Development of an autonomous drone spraying control system based on the coefficient of variation of spray distribution

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture Pub Date : 2024-10-12 DOI:10.1016/j.compag.2024.109529

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引用次数: 0

Abstract

Pests and disease prevention has long been a key area of focus in precision agriculture research. While unmanned aerial spraying systems have advanced significantly and gained widespread adoption in recent years, challenges persist, including the high cost of precision spraying drones and issues related to uneven spraying and over-application with conventional systems. To address these limitations, this paper introduces a low-cost, versatile, and modular autonomous spraying control system that includes a ground base station and a spraying control assistant. The system integrates a spraying uniformity control algorithm based on a regression forest model, ensuring a coefficient of variation (CV) below 30 %. It also collects real-time environmental data to optimize the drone’s spraying strategy. Environmental data and global positioning system’s correction signals are transmitted from the ground base station to the onboard spraying control system (mobile station) via LoRa communication, enabling precise positioning and real-time adjustments during spraying. Indoor spraying simulation experiments demonstrate that the autonomous spraying control system achieved a CV within the standardized requirement in 15 out of 23 trials, with an overall predicted CV of less than 30 %. In outdoor experiments, using a hypothetical prescription map for targeted precision spraying, the system successfully completed all prescribed spraying zones. All targeted zones met directed spraying performance indicators exceeding 0.87, demonstrating high accuracy. The system shows significant potential for enhancing the precision spraying capabilities of conventional drones while reducing pest and disease control costs.

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开发基于喷雾分布变异系数的无人机自主喷洒控制系统

长期以来，病虫害预防一直是精准农业研究的重点领域。近年来，无人机喷洒系统取得了长足进步，并得到了广泛应用，但挑战依然存在，包括无人机精准喷洒的高成本以及传统系统喷洒不均匀和过度喷洒的相关问题。为解决这些局限性，本文介绍了一种低成本、多功能、模块化的自主喷洒控制系统，包括地面基站和喷洒控制助手。该系统集成了基于回归森林模型的喷洒均匀性控制算法，确保变异系数（CV）低于 30%。它还能收集实时环境数据，优化无人机的喷洒策略。环境数据和全球定位系统的校正信号通过 LoRa 通信从地面基站传输到机载喷洒控制系统（移动站），从而实现喷洒过程中的精确定位和实时调整。室内喷洒模拟实验表明，在 23 次试验中，自主喷洒控制系统在 15 次试验中实现了符合标准要求的 CV 值，总体预测 CV 值低于 30%。在室外实验中，该系统使用假设处方图进行定向精确喷洒，成功完成了所有规定的喷洒区域。所有目标区域的定向喷洒性能指标都超过了 0.87，显示了很高的准确性。该系统在提高传统无人机的精确喷洒能力，同时降低病虫害防治成本方面显示出巨大潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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.