scholarly journals SE-CapsNet: Automated evaluation of plant disease severity based on feature extraction through Squeeze and Excitation (SE) networks and Capsule networks

2021 ◽  
Vol 49 (1) ◽  
Author(s):  
Shradha Verma ◽  
◽  
Anuradha Chug ◽  
Ravinder P. Singh ◽  
Amit P. Singh ◽  
...  
Keyword(s):  
Disease Severity ◽  
Image Size ◽  
Feature Maps ◽  
Species Classification ◽  
Tomato Crop ◽  
Automated Evaluation ◽  
Proposed Model ◽  
Late Blight Disease ◽  
Improved Performance ◽  
Blight Disease

Diseases in plants harm the quantity of the overall food production as well as the quality of the yield. Early detection, diagnosis and treatment can greatly reduce losses, both economic and ecological. Intuitively, reduction in the use of agrochemicals due to timely detection of the disease, would greatly help in mitigating the environmental impact. In this paper, the authors have proposed an improved feature computation approach based on Squeeze and Excitation (SE) Networks, before processing by the original Capsule networks (CapsNet) for classification, for estimating the disease severity in plants. Two SE networks, one based on AlexNet and another on ResNet have been combined with Capsule networks. Leaf images for the devastating Late Blight disease occurring in the Tomato crop have been utilized from the PlantVillage dataset. The images, divided into four severity stages i.e. healthy, early, middle and end, are downscaled, enhanced and given as input to the SE networks. The feature maps generated from the two networks are separately given as input to the Capsule Network for classification and their performances are compared with the original CapsNet, on two image sizes 32X32 and 64X64. SE-Alex-CapsNet achieves the highest accuracy of 92.1% and SE-Res CapsNet achieves the highest accuracy of 93.75% with 64X64 image size, as compared to CapsNet that results in 85.53% accuracy. The classification accuracies of six state-of-the-art CNN models namely AlexNet, SqueezeNet, ResNet50, VGG16, VGG19 and Inception V3 are also presented for comparison purposes. Accuracy as well as precision, recall, F1-score, validation loss etc. measures have been recorded and compared. The findings have been validated by implementing the proposed approaches with another dataset, achieving similar resultant accuracy measures. The implementation was also accomplished with datasets after noise addition in six different variations, to verify the robustness of the proposed model. Based on the performances, the proposed techniques can be exploited for disease severity assessment in other crops as well and can be extended to other areas of applications such as plant species classification, weed identification etc. In addition to improved performance, with reduced image size, the proposed methodology can be utilized to create a mobile application requiring low processing capabilities, to be installed on reasonably priced smartphones for practical usage by farmers.

scholarly journals Antagonistic effects of biocontrol agents against Phytophthora infestans and growth stimulation in tomatoes

2020 ◽  
Vol 28 (s1) ◽  
pp. 55-70
Author(s):  
W.G. Kariuki ◽  
N.W. Mungai ◽  
D.O. Otaye ◽  
M. Thuita ◽  
E. Muema ◽  
...  
Keyword(s):  
Late Blight ◽  
Phytophthora Infestans ◽  
Disease Severity ◽  
Greenhouse Experiment ◽  
Biocontrol Agents ◽  
Eastern Africa ◽  
Commercial Products ◽  
Antagonistic Effects ◽  
Late Blight Disease ◽  
Blight Disease

Late blight disease is a major cause of economic losses in tomato (Lycopersicon esculentum L.) production in eastern Africa. The objective of this study was to evaluate the efficacy of Trichoderma species in controlling late blight disease and their role on the growth of tomato. Trichoderma asperellum and T. harzianum were isolated from two commercial products containing the antagonistic species. Culture-based and molecular approaches, genomic DNA isolation and amplification, using ITS1 and ITS4 universal primers, and sequencing, were used to characterise the products. Trichoderma antagonistic effects against Phytophthora infestans (causative of tomato late blight) experiments were conducted in vitro and in the greenhouse. The greenhouse experiment had five treatments; namely, a negative control, Metalaxl-M, T. asperellum, T. harzanium and mixture of the two biocontrol agents, laid out in a randomised complete block design. The experiment was carried out for 12 weeks, with 3 weeks measurements intervals. Morphological and molecular characterisation confirmed the organism in most of the commercial products as T. harzianum and T. asperellum. An inhibiting action was observed on the P. Infestans mycelial growth, by the effect of T. asperellum (30.7%), and T. harzianum (36.9%).Trichoderma spp. suppressed late blight disease in the greenhouse experiment. These effects were specific to soil type, with the higher effectiveness realised in Ferralsols (27% disease severity) and least in Nitisols (36% disease severity). Trichoderma harzianum and T. asperellum resulted in higher above ground biomass of tomato of 31 and 19% increase over the control, respectively. There is potential of biocontrol agents in reducing P. infestans effects in tomatoes and in stimulating growth.

