Determining Spatiotemporal Distribution of Macronutrients in a Cornfield Using Remote Sensing and a Deep Learning Model

Jaihuni, Mustafa; Khan, Fawad; Lee, Deoghyun; Basak, Jayanta Kumar; Bhujel, Anil; Moon, Byeong Eun; Park, Jaesung; Kim, Hyeon Tae

doi:10.1109/access.2021.3059314

Cited by 11 publications

(5 citation statements)

References 31 publications

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“…In the literature [20][21][22][23][24], these algorithms have satisfactory results for many issues involving images, including plant classification, features extraction and determination of nutrients in plants. These classifications generally rely on morphological descriptors such as leaves, stems, fruits, and flowers [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Towards Amazon Forest Restoration: Automatic Detection of Species from UAV Imagery

et al. 2021

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Precise assessments of forest species’ composition help analyze biodiversity patterns, estimate wood stocks, and improve carbon stock estimates. Therefore, the objective of this work was to evaluate the use of high-resolution images obtained from Unmanned Aerial Vehicle (UAV) for the identification of forest species in areas of forest regeneration in the Amazon. For this purpose, convolutional neural networks (CNN) were trained using the Keras–Tensorflow package with the faster_rcnn_inception_v2_pets model. Samples of six forest species were used to train CNN. From these, attempts were made with the number of thresholds, which is the cutoff value of the function; any value below this output is considered 0, and values above are treated as an output 1; that is, values above the value stipulated in the Threshold are considered as identified species. The results showed that the reduction in the threshold decreases the accuracy of identification, as well as the overlap of the polygons of species identification. However, in comparison with the data collected in the field, it was observed that there exists a high correlation between the trees identified by the CNN and those observed in the plots. The statistical metrics used to validate the classification results showed that CNN are able to identify species with accuracy above 90%. Based on our results, which demonstrate good accuracy and precision in the identification of species, we conclude that convolutional neural networks are an effective tool in classifying objects from UAV images.

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Section: Introductionmentioning

confidence: 99%

Towards Amazon Forest Restoration: Automatic Detection of Species from UAV Imagery

et al. 2021

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“…Current techniques for monitoring crop growth status using deep learning and convolutional neural networks 11 – 13 are well established. Lee et al 14 .…”

Section: Related Workmentioning

confidence: 99%

“…Current techniques for monitoring crop growth status using deep learning and convolutional neural networks [11][12][13] are well established. Lee et al 14 combined drone and deep learning techniques to monitor broccoli growth.…”

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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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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“…Recently, many papers have reported using deep neural networks to classify plant nutrient deficiencies [31], [32], [33]. Sudhakar et al [34] presented an extensive survey about machine and deep learning (DL) methods for identifying plant nutrient deficiencies based on plant images.…”

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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Determining Spatiotemporal Distribution of Macronutrients in a Cornfield Using Remote Sensing and a Deep Learning Model

Cited by 11 publications

References 31 publications

Towards Amazon Forest Restoration: Automatic Detection of Species from UAV Imagery

Towards Amazon Forest Restoration: Automatic Detection of Species from UAV Imagery

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

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

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