资源受限环境下可持续农业系统的预测性优化水与能源灌溉(POWEIr)控制器调度的敏感性研究

IF 7.7 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Computers and Electronics in Agriculture Pub Date : 2024-09-10 DOI:10.1016/j.compag.2024.109230
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引用次数: 0

摘要

在保护地球有限自然资源的同时,满足不断增长的全球人口日益增长的粮食需求势在必行。对于居住在中低收入国家(LMICs)资源有限的农民来说,这一挑战变得更加紧迫,因为他们在粮食不安全问题上首当其冲。针对这一关键问题,预测性优化水和能源灌溉(POWEIr)控制器是一个很有前途的解决方案。POWEIr 控制器是专为太阳能滴灌 (SPDI) 系统设计的经济实惠的精确灌溉控制器,它为低收入农民提供了一个扩大太阳能滴灌和精确农业使用范围的途径。POWEIr 控制器能制定节能节水的灌溉计划,从而降低整个系统的成本。为了保持成本效益,该控制器采用了简单而有效的基于物理的模型和最少的传感器,但其精确性至今仍有待探索。本文研究了 POWEIr 控制器优化灌溉计划对用户和气象传感器输入准确性误差的敏感性,同时评估了这些误差对模拟作物产量的影响。结果表明,在测试的情况下,选择低成本气象站而不是高质量气象站有可能为农民节省 900 多美元,而对作物产量的影响可以忽略不计。这一结论在不同作物和土壤类型中都得到了验证。研究发现,影响最佳灌溉计划的最重要因素是作物系数的变化,这说明需要对控制器进行校准。这项研究强调了 POWEIr 控制器通过使用成本效益高的传感器和最少的校准工作优化灌溉计划的能力。这为低收入国家的农民更多地采用精准灌溉技术和可持续灌溉实践打开了大门。最终,这一进展有可能促进全球范围内的可持续农业集约化,使我们更接近一个更有粮食保障、对环境更负责任的未来。
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Sensitivity study of the Predictive Optimal Water and Energy Irrigation (POWEIr) controller’s schedules for sustainable agriculture systems in resource-constrained contexts

It is imperative to meet the growing food demands of our expanding global population while safeguarding the Earth’s finite natural resources. This challenge becomes even more pressing for resource-constrained farmers residing in low- and middle-income countries (LMICs), who disproportionately bear the brunt of food insecurity. In response to this critical issue, the Predictive Optimal Water and Energy Irrigation (POWEIr) controller is a promising solution. The POWEIr controller was designed as an affordable precision irrigation controller for solar-powered drip irrigation (SPDI) systems and offers an avenue to widen access to SPDI and precision agriculture for low-income farmers. The POWEIr controller creates energy- and water-efficient irrigation schedules that aim to reduce overall system costs. Employing simple yet effective physics-based models alongside minimal sensors to maintain cost-effectiveness, the controller’s accuracy has, until now, remained unexplored. This paper investigates the sensitivity of the POWEIr controller’s optimized irrigation schedules to user and weather sensor accuracy errors in inputs, while also assessing their impact on simulated crop yields. The results reveal that, under the tested scenarios, opting for a low-cost weather station over a high-quality counterpart could potentially save farmers over $900 with negligible consequences to crop yields. This conclusion held steadfast across diverse crop and soil types. The most significant factor affecting the optimal irrigation schedule was found to be changes in the crop coefficient, pointing to the need for calibration of the controller. This research underscores the POWEIr controller’s capability to optimize irrigation schedules through the use of cost-effective sensors and minimal calibration efforts. In doing so, it opens the door to greater adoption of precision irrigation technology and sustainable irrigation practices among farmers in LMICs. Ultimately, this progress has the potential to catalyze sustainable agriculture intensification on a global scale, moving us closer to a more food-secure and environmentally responsible future.

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来源期刊
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture 工程技术-计算机:跨学科应用
CiteScore
15.30
自引率
14.50%
发文量
800
审稿时长
62 days
期刊介绍: Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.
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