Evaluation of bio-agents and neem based products against late blight disease (Phytophthora infestans) of potato

2021 ◽  
Vol 74 (1) ◽  
pp. 181-187
Author(s):  
Mehi Lal ◽  
Sorabh Chaudhary ◽  
Sanjay Rawal ◽  
Sanjeev Sharma ◽  
Manoj Kumar ◽  
...  
Keyword(s):  
Late Blight ◽  
Phytophthora Infestans ◽  
Late Blight Disease ◽  
Blight Disease

Two new defensive constituents from potato tubers (Solanum tuberosum)

2017 ◽  
Vol 72 (6) ◽  
pp. 393-396
Author(s):  
Liangyan Liu ◽  
Jun Han ◽  
Yong Shen
Keyword(s):  
Solanum Tuberosum ◽  
Late Blight ◽  
Quantum Chemical ◽  
Spectroscopic Analysis ◽  
2D Nmr ◽  
Moderate Activity ◽  
Potato Tubers ◽  
Mycelia Growth ◽  
Late Blight Disease ◽  
Blight Disease

AbstractTwo new defensive constituents, solatuberenol A (1) and 3-O-β-d-glucopyranosyl stigmasta-5(6),24(28)-diene (2), were isolated from the potato tubers (Solanum tuberosum) infected with late blight disease. Their structures were identified by extensive spectroscopic analysis, including HRMS, IR, UV, 1D/2D NMR, ECD and quantum chemical calculations. Compounds 1 and 2 showed moderate activity against Phytophthora infestans with mycelia-growth inhibition of 30.1% and 52.4%, respectively, at the concentration of 500 ppm.

An Object-Based Image Reducing Approach

2014 ◽  
Vol 1044-1045 ◽  
pp. 1049-1052 ◽  
Author(s):  
Chin Chen Chang ◽  
I Ta Lee ◽  
Tsung Ta Ke ◽  
Wen Kai Tai
Keyword(s):  
Visual Saliency ◽  
Saliency Map ◽  
Input Image ◽  
Image Size ◽  
Target Image ◽  
Feature Maps ◽  
Object Based ◽  
Wide Range

Common methods for reducing image size include scaling and cropping. However, these two approaches have some quality problems for reduced images. In this paper, we propose an image reducing algorithm by separating the main objects and the background. First, we extract two feature maps, namely, an enhanced visual saliency map and an improved gradient map from an input image. After that, we integrate these two feature maps to an importance map. Finally, we generate the target image using the importance map. The proposed approach can obtain desired results for a wide range of images.

scholarly journals Phenotypic Characterization of Recent Clonal Lineages of Phytophthora infestans in the United States

Plant Disease ◽  
2013 ◽  
Vol 97 (7) ◽  
pp. 873-881 ◽  
Author(s):  
G. Danies ◽  
I. M. Small ◽  
K. Myers ◽  
R. Childers ◽  
W. E. Fry
Keyword(s):  
United States ◽  
Late Blight ◽  
Phytophthora Infestans ◽  
The United States ◽  
Phenotypic Characterization ◽  
Phenotypic Traits ◽  
Mating Types ◽  
Late Blight Disease ◽  
Blight Disease

Phytophthora infestans, the causal agent of late blight disease, has been reported in the United States and Canada since the mid-nineteenth century. Due to the lack of or very limited sexual reproduction, the populations of P. infestans in the United States are primarily reproducing asexually and, thus, show a simple genetic structure. The emergence of new clonal lineages of P. infestans (US-22, US-23, and US-24) responsible for the late blight epidemics in the northeastern region of the United States in the summers of 2009 and 2010 stimulated an investigation into phenotypic traits associated with these genotypes. Mating type, differences in sensitivity to mefenoxam, differences in pathogenicity on potato and tomato, and differences in rate of germination were studied for clonal lineages US-8, US-22, US-23, and US-24. Both A1 and A2 mating types were detected. Lineages US-22, US-23, and US-24 were generally sensitive to mefenoxam while US-8 was resistant. US-8 and US-24 were primarily pathogenic on potato while US-22 and US-23 were pathogenic on both potato and tomato. Indirect germination was favored at lower temperatures (5 and 10°C) whereas direct germination, though uncommon, was favored at higher temperatures (20 and 25°C). Sporangia of US-24 released zoospores more rapidly than did sporangia of US-22 and US-23. The association of characteristic phenotypic traits with genotype enables the prediction of phenotypic traits from rapid genotypic analyses for improved disease management.

The Assessment of Potato Late Blight Disease in Western Amhara Region, Ethiopia

2021 ◽  
Author(s):  
Gebremariam Asaye Emrie ◽  
Merkuz Abera Admassu ◽  
Adane Tesfaye Lema
Keyword(s):  
Late Blight ◽  
Potato Late Blight ◽  
Amhara Region ◽  
Late Blight Disease ◽  
Blight Disease

scholarly journals Evaluation of various plant extracts against the early blight disease of tomato plants under greenhouse and field conditions

2012 ◽  
Vol 48 (No. 2) ◽  
pp. 74-79 ◽  
Author(s):  
S.M.A. Nashwa ◽  
K.A.M. Abo-Elyousr
Keyword(s):  
Disease Severity ◽  
Plant Extracts ◽  
Mycelial Growth ◽  
Early Blight ◽  
Field Conditions ◽  
Nerium Oleander ◽  
Tomato Plants ◽  
Infected Control ◽  
Blight Disease

