Towards high-yield and sustainable agriculture

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.

Towards high-yield and sustainable agriculture

Gideon Oron

Article ID: 2697
Vol 4, Issue 2, 2023

DOI: https://doi.org/10.54517/ama.v4i2.2697
VIEWS - 835 (Abstract)

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Abstract

The key challenges for researchers in agriculture today may be how to guarantee the quality of various crops and how to efficiently increase agricultural yields. Volume 4, Issue 2, of this open access journal, Advances in Modern Agriculture, which features 12 excellent articles, primarily offers readers an engaging conversation about how to increase agricultural productivity through the use of various research approaches from multiple perspectives. In the meantime, insightful recommendations regarding how to enhance crop productivity are made, offering researchers working in related fields helpful knowledge and illumination.

References

1. Osuji EE, Tim-Ashama AC, Agunanne UT, et al. Understanding factors intercepting response of rice farmers to climate change in Ebonyi State, Nigeria. Advances in Modern Agriculture 2023; 4(2): 2285. doi: 10.54517/ama.v4i2.2285

2. Wikipedia. Fertilizer (Chinese). Available online: https://zh.wikipedia.org/wiki/%E8%82%A5%E6%96%99 (accessed on 10 June 2023).

3. Amanullah A, Khan U. Advancing sustainable agriculture with beneficial microbes: Enhancing crop growth and yield for food security and human health. Advances in Modern Agriculture 2023; 4(2): 2426. doi: 10.54517/ama.v4i2.2426

4. Mutetwa M, Mtaita T, Ngezimana W, Chaibva P. Growth of horned melon (Cucumis metuliferus) under different levels of Trichoderma-based bio-fertilizer. Advances in Modern Agriculture 2023; 4(2): 2367. doi: 10.54517/ama.v4i2.2367

5. Rao GBN. Applications of artificial intelligence in precision agriculture to ameliorate production and distribution. Advances in Modern Agriculture 2023; 4(2): 2374. doi: 10.54517/ama.v4i2.2374

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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

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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.

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