Early identification of Tuta absoluta in tomato plants using deep learning

2021 ◽

Vol 11 (5) ◽

pp. 7730-7737

Author(s):

L. Loyani ◽

D. Machuve

Keyword(s):

Deep Learning ◽

Mobile Application ◽

Smartphone Application ◽

Tuta Absoluta ◽

Growth Stages ◽

Effective Management ◽

Tomato Plants ◽

Computer Vision Applications ◽

Time Diagnosis ◽

Leaf Image

With the advances in technology, computer vision applications using deep learning methods like Convolutional Neural Networks (CNNs) have been extensively applied in agriculture. Deploying these CNN models on mobile phones is beneficial in making them accessible to everyone, especially farmers and agricultural extension officers. This paper aims to automate the detection of damages caused by a devastating tomato pest known as Tuta Absoluta. To accomplish this objective, a CNN segmentation model trained on a tomato leaf image dataset is deployed on a smartphone application for early and real-time diagnosis of the pest and effective management at early tomato growth stages. The application can precisely detect and segment the shapes of Tuta Absoluta-infected areas on tomato leaves with a minimum confidence of 70% in 5 seconds only.

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Gait analysis for early Parkinson’s disease detection based on deep learning

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2019-0003 ◽

2019 ◽

Vol 5 (1) ◽

pp. 9-12

Author(s):

Jyothsna Kondragunta ◽

Christian Wiede ◽

Gangolf Hirtz

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Deep Learning ◽

Gait Analysis ◽

Pose Estimation ◽

Early Identification ◽

Human Gait ◽

3D Pose Estimation ◽

Wearable Technologies ◽

Advantages And Disadvantages

AbstractBetter handling of neurological or neurodegenerative disorders such as Parkinson’s Disease (PD) is only possible with an early identification of relevant symptoms. Although the entire disease can’t be treated but the effects of the disease can be delayed with proper care and treatment. Due to this fact, early identification of symptoms for the PD plays a key role. Recent studies state that gait abnormalities are clearly evident while performing dual cognitive tasks by people suffering with PD. Researches also proved that the early identification of the abnormal gaits leads to the identification of PD in advance. Novel technologies provide many options for the identification and analysis of human gait. These technologies can be broadly classified as wearable and non-wearable technologies. As PD is more prominent in elderly people, wearable sensors may hinder the natural persons movement and is considered out of scope of this paper. Non-wearable technologies especially Image Processing (IP) approaches captures data of the person’s gait through optic sensors Existing IP approaches which perform gait analysis is restricted with the parameters such as angle of view, background and occlusions due to objects or due to own body movements. Till date there exists no researcher in terms of analyzing gait through 3D pose estimation. As deep leaning has proven efficient in 2D pose estimation, we propose an 3D pose estimation along with proper dataset. This paper outlines the advantages and disadvantages of the state-of-the-art methods in application of gait analysis for early PD identification. Furthermore, the importance of extracting the gait parameters from 3D pose estimation using deep learning is outlined.

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Early identification of live bacteria in water samples using time-lapse holographic imaging and deep learning

Emerging Topics in Artificial Intelligence (ETAI) 2021 ◽

10.1117/12.2593804 ◽

2021 ◽

Author(s):

Hongda Wang ◽

Hatice C. Koydemir ◽

Yunzhe Qiu ◽

Bijie Bai ◽

Yibo Zhang ◽

...

Keyword(s):

Deep Learning ◽

Water Samples ◽

Early Identification ◽

Time Lapse ◽

Holographic Imaging ◽

Live Bacteria

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Comparative Effectiveness and Injury to Tomato Plants of Three Neotropical Mirid Predators of Tuta absoluta (Lepidoptera: Gelechiidae)

Journal of Economic Entomology ◽

10.1093/jee/toy057 ◽

2018 ◽

Vol 111 (3) ◽

pp. 1080-1086 ◽

Cited By ~ 5

Author(s):

Joop C van Lenteren ◽

V H P Bueno ◽

F J Calvo ◽

Ana M Calixto ◽

Flavio C Montes

Keyword(s):

Comparative Effectiveness ◽

Tuta Absoluta ◽

Tomato Plants

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L2MXception: An Improved Xception Network for Classification of Peach diseases

10.21203/rs.3.rs-31469/v2 ◽

2020 ◽

Author(s):

Na Yao ◽

Fuchuan Ni ◽

Ziyan Wang ◽

Jun Luo ◽

Wing-Kin Sung ◽

...

