Nanodiagnostics Tools for Microbial Pathogenic Detection in Crop Plants

Álvarez, Sandra Pérez; Magallanes-Tapia, Marco Antonio; Medina, J.; Ardisana, Eduardo Fidel Héctor; Vega, María Esther González

doi:10.1007/978-3-319-99570-0_16

Cited by 2 publications

(1 citation statement)

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“…References [37][38][39][40] introduced new technique for leaf categorization using DL on the limited datasets. Reference [41] offers a range of approaches to discuss, optimization and allow multidisciplinary ML studies in the healthcare informatics. References [42][43][44][45] explored that practicality and probability of the pre-symptomatic tobacco disease identification utilizing hyperspectral analysis, together with technique for variable preference and ML.…”

Section: Related Workmentioning

confidence: 99%

Fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks

Amin¹,

Anjum²,

Sharif³

et al. 2022

Computers, Materials &Amp; Continua

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As they have nutritional, therapeutic, so values, plants were regarded as important and they're the main source of humankind's energy supply. Plant pathogens will affect its leaves at a certain time during crop cultivation, leading to substantial harm to crop productivity & economic selling price. In the agriculture industry, the identification of fungal diseases plays a vital role. However, it requires immense labor, greater planning time, and extensive knowledge of plant pathogens. Computerized approaches are developed and tested by different researchers to classify plant disease identification, and that in many cases they have also had important results several times. Therefore, the proposed study presents a new framework for the recognition of fruits and vegetable diseases. This work comprises of the two phases wherein the phase-I improved localization model is presented that comprises of the two different types of the deep learning models such as You Only Look Once (YOLO)v2 and Open Exchange Neural (ONNX) model. The localization model is constructed by the combination of the deep features that are extracted from the ONNX model and features learning has been done through the convolutional-05 layer and transferred as input to the YOLOv2 model. The localized images passed as input to classify the different types of plant diseases. The classification model is constructed by ensembling the deep features learning, where features are extracted dimension of 1 × 1000 from pre-trained Efficientnetb0 model and supplied to next 07 layers of the convolutional neural network such as 01 features input, 01 ReLU, 01 Batch-normalization, 02 fully-connected. The proposed model classifies the plant input images into associated labels with approximately 95% prediction scores that are far better as compared to current published work in this domain.

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