Spatial interpolators for Delineating management zones to mitigate Mucuna pruriens in sugarcane plantations in the Eastern Amazon

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture Pub Date : 2024-11-10 DOI:10.1016/j.compag.2024.109615

Luiz Antonio Soares Cardoso , Paulo Roberto Silva Farias , João Almiro Corrêa Soares , Carlos Rodrigo Tanajura Caldeira , Fábio Júnior de Oliveira

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引用次数: 0

Abstract

Unmanned Aerial Vehicles (UAVs) have emerged as essential tools in precision agriculture, employing aerial photogrammetry concepts to aid producers in various decision-making processes. This study evauates different spatial interpolators to define management zones in sugarcane fields, aiming to control potential infestations by Mucuna pruriens. We collected images using the EbeeSQ UAV, equipped with a multispectral sensor, and calculated vegetation indices, including NDVI, SAVI, NDRE, and GNDVI. Analysis revealed that GNDVI yielded the most favorable results, with a mean value of 0.304 and a coefficient of variation of 11.747 %. Using regular and random sampling grids, we applied Ordinary Kriging (OK) and Support Vector Machine (SVM) interpolators to assess spatial variability across 13 survey zones. The results indicated a Degree of Spatial Dependence averaging 57.197 % and a Moran Index of 0.609, confirming moderate spatial dependence. Cross-validation showed that OK with random sampling outperformed other methods, achieving a Root Mean Square Error (RMSE) of 0.064 and a coefficient of determination (r²) averaging 0.347. Furthermore, the relationship between the Fuzzy Performance Index (averaging 0.069) and Normalized Classification Entropy (averaging 0.077) enabled the creation of management zone maps. These maps effectively identify distinct classes within the study areas, enhancing decision-making for producers in managing velvet bean weed during critical developmental phases.

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用于划定管理区的空间插值器，以减少亚马逊河东部甘蔗种植园中的金丝桃危害

无人驾驶飞行器（UAV）已成为精准农业的重要工具，它采用航空摄影测量概念来帮助生产者进行各种决策。本研究利用不同的空间插值法来确定甘蔗田的管理区域，目的是控制潜在的金丝楠木侵扰。我们使用配备多光谱传感器的 EbeeSQ 无人机采集图像，并计算植被指数，包括 NDVI、SAVI、NDRE 和 GNDVI。分析表明，GNDVI 的结果最理想，平均值为 0.304，变异系数为 11.747%。我们使用规则和随机抽样网格，应用普通克里金（OK）和支持向量机（SVM）插值器评估了 13 个调查区的空间变异性。结果表明，空间依赖度平均为 57.197%，莫兰指数为 0.609，证实了中等程度的空间依赖性。交叉验证结果表明，采用随机抽样的 OK 方法优于其他方法，其均方根误差（RMSE）为 0.064，判定系数（r2）平均为 0.347。此外，模糊性能指数（平均为 0.069）和归一化分类熵（平均为 0.077）之间的关系使得管理区域图得以绘制。这些地图有效地确定了研究区域内的不同等级，从而提高了生产者在关键生长阶段管理绒豆杂草的决策能力。

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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.