The antimicrobial activity of six plant extracts from Ocimum basilicum (Sweat Basil), Azadirachta indica (Neem), Eucalyptus chamadulonsis (Eucalyptus), Datura stramonium (Jimsonweed), Nerium oleander (Oleander), and Allium sativum (Garlic) was tested for controlling Alternaria solani in vitro and in vivo. In in vitro study the leaf extracts of D. stramonium, A. indica, and A. sativum at 5% concentration caused the highest reduction of mycelial growth of A. solani (44.4, 43.3 and 42.2%, respectively), while O. basilicum at 1% and 5% concentration and N. oleander at 5% concentration caused the lowest inhibition of mycelial growth of the pathogen. In greenhouse experiments the highest reduction of disease severity was achieved by the extracts of A. sativum at 5% concentration and D. stramonium at 1% and 5% concentration. The greatest reduction of disease severity was achieved by A. sativum at 5% concentration and the smallest reduction was obtained when tomato plants were treated with O. basilicum at 1% and 5% concentration (46.1 and 45.2 %, respectively). D. stramonium and A. sativum at 5% concentration increased the fruit yield by 76.2% and 66.7% compared to the infected control. All treatments with plant extracts significantly reduced the early blight disease as well as increased the yield of tomato compared to the infected control under field conditions.

scholarly journals Machine Learning and Prediction-Based Resource Management in IoT Considering Qos

2019 ◽  
Vol 8 (2) ◽  
pp. 687-694
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Short Term Memory ◽  
Real Data ◽  
Computing Methods ◽  
Energy Utilization ◽  
Real Field ◽  
Proposed Model ◽  
Improved Performance ◽  
Video Sensors

Internet of Things (IoT) is one of the fast-growing technology paradigms used in every sectors, where in the Quality of Service (QoS) is a critical component in such systems and usage perspective with respect to ProSumers (producer and consumers). Most of the recent research works on QoS in IoT have used Machine Learning (ML) techniques as one of the computing methods for improved performance and solutions. The adoption of Machine Learning and its methodologies have become a common trend and need in every technologies and domain areas, such as open source frameworks, task specific algorithms and using AI and ML techniques. In this work we propose an ML based prediction model for resource optimization in the IoT environment for QoS provisioning. The proposed methodology is implemented by using a multi-layer neural network (MNN) for Long Short Term Memory (LSTM) learning in layered IoT environment. Here the model considers the resources like bandwidth and energy as QoS parameters and provides the required QoS by efficient utilization of the resources in the IoT environment. The performance of the proposed model is evaluated in a real field implementation by considering a civil construction project, where in the real data is collected by using video sensors and mobile devices as edge nodes. Performance of the prediction model is observed that there is an improved bandwidth and energy utilization in turn providing the required QoS in the IoT environment.

scholarly journals Effects of Tomato Leaf Curl Virus on Growth and Yield Parameters of Tomato Crop

2021 ◽  
Vol 39 (1) ◽  
pp. 79-83
Author(s):  
Yasir Iftikhar ◽  
◽  
Mustansar Mubeen ◽  
Ashara Sajid ◽  
Mohamed Ahmad Zeshan ◽  
...  
Keyword(s):  
Disease Severity ◽  
Disease Incidence ◽  
Tomato Leaf ◽  
Growth And Yield ◽  
Tomato Crop ◽  
Tomato Leaf Curl Virus ◽  
Yield Parameters ◽  
Tomato Leaf Curl ◽  
Leaf Curl Virus ◽  
Leaf Curl

Iftikhar, Y., M. Mubeen, A. Sajid, M.A. Zeshan, Q. Shakeel, A. Abbas, S. Bashir, M. Kamran and H. Anwaar. 2021. Effects of Tomato Leaf Curl Virus on Growth and Yield Parameters of Tomato Crop. Arab Journal of Plant Protection, 39(1): 79-83. Tomato is an important vegetable crop, belongs to the family Solanaceae and is the second most consumed vegetable following potatoes. The tomato crop is grown all over the world in both summer and winter seasons, and plant viruses are a major threat to tomato production. Among these viruses, tomato leaf curl virus (TLCV) causes considerable yield loss to tomato crop. This virus is transmitted by a whitefly (Bemisia tabaci) vector. In this study, the effect of TLCV infection, on the following tomato growth and yield parameters, was evaluated: plant leaf number and area, plant biomass, plant height, root length, and plant stem diameter and yield. Tomato plants were transplanted in wellprepared plots with 4 replications. The control group was covered with polyethene bag to avoid whitefly infestation. Plants were scored on the 15th and 30th day after inoculation and TLCV disease severity was recorded. Analysis of variance (ANOVA) showed the significant differences between the healthy and infected tomato plants. Moreover, growth and yield parameters were reduced with the increase in disease incidence, disease severity and whitefly infestation. Disease severity was increased with the increase in temperature during the growing season. It can be concluded from this study that TLCV significantly affects growth and yield of the tomato crop. Keywords: Tomato, Tomato leaf curl virus, TLCV, disease incidence, disease severity.

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