman, World Confederation of Productivity Science China Chapter, China

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