A deep learning approach to measure stress level in plants due to Nitrogen deficiency

Azimi, Shiva; Kaur, Taranjit; Gandhi, Tapan Kumar

doi:10.1016/j.measurement.2020.108650

Cited by 71 publications

(43 citation statements)

References 39 publications

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“…On the other hand, N deficit increases the root/shoot ratio or decreases the shoot/root ratio (Gastal et al, 2015). However, variation exists between species in the intensity of the shoot/root response to N deficiency (Azimi et al, 2021). This explains the results of M. crystallinum plants transferred from full N to 0 N having higher shoot/root FW ratio, which was different from those reported by others (Gastal et al, 2015;Tolley and Mohammadi, 2020).…”

Section: Productivity Of Shoot and Root And Leaf Growthmentioning

confidence: 63%

“…Leaf area determines light interception and is an important parameter in determining plant productivity (He et al, 2019). The reductions of leaf number (Figure 2A) and TLA (Figure 2B) might partly account for the lower biomass for M. crystallinum grown with full N and for those transferred from full N to 1/4 N and 0 N. N deficiency significantly reduced leaf area, resulting in lower photosynthetic capacity leading to lower biomass production (Broadley et al, 2001;Tolley and Mohammadi, 2020;Azimi et al, 2021;Mu and Chen, 2021). However, there is very little information on the effects of excessive N on leaf number and TLA.…”

Section: Productivity Of Shoot and Root And Leaf Growthmentioning

confidence: 99%

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Impacts of Reduced Nitrate Supply on Nitrogen Metabolism, Photosynthetic Light-Use Efficiency, and Nutritional Values of Edible Mesembryanthemum crystallinum

Qin

2021

Front. Plant Sci.

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Mesembryanthemum crystallinum (common ice plant), as a nutritious ready-to-eat salad in Singapore, has become popular in recent years. However, basic data about the impacts of NO3– supply on its NO3– accumulation and nutritional quality are lacking. In this study, all plants were first grown indoor hydroponically in 10% artificial seawater (ASW) with modified full-strength Netherlands Standard Composition nutrient solution for 11 days, before transferring them to different reduced NO3– solutions. All plants grew well and healthy after 7 days of treatment. However, plants grown with 3/4 N and 1/2 N were bigger with higher shoot and root fresh weight (FW), greater leaf number, and total leaf area (TLA) than those grown with full nitrogen (N), 1/4 N, and 0 N. Mesembryanthemum crystallinum grown with full N, 3/4 N, and 1/4 N had similar specific leaf area (SLA), while 0 N plants had significantly lower SLA. All plants had similar leaf succulence (LS). However, leaf water content (LWC) was lower, while leaf dry matter accumulation (LDMC) was higher in 0 N plants after 7 days of treatment. Compared with plants grown with full N, shoot NO3– concentrations in 3/4 N, 1/2 N, and 1/4 N plants were constant or slightly increased during the treatments. For 0 N plants, shoot NO3– concentration decreased significantly during the treatment compared with other plants. Shoot NO3– accumulation was associated with nitrate reductase activity (NRA). For instance, after 7 days of treatment, shoot NO3– concentration and NRA on a FW basis in 0 N plants were, respectively, 45 and 31% of full N plants. After transferring full N to 0 N for 7 days, all M. crystallinum had higher chlorophyll (Chl) content coupled with higher electron transport rate (ETR) and higher effective quantum yield of PSII, while full N plants had higher non-photochemical quenching (NPQ). The 0N plants had much higher concentrations of proline, total soluble sugar (TSS), and total ascorbic acid (ASC) than other plants. In conclusion, totally withdrawing NO3– from the growth media prior to harvest could be one of the strategies to reduce shoot NO3– concentration. Reduced NO3– supply further enhanced nutritional values as concentrations of proline, TSS, and ASC were enhanced markedly in M. crystallinum plants after transferring them from full N to 0 N.

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Section: Productivity Of Shoot and Root And Leaf Growthmentioning

confidence: 63%

Section: Productivity Of Shoot and Root And Leaf Growthmentioning

confidence: 99%

Impacts of Reduced Nitrate Supply on Nitrogen Metabolism, Photosynthetic Light-Use Efficiency, and Nutritional Values of Edible Mesembryanthemum crystallinum

Qin

2021

Front. Plant Sci.

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“…Sometimes, due to the heavy chewing of plant leaves by insects, the total area of a leaf is reduced to a great extent, which results in a reduction of photosynthesis as well. Azimi et al [ 13 ] presented a deep learning-based approach for plant leaf stress identification caused by nitrogen deficiency. On the other hand, Noon et al [ 14 ] presented a plant leaf stress identification survey using deep learning techniques.…”

Section: Methodsmentioning

confidence: 99%

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

Meena

Dhaka

Sinwar

et al. 2021

Sensors

231

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In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.

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“…As Anami et al [31], who proposed the method to classify water stress in paddy crop resulting in training data accuracy of 92.89%. Other researchers using deep learning to detect stress on nitrogen deficiency in sorghum plants with an accuracy of 92% [32] and convolutional neural network (CNN) use to detect light stress in lettuce with an accuracy of 87.95% [33]. This kind of deep learning, CNN, showed an outstanding performance in image recognition scope and therefore this may bring potential methods to detect water stress in plants compared to other machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

Classification of water stress in cultured Sunagoke moss using deep learning

et al. 2021

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Water stress greatly determines plant yield as it affects plant metabolism, photosynthesis rate, chlorophyll content index, number of leaves, physiological, biochemical compound, and vegetative growth. The research aimed to detect and classify water stress of cultured Sunagoke moss into several categories i.e. dry, semi-dry, wet, and soak by using a low-cost commercial visible light camera combined with a deep learning model. Cultured Sunagoke moss is a commercial product which has the potential use as rooftop-greening and wall-greening material. This research compared the performance of four convolutional neural network models, such as SqueezeNet, GoogLeNet, ResNet50, and AlexNet. The best convolutional neural network model according to the training and validation result was ResNet50 with RMSProp optimizer, 30 epoch, and 128 mini-batch size; this also gained an accuracy rate at 87.50%. However, the best result of the convolutional neural network model on data testing using confusion matrices on different data sample was ResNet50 with Adam optimizer, 30 epoch, 128 mini-batch size, and average testing accuracy of 94.15%. It can be concluded that based on the overall results, convolutional neural network model seems promising as a smart irrigation system that real-time, non-destructive, rapid, and precise method when controlling water stress of plants.

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A deep learning approach to measure stress level in plants due to Nitrogen deficiency

Cited by 71 publications

References 39 publications

Impacts of Reduced Nitrate Supply on Nitrogen Metabolism, Photosynthetic Light-Use Efficiency, and Nutritional Values of Edible Mesembryanthemum crystallinum

Impacts of Reduced Nitrate Supply on Nitrogen Metabolism, Photosynthetic Light-Use Efficiency, and Nutritional Values of Edible Mesembryanthemum crystallinum

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

Classification of water stress in cultured Sunagoke moss using deep learning

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