Keyword(s):

Deep Learning ◽

Early Identification ◽

Image Data ◽

Yield Reduction ◽

Disease Prediction ◽

Imaging Data ◽

Regularization Term ◽

Detection And Identification ◽

L2 Norm

Abstract Background: Peach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach diseases using imaging data. However, peach disease image data is difficult to collect and samples are imbalance. The popular deep networks perform poor for this issue.Results: This paper proposed an improved Xception network named as L2MXception which ensembles regularization term of L2-norm and mean. With the peach disease image dataset collected, results on seven mainstream deep learning models were compared in details and an improved loss function was integrated with regularization term L2-norm and mean (L2M Loss). Experiments showed that the Xception model with L2M Loss outperformed the current best method for peach disease prediction. Compared to the original Xception model, the validation accuracy of L2MXception was up to 93.85%, increased by 28.48%. Conclusions: The proposed L2MXception network may have great potential in early identification of peach diseases.

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Parasitism of Tuta absoluta in tomato plants by Trichogramma pretiosum Riley in response to host density and plant structures

Ciência Rural ◽

10.1590/s0103-84782008000600002 ◽

2008 ◽

Vol 38 (6) ◽

pp. 1504-1509 ◽

Cited By ~ 27

Author(s):

Cristina Arantes Faria ◽

Jorge Braz Torres ◽

Adriana Maria Vieira Fernandes ◽

Angela Maria Isidro Farias

Keyword(s):

Host Density ◽

Tuta Absoluta ◽

Plant Canopy ◽

Trichogramma Pretiosum ◽

Parasitism Rate ◽

Tomato Plants ◽

Processing Tomato ◽

Upper Third ◽

Oviposition Sites ◽

Host Patch

One important factor determining the efficacy of parasitoids is the way they exploit different host patch. This study evaluated the response of females of Trichogramma pretiosum Riley (Hymenoptera: Trichogrammatidae) to the oviposition sites of Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) on processing tomato plants. In fully developed caged tomato plants T. absoluta moths were released, followed by the release of T. pretiosum females 12h later. After 24h of parasitoid release, the moth oviposition sites were mapped according to the plant canopy, and levels of parasitism assessed. The parasitism rate varied from 1.5 to 28%. There was not influence of plant structures on parasitism, except for the absence of parasitism on the plant apex. Levels of both T. absoluta oviposition and parasitism by T. pretiosum were higher on the upper third of the plant, decreasing downward along the plant canopy.

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Increased water salinity applied to tomato plants accelerates the development of the leaf miner Tuta absoluta through bottom-up effects

Scientific Reports ◽

10.1038/srep32403 ◽

2016 ◽

Vol 6 (1) ◽

Cited By ~ 14

Author(s):

Peng Han ◽

Zhi-jian Wang ◽

Anne-Violette Lavoir ◽

Thomas Michel ◽

Aurélie Seassau ◽

...

Keyword(s):

High Salinity ◽

Saline Water ◽

Development Rate ◽

Tuta Absoluta ◽

Water Salinity ◽

Leaf Miner ◽

Bottom Up ◽

Tomato Plants ◽

Tomato Leaf Miner ◽

Pupal Mass

Abstract Variation in resource inputs to plants may trigger bottom-up effects on herbivorous insects. We examined the effects of water input: optimal water vs. limited water; water salinity: with vs. without addition of 100 mM NaCl; and their interactions on tomato plants (Solanum lycopersicum), and consequently, the bottom-up effects on the tomato leaf miner, Tuta absoluta (Meytick) (Lepidoptera: Gelechiidae). Plant growth was significantly impeded by limited water input and NaCl addition. In terms of leaf chemical defense, the production of tomatidine significantly increased with limited water and NaCl addition, and a similar but non-significant trend was observed for the other glycoalkaloids. Tuta absoluta survival did not vary with the water and salinity treatments, but the treatment “optimal water-high salinity” increased the development rate without lowering pupal mass. Our results suggest that caution should be used in the IPM program against T. absoluta when irrigating tomato crops with saline water.

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