Estimation of eggplant yield with machine learning methods using spectral vegetation indices

Taşan, Sevda; Cemek, Bilal; Taşan, Mehmet; Cantürk, Aslıhan

doi:10.1016/j.compag.2022.107367

Cited by 18 publications

(4 citation statements)

References 61 publications

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“…Then, the PCA was performed to reduce the number of input VIs. The feature screening methods proposed by Taşan et al [34] were also employed. In addition to the Person correlation and PCA, other feature selection methods were used in previous studies [35,36].…”

Section: Discussionmentioning

confidence: 99%

Biomass Estimation of Milk Vetch Using UAV Hyperspectral Imagery and Machine Learning

Hu,

Zhou,

Cao

et al. 2024

Remote Sensing

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Milk vetch (Astragalus sinicus L.) is a winter-growing plant that can enhance soil fertility and provide essential nutrients for subsequent season crops. The fertilizing capacity of milk vetch is closely related to its above-ground biomass. Compared to the manual measurement methods of milk vetch biomass, remote sensing-based estimation methods have the advantages of rapid, noninvasive, and large-scale measurement. However, few studies have been conducted on remote sensing-based estimation of milk vetch biomass. To address this shortcoming, this study proposes combining unmanned aerial vehicle (UAV)-based hyperspectral imagery and machine learning algorithms for accurate estimation of milk vetch biomass. Through the analysis of hyperspectral images and feature selection based on the Pearson correlation and principal component analysis, vegetation indices (VIs), including near-infrared reflectance (NIR), red-edge spectral transform index (RE), and difference vegetation index (DVI), are selected as estimation metrics of the model development process. Four machine learning methods, including random forest (RF), multiple linear regression (MLR), deep neural network (DNN), and support vector machine (SVM), are used to construct the biomass models. The results show that the RF estimation model exhibits the highest coefficient of determination (R2) of 0.950 and the lowest relative root-mean-squared error (RRMSE) of 14.86% among all the models. Notably, the DNN model demonstrates promising performance on the test set, with the R2 and RRMSE values slightly superior and inferior to those of the RF, respectively. The proposed method based on UAV imagery and machine learning can provide an accurate and reliable large-scale estimation of milk vetch biomass.

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Section: Discussionmentioning

confidence: 99%

Biomass Estimation of Milk Vetch Using UAV Hyperspectral Imagery and Machine Learning

Hu,

Zhou,

Cao

et al. 2024

Remote Sensing

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“…Various spectral vegetation indices have been developed for agricultural studies. These indices, obtained by calculating reflectance values in two or more spectral bands, provide information about the growth stages of crops (Taşan et al, 2022;Sunar et al, 2011).…”

Section: Spectral Vegetation Indexmentioning

confidence: 99%

A Comparative Study of Satellite Image Resolutions for Detecting Pest Damage in Sunflower Fields

Ercan,

Maden,

Kara

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The diversity of sensors in remote sensing allows for faster and easier detection of changes and issues across different scales, in contrast to conventional ground-based systems. One of the most important distinguishing features among these sensors is their varying resolutions, contributing to the versatility of remote sensing technologies across diverse environmental applications. In this study, the effectiveness of PlanetScope and Sentinel-2 satellite images with different image resolutions in detecting damage caused by a harmful insect (beet webworm moth - Loxostege sticticalis) in sunflower fields in Lüleburgaz district of Kırklareli in the Trace region was evaluated. Damage rates in sunflower fields were analyzed using various spectral indices (Enhanced Vegetation Index and Chlorophyll Index Green) and spectral transformation (Tasseled Cap Greenness) in conjunction with in situ data. Based on the spectral analysis, the satellite image dated 26 July, which showed the most severe damage, was used in the damage assessment analysis. The damaged areas were compared by classifying both satellite images with the Random Forest algorithm. According to the results of the classification accuracy assessment, PlanetScope satellite imagery showed the highest accuracy, with 90% overall accuracy and 84% Kappa statistics, making it a more suitable sensor choice for agricultural applications.

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“…Supervised learning approaches are most employed and yield prediction models are the most common use case. Estimating yield prior to harvest allows for accurate and timely planning (Taşan et al, 2022), which promotes efficient use of harvesting robots. Satellite and UAV imagery combined with ML methods have created accurate and detailed yield maps for crops grown in open fields (Maponya et al, 2020).…”

Section: Machine Learningmentioning

confidence: 99%

Leveraging I4.0 smart methodologies for developing solutions for harvesting produce

Recchia,

Urbanic

2023

Front. Manuf. Technol.

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Leveraging Computer-Aided Design (CAD) and Manufacturing (CAM) tools with advanced Industry 4.0 (I4.0) technologies presents numerous opportunities for industries to optimize processes, improve efficiency, and reduce costs. While certain sectors have achieved success in this effort, others, including agriculture, are still in the early stages of implementation. The focus of this research paper is to explore the potential of I4.0 technologies and CAD/CAM tools in the development of pick and place solutions for harvesting produce. Key technologies driving this include Internet of Things (IoT), machine learning (ML), deep learning (DL), robotics, additive manufacturing (AM), and simulation. Robots are often utilized as the main mechanism for harvesting operations. AM rapid prototyping strategies assist with designing specialty end-effectors and grippers. ML and DL algorithms allow for real-time object and obstacle detection. A comprehensive review of the literature is presented with a summary of the recent state-of-the-art I4.0 solutions in agricultural harvesting and current challenges/barriers to I4.0 adoption and integration with CAD/CAM tools and processes. A framework has also been developed to facilitate future CAD/CAM research and development for agricultural harvesting in the era of I4.0.

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Estimation of eggplant yield with machine learning methods using spectral vegetation indices

Cited by 18 publications

References 61 publications

Biomass Estimation of Milk Vetch Using UAV Hyperspectral Imagery and Machine Learning

Biomass Estimation of Milk Vetch Using UAV Hyperspectral Imagery and Machine Learning

A Comparative Study of Satellite Image Resolutions for Detecting Pest Damage in Sunflower Fields

Leveraging I4.0 smart methodologies for developing solutions for harvesting produce

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