Nutrient Status Diagnosis of Infield Oilseed Rape via Deep Learning-Enabled Dynamic Model

Abdalla, Alwaseela; Cen, Haiyan; Wan, Lijuan; Mehmood, Khalid; He, Yong

doi:10.1109/tii.2020.3009736

Cited by 54 publications

(28 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Inceptionv3 achieved superior performance, with the highest Acc, Pr, Re, and F1_score of 96.5%, 95.7%, 96.2%, and 95.9%, respectively, compared to the ResNet18, which also performed well with Acc, Pr, Re, and F1_score values of 92.1%, 91.6%, 92.1%, and 91.8%, respectively. The performance of our proposed framework was comparable to the reported study that employed Inceptionv3 for extracting deep features and classification of Oilseed Rape [39]. At the same time, one should notice the difference between experimental conditions and classification subjects.…”

Section: Performance Of Classification Methodssupporting

confidence: 72%

See 1 more Smart Citation

Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics

et al. 2022

Self Cite

View full text Add to dashboard Cite

In the aquaponic system, plant nutrients bioavailable from fish excreta are not sufficient for optimal plant growth. Accurate and timely monitoring of the plant’s nutrient status grown in aquaponics is a challenge in order to maintain the balance and sustainability of the system. This study aimed to integrate color imaging and deep convolutional neural networks (DCNNs) to diagnose the nutrient status of lettuce grown in aquaponics. Our approach consists of multi-stage procedures, including plant object detection and classification of nutrient deficiency. The robustness and diagnostic capability of proposed approaches were evaluated using a total number of 3000 lettuce images that were classified into four nutritional classes—namely, full nutrition (FN), nitrogen deficiency (N), phosphorous deficiency (P), and potassium deficiency (K). The performance of the DCNNs was compared with traditional machine learning (ML) algorithms (i.e., Simple thresholding, K-means, support vector machine; SVM, k-nearest neighbor; KNN, and decision Tree; DT). The results demonstrated that the deep proposed segmentation model obtained an accuracy of 99.1%. Also, the deep proposed classification model achieved the highest accuracy of 96.5%. These results indicate that deep learning models, combined with color imaging, provide a promising approach to timely monitor nutrient status of the plants grown in aquaponics, which allows for taking preventive measures and mitigating economic and production losses. These approaches can be integrated into embedded devices to control nutrient cycles in aquaponics.

show abstract

Section: Performance Of Classification Methodssupporting

confidence: 72%

“…Tran et al [38] employed deep learning networks, such as Inception-ResNetv2 and Autoencoder, for classifying calcium, nitrogen, and potassium deficit in tomato plants. Abdalla et al [39] deep learning model to diagnose the nutrient status of oilseed rape by classifying the nutrient statuses of the plants into nine classes.…”

Section: Introductionmentioning

confidence: 99%

Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition to the novelties, the soil NPKC macronutrient prediction accuracies have surpassed the results of the benchmark studies in [7,[9][10][11][12][13] and were comparable to the results in [8]. Several reasons could be cited which might have induced such an improvement.…”

Section: E Efficacy Of the Resultsmentioning

confidence: 77%

“…Finally, the stability in the predictions with no large error rates showed that the model was performing consistently in the face of adverse spectral information. On the other hand, the authors in [8] had worked out comparable RMSE levels in estimating nutrient contents by consuming small datasets of manually acquired images, which we are doubtful that would nullify the risk of overfitting in their models; hence, in such a model the question of reliability and generalizability are needed to be scrutinized further with larger and different datasets.…”

Section: E Efficacy Of the Resultsmentioning

confidence: 99%

“…For example, the authors in [8] have utilized large spatiotemporal datasets with deep learning models, CNN and Long Short Term Memory (LSTM), to assess NPK nutrient deficiencies in Oilseed Rape during different growth stages. They have obtained a 95% high prediction accuracy rate with the manually captured plant canopy images, although the study lacks quantification of the nutrient deficiency levels.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2021

View full text Add to dashboard Cite

Fertilizer misapplications have induced widespread environmental deteriorations, climatic catastrophes, and economic losses; meanwhile, the Precision Agriculture (PA) endorsements have been influential in alleviating these issues. This study intended to tackle the fertilizer consumption inefficiencies by utilizing non-destructive remote sensing technologies, soil macronutrient distribution analysis, and a deep learning model. Specifically, an Unmanned Air Vehicle (UAV) was used in a cornfield to capture the plant's reflectance information for retrieving the Normalized Difference Vegetation Index (NDVI) during the vegetative and reproductive growth stages. Consequently, the field's soil samples were examined for their Nitrogen, Phosphorus, Potassium, and Carbon (NPKC) macronutrient constituencies. Finally, a Convolutional Neural Network-Regression model was developed to predict infield NPKC spatiotemporal variations in soil using the NDVI measurements. The deep learning model effectively determined the surpluses or shortages of the NPKC macronutrients within the cornfield throughout the growth stages. The model performed vigorously with R 2 values of 0.93, 0.92, 0.98, and 0.83 in predicting N, P, K, and C levels in soil, respectively. INDEX TERMS Convolutional Neural Network-Regression, Macronutrients in soil, NDVI, UAV Mr. Mustafa Jaihuni received a BSc degree in Electrical & Electronics Engineering in 2009 from Boğaziçi University, Istanbul, Turkey, and an M.S. degree in 2020 in Biosystems Engineering from Gyeongsang National University (GNU), Gyeongsangnam-do, Republic of Korea. Currently, he is a research student at GNU in the Smart Farm Systems Lab, department of Biosystems Engineering. He has published 2 articles in different Journals related to Agriculture, Renewable Energy and Smart Farming applications. Mr. Jaihuni's research interests are Remote Sensing, Deep Learning, ICT applications in Agriculture and Wind-Solar Renewable Energy Systems. Mr. Fawad Khan was born in Peshawar, Pakistan, in 1991. He received a BS (Hons) degree in Environmental Sciences and a Master's degree in Biosystems Engineering from the University of Peshawar (Pakistan) and Gyeongsang National University (Republic of Korea), respectively. He published seventeen articles in different Journals related to Agriculture and Horticulture, Energy, and Bio-systems engineering. Mr. Khan's research interests nutrients management, crop yield, and greenhouse gases

show abstract

A vision of precision agriculture: Balance between agricultural sustainability and environmental stewardship

Nath¹

2023

Agronomy Journal

View full text Add to dashboard Cite

Precision agriculture (PA) has become an increasingly popular approach to sustainable intensification in agriculture, with the intent of a balance between agricultural productivity and environmental stewardship. Sustainable intensification aims to increase agricultural productivity while minimizing negative environmental impacts. To assist this, PA has been enabled through the use of advanced technologies such as the Internet of Things, global positioning system, geographic information systems, sensors, drones, and machine learning. These technologies have enabled farmers to practice more efficient and precise farming techniques. This review provides a vision of sustainable intensification and explores the potential of PA to contribute to this goal. This review emphasizes various PA practices, such as precise irrigation and fertilizer management, accurate pest and disease management, and precise livestock farming. PA is also explored for its role in addressing climate change through mitigation and adaptation, while proactively addressing the barriers and challenges that may arise in implementing sustainable PA. Therefore, the future of sustainable precision agriculture lies in technological innovation, sustainable farming practices, data analytics, and policy interventions.

show abstract

Nutrient Status Diagnosis of Infield Oilseed Rape via Deep Learning-Enabled Dynamic Model

Cited by 54 publications

References 26 publications

Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics

Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics

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

A vision of precision agriculture: Balance between agricultural sustainability and environmental stewardship

Contact Info

Product

Resources

About