Detection and Classification of Mosaic Virus Disease in Cassava Plants by Proximal Sensing of Photochemical Reflectance Index

Raji, Sadasivan Nair; Subhash, Narayanan; Ravi, V.; Saravanan, R.; Mohanan, Changatharayil N.; Makeshkumar, T.; Sukumar, Nita

doi:10.1007/s12524-016-0565-6

Cited by 18 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use with Photochemical Reflectance Indicator (PRI) images to identify and evaluate the effects of different degrees of CMD infection in cassava was examined by Nair et al [7] in 2016. Narrow band reflectance photos of cassava plants cultivated in the field were taken in this regard using proximate sensing and multispectral imaging systems (MSIS) at 531 and 571 nm.…”

Section: Symptomsmentioning

confidence: 99%

Comprehensive Review on Plant Disease Detection and Identification

Indrabhan S. Borse

2024

jes

View full text Add to dashboard Cite

Image Agriculture is a major contributor to financial development and is the main source of income in many countries. The primary goal of disease prevention and treatment, notwithstanding the different challenges farmers face, including plant diseases, is to precisely identify and evaluate the disease while the plant is still growing. Object recognition and classification in images has been made possible by rapid advancements in deep learning, or DL, techniques. Recently, DL approaches have been applied to farming and other agricultural applications and have showed potential in a variety of areas. These plants are commonly affected by diseases such as Septoria leaf spots, bacterial spots, late plant blight, and curled yellow leaves. Our work presents a hybrid approach to early illness detection that applies region-based full convolutional networks (RFCN) for early disease identification and region-based convolutional neural networks (RCNN) for diseases classification. We construct a network model based on the EfficientNetB7 model that optimizes at several scales via dilated convolution. The network is constructed layer by layer, top-down, with several optimizations. Real-time crop images taken from crop fields are used in the experiment, and Efficient Net B7 and hybrid convolution models are used. When the suggested ensemble model is implemented in Python, the outcome demonstrates that it has high accuracy and low loss when compared to other current methodologies.

show abstract

Section: Symptomsmentioning

confidence: 99%

Comprehensive Review on Plant Disease Detection and Identification

Indrabhan S. Borse

2024

jes

View full text Add to dashboard Cite

show abstract

“…FCM clustering is nothing but an unsupervised clustering mechanism. It entails one input vector to distinct clusters concerning its objective function in Equation (5).…”

Section: Fcm-based Segmentationmentioning

confidence: 99%

“…The above‐mentioned strategies are extremely useful for the plant disease diagnosis system to attain high efficiency in detecting and diagnosing particular diseases in the real‐world applications of the cultivation yield. Moreover, it is said to be a user‐friendly, easy‐to‐use, and appropriate mobile application necessary for diagnosing plant diseases 5,6 . The nomenclature for the proposed methodology is given in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Adaptive leaf abnormality segmentation and improved plant disease classification by tuned long short‐term memory with recurrent neural network

Y.¹,

Rao

Veeresh

et al. 2023

Concurrency and Computation

View full text Add to dashboard Cite

The conventional models suffer in providing significant accuracy and detection speed, especially in detecting apple diseases, to assure the healthy development of the apple industry. The main aspect of the proposed scheme is to present a new plant disease classification using intelligent segmentation and classification models. The adaptive leaf abnormality segmentation is enhanced by the solution index-based Jaya-Krill Herd optimization (SI-JKHO). Further, the feature extraction is accomplished using "gray level co-occurrence matrix, hybrid local binary pattern with local gradient patterns," color features, and shape features. These collected features are subjected to feature selection using principle component analysis, in which the optimal features are obtained that are further considered to "tuned long short-term memory with recurrent neural network (T-LSRNN)." From the validating results, the performance of the enhanced-JKHO-LSRNN method is correspondingly secured at 6.66%, 6.66%, 7.86%, and 4.34% higher enriched performance RNN, long short-term memory, convolutional neural networks, and LSRNN at 35th learning percentage in dataset 3. The results confirm that the developed model can accurately determine plant diseases compared to conventional models based on diverse performance metrics like "accuracy, precision, specificity, sensitivity, false positive rate, and false negative rate," and so forth. K E Y W O R D Sadaptive fuzzy C-means clustering with thresholding, plant disease classification, solution index-based Jaya-Krill Herd optimization, tuned long short-term memory with recurrent neural network INTRODUCTIONPlant disease can be diagnosed through the plant leaves, which involve more complexities while considering optical observation. 1 Due to the increasing cultivation of plants, the complexities also increase along with the conventional phytopathological problems. Although experienced agronomists and pathologists diagnose plant diseases, sometimes it causes failures in diagnosing particular plant diseases and leads to wrong conclusions and further mistaken treatments. 2 The existing automatic computational system has offered significant support to the agronomist in detecting and diagnosing plant diseases while subjecting the optical observation of infected plants through their leaves. 3 Here, the developed system has to be simple

show abstract

“…In susceptible cultivars, CMD has caused yield losses up to 82% [ 11 ] by significantly reducing the number and size of tubers. However, the severity of the damage is determined by the strain of the virus, cassava variety, age and sensitivity of the host, and environmental factors such as soil fertility [ 12 ]. CMD presents itself with symptoms of yellow and/or pale green chlorotic mosaic of leaves accompanied by distortion and an overall reduction in leaf size [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Proteome Mapping of South African Cassava Mosaic Virus-Infected Susceptible and Tolerant Landraces of Cassava

Ramulifho

Rey

2021

Proteomes

View full text Add to dashboard Cite

The production of cassava is threatened by the geminivirus South African cassava mosaic virus (SACMV), which causes cassava mosaic disease. Cassava landrace TME3 shows tolerance to SACMV, while T200 is highly susceptible. This study aimed to identify the leaf proteome involved in anti-viral defence. Liquid chromatography mass spectrometry (LC-MS) identified 2682 (54 differentially expressed) and 2817 (206 differentially expressed) proteins in both landraces at systemic infection (32 days post infection) and symptom recovery (67 days post infection), respectively. Differences in the number of differentially expressed proteins (DEPs) between the two landraces were observed. Gene ontology analysis showed that defence-associated pathways such as the chloroplast, proteasome, and ribosome were overrepresented at 67 days post infection (dpi) in SACMV-tolerant TME3. At 67 dpi, a high percentage (56%) of over-expressed proteins were localized in the chloroplast in TME3 compared to T200 (31% under-expressed), proposing that chloroplast proteins play a role in tolerance in TME3. Ribosomal_L7Ae domain-containing protein (Manes.12G139100) was over-expressed uniquely in TME3 at 67 dpi and interacts with the ribosomal protein Sac52 (RPL10). RPL10 is a known key player in the NIK1-mediated effector triggered immunity (ETI) response to geminivirus infection, indicating a possible role for Sac52 in SACMV recovery in TME3. In conclusion, differential protein expression responses in TME3 and T200 may be key to unravel tolerance to CMD.

show abstract

Detection and Classification of Mosaic Virus Disease in Cassava Plants by Proximal Sensing of Photochemical Reflectance Index

Cited by 18 publications

References 27 publications

Comprehensive Review on Plant Disease Detection and Identification

Comprehensive Review on Plant Disease Detection and Identification

Adaptive leaf abnormality segmentation and improved plant disease classification by tuned long short‐term memory with recurrent neural network

Proteome Mapping of South African Cassava Mosaic Virus-Infected Susceptible and Tolerant Landraces of Cassava

Contact Info

Product

Resources

About