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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.

Gradient throwing characteristics of oscillating slat shovel for rhizome crop harvesters

Lipengcheng Wan, Yonglei Li, Hu Zhao, Guanghao Xu, Jiannong Song, Xiangqian Dong, Chao Zhang, Jicheng Wang

Article ID: 2047
Vol 3, Issue 1, 2022

DOI: https://doi.org/10.54517/ama.v3i1.2047
VIEWS - 3512 (Abstract)

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Abstract

An oscillating slat shovel has presented a promising application potential in the energy-saving and efficient harvesting of deep rhizome crops. This new type of shovel slat integrated harvesting device was developed using gradient amplitude and gradient vibration technology. This study aims to clarify the working characteristics of oscillating slat shovels and the mechanism of throwing separation. The throwing coefficient was selected to characterize the throwing separation ability of the slat shovel work plane. A motion analysis was made to calculate the swing acceleration of the slat shovel work plane. An analytical equation of the throwing coefficient was then established to combine with the working process, the periodic variation of the throwing coefficient, and the influence of parameters, including the amplitude, vibration frequency, and working length. The results showed that the throwing coefficient gradually increased at each point of the slat shovel work plane, indicating outstanding gradient throwing characteristics and strong throwing ability. The maximum throwing coefficient was 9.98–19.72 in the separation area. After that, an investigation was made to determine the influence of the structure and working parameters of the oscillating slat shovel on the soil-throwing separation performance. The EDEM-MBD coupling simulation model of the single pendulum shovel gate was established to simplify the structural model and the interaction between the rhizome, soil, and working components, where the indicators were set as the traction resistance, driving torque, the maximum separation distance between the soil and the slat (separation distance), and the ratio of the separated soil quality of each functional area of the work-plane to the total soil mass (separation ratio). A single-factor test was carried out with the amplitude, vibration frequency, and forward speed as factors. The results indicated that: 1) There were outstanding strong-weak cycles in the traction resistance and driving torque due to the gradient throwing characteristics of the oscillating slat shovel, soil viscosity, and plasticity. In the strong period, there was a large interaction force between the shovel slat and soil, where the maximum separation distance occurred at the middle point of the separation area at the endpoint of the cutting stroke. 2) The amplitude was negatively correlated to the traction resistance but positively correlated with the driving torque and separation distance. The vibration frequency was negatively correlated to the traction resistance, driving torque, and separation spacing. The forward velocity was positively correlated to traction resistance and driving torque but negatively correlated to the separation distance. 3) There was a small influence of amplitude and vibration frequency on the separation ratio. There was a low separation of oscillating slat shovels with the increase in forward velocity. 4) A combination of parameters was achieved when the amplitude was 7–11 mm in the strong period, where the average traction resistance was about 1580.93–2019.9 N, the maximum driving torque was about 224.04–322.11 N·m, and the maximum separation distance was about 59.58–98.3 mm. 5) The average traction resistance was about 1416.43–1866.38 N, the maximum driving torque was about 315.28–364.19 N·m, and the maximum separation distance was about 78.43–94.67 mm when the vibration frequency was 6.67–10.67 Hz. 6) The average traction resistance was about 1429.43–2110.48 N, the maximum driving torque was about 241.27–387.78 N·m, and the maximum separation distance was about 62.5–102.5 mm when the forward speed was 0.2–0.4 m/s. An optimal combination of parameters was selected for the field experiment: the amplitude was 9 mm, the vibration frequency was 9.4 Hz, and the working speed was 0.32 m/s. The licorice harvesting test indicated that the traction resistance was about 32.17 kN, the driving torque was about 802.02 N·m, the excavation depth was about 468 mm, and the cleaning rate was about 96.42%. Consequently, the oscillating slat shovel harvesting device can be feasible for smooth and orderly operation as well as the higher separation performance of rhizome-soil, where all the operation indexes meet the national standards. This finding can provide a new method and design reference for the energy-saving and efficient harvesting of rhizomes, especially deep rhizome crops.

Keywords

harvest; simulation; rhizome; oscillating slat shovel; gradient throwing

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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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