Improving model robustness for soybean iron deficiency chlorosis rating by unsupervised pre-training on unmanned aircraft system derived images

Li, Jiating; Oswald, Cody; Graef, George L.; Shi, Yeyin

doi:10.1016/j.compag.2020.105557

Cited by 10 publications

(4 citation statements)

References 37 publications

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“…The identification accuracy is 98.58%. Li et al 16 proposed a nutrient rating model for detecting different nutrient growth stages of soybeans. This model included an unsupervised pre-trained convolutional autoencoder (CAE) and a convolutional neural network used to supervise the training of soybean images.…”

Section: Related Workmentioning

confidence: 99%

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Deep learning-based method for real-time spinach seedling health monitoring

Xu,

Cong,

Zhai

et al. 2024

J. Electron. Imag.

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It is difficult to obtain real-time crop water shortage during spinach seedling production, so this study proposes a real-time spinach seedling water stress classification method based on hierarchical transformer encoder (HTE) MobileNet (HT-MobileNet). An efficient sandglass residual module is designed for the grading algorithm to significantly improve the model grading accuracy. Meanwhile, a lightweight attention mechanism is proposed. It can effectively suppress the influence of irrelevant features on the classification results. Furthermore, an HTE is introduced, classifying the degree of stress accurately under extremely complex spinach seedling conditions. By introducing grouped convolution, the number of model parameters are reduced greatly, which lays the foundation for practical application. The grading accuracy is 94.61%. The number of parameters is 1.78M and the model size is 4.57MB, which are 40.67% lower than the original network. Comparison experiments are conducted between HT-MobileNet and 10 mainstream convolutional neural networks, such as ConvNext. The experimental results demonstrate the outstanding advantages of this research in terms of accuracy and weight. In addition, an intelligent spinach seedling water stress grading system is designed and tested. This study can provide technical support for real-time monitoring of spinach seedling growth status.

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Section: Related Workmentioning

confidence: 99%

“…The identification accuracy is 98.58%. Li et al 16 . proposed a nutrient rating model for detecting different nutrient growth stages of soybeans.…”

Section: Related Workmentioning

confidence: 99%

Deep learning-based method for real-time spinach seedling health monitoring

Xu,

Cong,

Zhai

et al. 2024

J. Electron. Imag.

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“…For identification of disease and deficiency from image data, CNN based DL models are most popularly used. Pre-trained convolutional encoders performed better than recurrent attention neural network (RAN)-CNN in detection of iron deficiency of Soybean [20]. TL model ResNet-50 showed an accuracy of 65.44% in detecting seven nutrient deficiencies in a dataset of 4088 images of black gram [21].…”

Section: Related Workmentioning

confidence: 99%

Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant

Sharma¹,

Nath

Sharma

et al. 2022

Electronics

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Computer vision-based automation has become popular in detecting and monitoring plants’ nutrient deficiencies in recent times. The predictive model developed by various researchers were so designed that it can be used in an embedded system, keeping in mind the availability of computational resources. Nevertheless, the enormous popularity of smart phone technology has opened the door of opportunity to common farmers to have access to high computing resources. To facilitate smart phone users, this study proposes a framework of hosting high end systems in the cloud where processing can be done, and farmers can interact with the cloud-based system. With the availability of high computational power, many studies have been focused on applying convolutional Neural Networks-based Deep Learning (CNN-based DL) architectures, including Transfer learning (TL) models on agricultural research. Ensembling of various TL architectures has the potential to improve the performance of predictive models by a great extent. In this work, six TL architectures viz. InceptionV3, ResNet152V2, Xception, DenseNet201, InceptionResNetV2, and VGG19 are considered, and their various ensemble models are used to carry out the task of deficiency diagnosis in rice plants. Two publicly available datasets from Mendeley and Kaggle are used in this study. The ensemble-based architecture enhanced the highest classification accuracy to 100% from 99.17% in the Mendeley dataset, while for the Kaggle dataset; it was enhanced to 92% from 90%.

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“…Various phenotypes can be sensed at the canopy level by UAVs including plant height, canopy cover, and spectral reflectance (Galli et al., 2020; Kamilaris & Prenafeta‐Boldú, 2018; Sankaran et al., 2015; Volpato et al., 2021). For example, canopy reflectance are sensed by using multispectral or hyperspectral sensors to estimate leaf chlorophyll content, evapotranspiration rate, yield and productivity (Buchaillot et al., 2019; Chivasa et al., 2020; Galli et al., 2020; Li et al., 2020). Plant height and leaf rotation angle are derived from high‐resolution red‐green‐blue (RGB) images (Holman et al., 2016; Kawamura et al., 2020; Xu et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Heading percentage estimation in proso millet (Panicum miliaceum L.) using aerial imagery and deep learning

Zhao

Khound

Ghimire

et al. 2022

The Plant Phenome Journal

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Proso millet (Panicum miliaceum L.), one of the major cultivated millets, serves as a complement to major cereal crops due to its drought tolerance and low input demands. Timing of heading is one of the key agronomic traits associated with its adaptation to a target environment and a major focus in breeding. Conventionally, heading percentage of a plot (genotype) was rated visually by breeders in field. Despite many successful studies reported in automatic head detections in other small grain species, little progress had been made to estimate heading percentage especially when multiple tillers exist. This study aimed to develop a method for automatic proso millet panicle detection and, more importantly, heading percentage estimation using regular red‐green‐blue images collected by an unmanned aerial vehicle. Aerial images of two dates were collected at heading stage in 2020 in Scottsbluff, NE. Faster regions with convolutional neural network models were trained to detect and count proso millet panicles in each plot. Then, using a sigmoid model, the number of detected panicles was converted to heading percentage without having the information of stand count and the number of tillers. Overall, the system achieved the highest coefficient of determination of 0.728 for proso millet heading percentage estimation, and an accuracy of 92.4% in determining whether a plot reached a certain threshold of heading (50% in this study). The methods developed in this study on heading percentage estimation can directly aid in decision making in proso millet breeding and can be ultimately incorporated into an automated proso millet high‐throughput phenotyping pipeline.

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Improving model robustness for soybean iron deficiency chlorosis rating by unsupervised pre-training on unmanned aircraft system derived images

Cited by 10 publications

References 37 publications

Deep learning-based method for real-time spinach seedling health monitoring

Deep learning-based method for real-time spinach seedling health monitoring

Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant

Heading percentage estimation in proso millet (Panicum miliaceum L.) using aerial imagery and deep learning

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