Machine Learning for the estimation of foliar nitrogen content in pineapple crops using multispectral images and Internet of Things (IoT) platforms

IF 4.8 Q1 AGRICULTURE, MULTIDISCIPLINARY
Jorge Enrique Chaparro , José Edinson Aedo , Felipe Lumbreras Ruiz
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Abstract

Nitrogen is the most important nutritional element during the vegetative growth phase of the pineapple crop; however, its presence in the soil is insufficient to meet plant demands. In this study, nine machine learning techniques were validated to estimate the total nitrogen (TN) content in MD2 pineapple crops from data from multiple sources. These sources included multispectral images captured by an unmanned aerial vehicle (UAV); in situ sensors, which collected information on ecological factors such as pH, temperature, solar radiation, relative humidity, soil moisture, wind speed and direction, as well as SPAD values indicating leaf chlorophyll content.

Total nitrogen (TN) values were taken from leaf tissue samples, which were then analyzed in a laboratory. To introduce nitrogen variability, a complete randomized block experimental design was implemented, applying five different treatments in five blocks, each with 12 replications, during a 6-month period in a pineapple crop located in Tauramena, Colombia. To address the inherent variability in the agricultural and environmental data, dimensionality was reduced using Principal Component Analysis (PCA). In addition, regularization techniques were applied, including cross-validation, feature selection, boost methods, L1 (Lasso) and L2 (Ridge) regularization, as well as hyperparameter optimization. These strategies generated more robust and accurate models, with the multilayer perceptron regressor (MLP regressor) and extreme gradient boosting (XGBoost) algorithms standing out. On the first sampling date, XGBoost achieved an R2 of 86.98 %, being the highest. On the following dates, MLP achieved a R2 of 59.11 % on the second date; XGBoost achieved a R2 of 68.00 % on the third date, and on the last date, MLP achieved a R2 of 69.4 %. These results indicate that the integration of data from multiple sources and the use of machine learning models could greatly improve the precision of nitro-gen (N) diagnostics in pineapple crops, especially in real-time applications. These findings highlight the promising potential of developing machine learning models that integrate multisensor data fusion for various applications in agriculture.

Abstract Image

利用多光谱图像和物联网(IoT)平台进行机器学习,估算菠萝作物叶面氮含量
氮是菠萝作物无性生长阶段最重要的营养元素;然而,土壤中的氮含量不足以满足植物的需求。在本研究中,对九种机器学习技术进行了验证,以便从多种来源的数据中估算 MD2 菠萝作物中的总氮(TN)含量。这些数据源包括无人飞行器(UAV)拍摄的多光谱图像;现场传感器收集的生态因子信息,如 pH 值、温度、太阳辐射、相对湿度、土壤湿度、风速和风向,以及表示叶片叶绿素含量的 SPAD 值。为了引入氮的变异性,采用了完全随机区组实验设计,在哥伦比亚陶拉梅纳的菠萝作物中进行了为期 6 个月的试验,在 5 个区组中采用了 5 种不同的处理方法,每个区组 12 次重复。为了解决农业和环境数据中固有的变异性问题,使用主成分分析法(PCA)降低了维度。此外,还应用了正则化技术,包括交叉验证、特征选择、提升方法、L1(Lasso)和 L2(Ridge)正则化以及超参数优化。这些策略产生了更稳健、更准确的模型,其中多层感知器回归器(MLP 回归器)和极梯度提升(XGBoost)算法脱颖而出。在第一个取样日期,XGBoost 的 R2 达到了 86.98%,是最高的。在随后的日期中,MLP 在第二个日期的 R2 为 59.11%;XGBoost 在第三个日期的 R2 为 68.00%;在最后一个日期,MLP 的 R2 为 69.4%。这些结果表明,整合多种来源的数据并使用机器学习模型可以大大提高菠萝作物硝基(N)诊断的精确度,尤其是在实时应用中。这些发现凸显了开发机器学习模型,将多传感器数据融合应用于农业领域的巨大潜力。
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
CiteScore
5.40
自引率
2.60%
发文量
193
审稿时长
69 days
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