Determining leaf nutrient concentrations in citrus trees using UAV imagery and machine learning

Costa, Lucas; Kunwar, Sudip; Ampatzidis, Yiannis; Albrecht, Ute

doi:10.1007/s11119-021-09864-1

Cited by 35 publications

(9 citation statements)

References 48 publications

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“…The correlation coefficients here were quite close and amounted to r = 0.70-0.75, which may indicate the applicability in the calculations of each of the presented vegetation indices. The average absolute percentage error has been calculated in many studies on the use of UAVs in agriculture [51][52][53][54][55][56][57][58][59]. Studies with a high level of accuracy consider MAPE values up to 15% [52,[56][57][58][59].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Field Germination of Soybean Breeding Crops Using Multispectral Data from UAV

et al. 2023

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The use of multispectral aerial photography data contributes to the study of soybean plants by obtaining objective data. The evaluation of field germination of soybean crops was carried out using multispectral data (MSD). The purpose of this study was to develop ranges of field germination of soybean plants according to multispectral survey data from an unmanned aerial vehicle (UAV) for three years (2020, 2021, and 2022). As part of the ground-based research, the number of plants that sprang up per unit area was calculated and expressed as a percentage of the seeds sown. A DJI Matrice 200 Series v2 unmanned aerial vehicle and a MicaSense Altum multispectral camera were used for multispectral aerial photography. The correlation between ground-based and multispectral data was 0.70–0.75. The ranges of field germination of soybean breeding crops, as well as the vegetation indices (VIs) normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and chlorophyll index green (ClGreen) were calculated according to Sturges’ rule. The accuracy of the obtained ranges was estimated using the mean absolute percentage error (MAPE). The MAPE values did not exceed 10% for the ranges of the NDVI and ClGreen vegetation indices, and were no more than 18% for the NDRE index. The final values of the MAPE for the three years did not exceed 10%. The developed software for the automatic evaluation of the germination of soybean crops contributed to the assessment of the germination level of soybean breeding crops using multispectral aerial photography data. The software considers data of the three vegetation indices and calculated ranges, and creates an overview layer to visualize the germination level of the breeding plots. The developed method contributes to the determination of field germination for numerous breeding plots and speeds up the process of breeding new varieties.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Field Germination of Soybean Breeding Crops Using Multispectral Data from UAV

et al. 2023

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“…In one of the studies, the authors used color and shape features along with an artificial neural network, k-nearest neighbor (k-NN), and support vector machine (SVM) to classify macro-nutrient deficiency in maize plants [28]. In another study, the authors used an unmanned aerial vehicle to capture multi-spectral images of citrus plants and gradient boost regression to determine citrus plant nutrient concentrations in plant leaves [29]. Recently, machine learning models were used to identify Soybean genotypes from macro-nutrient contents to develop efficient genotypes.…”

Section: Related Workmentioning

confidence: 99%

Deep Neural Networks for Classifying Nutrient Deficiencies in Rice Plants Using Leaf Images

Kolhar,

Jagtap,

Shastri

2024

IJCDS

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Nutrients are vital in ensuring expected crop growth and yield quality. Accurate identification of nutrient deficiencies in plants is essential to provide appropriate supplements of fertilizers. Manual inspection of symptoms and identifying nutrient deficiencies is a tiresome task requiring higher expertise. This paper aims to design and develop a computationally efficient deep-learning model to classify plant nutrient deficiencies accurately. This paper presents an image-based deep-learning framework for nutrient deficiency identification. Three deep learning models, namely the Xception model, vision transformer, and multi-layer perceptron-based (MLP) mixer model, were trained to identify nitrogen (N), phosphorous (P), and potassium (K) deficiencies in rice plants from red-green-blue (RGB) images. The model performance is tested on nutrient deficiency symptoms in rice plants dataset available publicly on Kaggle. All three models achieved nutrient deficiency classification accuracy greater than 92%. The Xception model achieved the highest average accuracy of 95.14% at the cost of approximately 1.2 million total trainable parameters, much less than the vision transformer and MLP mixer model. The Xception model performs better than the other two models in classifying nutrient deficiencies with the least number of total trainable parameters. In the future, these neural networks can be trained and extended to accurately detect and segment nutrient-deficient crop areas in large fields to supply precise fertilizer supplements.

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“…Based on multispectral data, Costa et al proposed a novel methodology to determine leaf nutrient concentrations in citrus trees using unmanned aerial vehicle (UAV) multispectral imagery and artificial intelligence [8]. Sun et al used UAV multispectral indices to perform ordinary linear regression, multivariate stepwise regression, and ridge regression inversion models the on leaf NPK content [9].…”

Section: Rapid Detection Technology For Tree Nutrientsmentioning

confidence: 99%

Research Progress in Intelligent Diagnosis Key Technology for Orchard Nutrients

Yuan,

Qi,

Huang

et al. 2024

Applied Sciences

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The intelligent diagnosis key technology of orchard nutrients provides a decision-making basis for precision fertilization, which has important research significance. This article reviewed the recent research literature, compared and analyzed existing technologies, and summarized solved and unresolved problems. It aimed to find breakthroughs to further improve the level of intelligent diagnosis key technology for orchard nutrients, and promote the implementation and application of the technology. Research had found that the current rapid nutrient detection technologies were mostly based on spectral data, with a focus on preprocessing algorithms and regression models. Hyperspectral technology shows good performance in predicting tree and soil nutrients due to its large number of characteristic variables. Meanwhile, preprocessing algorithms such as filtering, transformation, and feature band selection had also solved the problem of data redundancy. However, there were few studies for small and trace elements, and field applications. Laser breakdown-induced spectroscopy has good prospects for soil nutrient detection, as it can simultaneously detect multiple nutrients. There had been some studies on the technology for generating suitable nutrient standards for orchards in terms of soil and tree nutrients, but it requires a long and extensive experiment, which is time-consuming and laborious. A universal and rapid method needs to be studied to meet the construction needs of suitable nutrient standards for different varieties of fruit trees.

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Determining leaf nutrient concentrations in citrus trees using UAV imagery and machine learning

Cited by 35 publications

References 48 publications

Evaluation of Field Germination of Soybean Breeding Crops Using Multispectral Data from UAV

Evaluation of Field Germination of Soybean Breeding Crops Using Multispectral Data from UAV

Deep Neural Networks for Classifying Nutrient Deficiencies in Rice Plants Using Leaf Images

Research Progress in Intelligent Diagnosis Key Technology for Orchard Nutrients

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