Analysis of New RGB Vegetation Indices for PHYVV and TMV Identification in Jalapeño Pepper (Capsicum annuum) Leaves Using CNNs-Based Model

Yee-Rendón, Arturo; Torres‐Pacheco, Irineo; Trujillo-Lopez, Angelica Sarahy; Romero-Bringas, Karen Paola; Millán-Almaraz, Jesús Roberto

doi:10.3390/plants10101977

Cited by 7 publications

(5 citation statements)

References 32 publications

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“…However, it was unexpected that, in a recent analysis, symptomatic trees were negative by qRT-PCR (false negatives) (Ochoa-Martínez, personal observations). The development of new techniques, such as the computational predictions of disrupted metabolites in leaf tissue or the implementation of “deep learning” approaches, such as those reported for virus diagnosis [ 93 ], will aid disease detection in the analyzed samples.…”

Section: Asbvd and The Origin Of Avocado Plantsmentioning

confidence: 99%

Mexico: A Landscape of Viroid Origin and Epidemiological Relevance of Endemic Species

Aviña-Padilla

Zamora-Macorra

Ochoa-Martínez

et al. 2022

Cells

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Viroids are single-stranded, circular RNA molecules (234-406 nt) that infect a wide range of crop species and cause economic losses in agriculture worldwide. They are characterized by the existence of a population of sequence variants, attributed to the low fidelity of RNA polymerases involved in their transcription, resulting in high mutation rates. Therefore, these biological entities exist as quasispecies. This feature allows them to replicate within a wide range of host plants, both monocots and dicots. Viroid hosts include economically important crops such as tomato, citrus, and fruit trees such as peach and avocado. Given the high risk of introducing viroids to viroid disease-free countries, these pathogens have been quarantined globally. As discussed herein, Mexico represents a geographical landscape of viroids linked to their origin and comprises considerable biodiversity. The biological features of viroid species endemic to Mexico are highlighted in this communication. In addition, we report the phylogenetic relationships among viroid and viroid strains, their economic impact, geographical distribution, and epidemiological features, including a broad host range and possible long-distance, seed, or insect-mediated transmission. In summary, this review could be helpful for a better understanding of the biology of viroid diseases and future programs on control of movement and spread to avoid economic losses in agricultural industries.

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Section: Asbvd and The Origin Of Avocado Plantsmentioning

confidence: 99%

Mexico: A Landscape of Viroid Origin and Epidemiological Relevance of Endemic Species

Aviña-Padilla

Zamora-Macorra

Ochoa-Martínez

et al. 2022

Cells

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“…To correctly find citrus diseases, it suggests a patch categorization network with an embed system, a cluster module, and a neural network model [12]. Stochastic gradient descent optimizer was used to train [82]. They used MobileNet v2, Inception v3, Xception, and VGG-16, which are all popular deep-learning network architectures.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

Dinesh,

Lakshmanan

2023

IJRITCC

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In Agriculture, orchards are the deciding factor in the country’s economy. There are many orchards, and citrus and sugarcane will cover 60 percent of them. These citrus orchards satisfy the necessity of citrus fruits and citrus products, and these citrus fruits contain more vitamin C. The citrus orchards have had some problems generating good yields and quality products. Pathogenic diseases, pests, and water shortages are the three main problems that plants face. Farmers can find these problems early on with the support of machine learning and deep learning, which may also change how they feel about technology. By doing this in agriculture, the farmers can cut off the major issues of yield and quality losses. This review gives enormous methods for identifying and classifying plant pathogens, pests, and water stresses using image-based work. In this review, the researchers present detailed information about citrus pathogens, pests, and water deficits. Methods and techniques that are currently available will be used to validate the problem. These will include pre-processing for intensification, segmentation, feature extraction, and selection processes, machine learning-based classifiers, and deep learning models. In this work, researchers thoroughly examine and outline the various research opportunities in the field. This review provides a comprehensive analysis of citrus plants and orchards; Researchers used a systematic review to ensure comprehensive coverage of this topic.

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“…The maximum number of epochs was set to 20 and batches of 20 were used in this experiment. Table 2 summarizes the hyperparameters used in our study (Yee-Rendon et al, 2021).…”

Section: Deep Learningmentioning

confidence: 99%

Experimental Evaluation of Coffee Leaf Disease Classification and Recognition Based on Machine Learning and Deep Learning Algorithms

Ayikpa¹,

Diarra²,

Gouton³

et al. 2022

Journal of Computer Science

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Coffee plant diseases constitute a significant danger to world coffee production, and the greatest challenge is to detect these diseases as early as possible to save the crop. Traditional methods are most often based on visual observations, often with errors in diagnosing diseases. Machine Learning has become a tool that presents itself as an alternative for automatically identifying plant diseases. Our study is to implement a robust method of classification and recognition of coffee leaf diseases using both classical ma learning and deep learning methods, so we set up a custom CNN. These methods were evaluated on the Arabica coffee leaf dataset known as JMuBEN. The results of the classical machine learning methods ranged from 81.03 to 100% and the best performance was obtained with SVM and Random Forest; while the deep learning. In comparison, these provided results between 97.37 and 100% with our CNN custom obtaining receiving accuracy with the lowest loss of 0.013%. Accuracy, precision score, recall, and MCC were employed as performance indicators to support this performance.

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Analysis of New RGB Vegetation Indices for PHYVV and TMV Identification in Jalapeño Pepper (Capsicum annuum) Leaves Using CNNs-Based Model

Cited by 7 publications

References 32 publications

Mexico: A Landscape of Viroid Origin and Epidemiological Relevance of Endemic Species

Mexico: A Landscape of Viroid Origin and Epidemiological Relevance of Endemic Species

Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

Experimental Evaluation of Coffee Leaf Disease Classification and Recognition Based on Machine Learning and Deep Learning Algorithms

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