Disease Detection in Plant using Artificial Neural Network

Gupta, Deepti

doi:10.22214/ijraset.2019.6434

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…In 2019, Gupta and colleagues [14] identified diseases in citrus plants, employing artificial neural networks (ANN) for classification. They utilized a dataset comprising 60 images and employed image processing to extract and categorize these diseases.…”

Section: Annmentioning

confidence: 99%

Intelligent Plant Disease Diagnosis Using Machine Learning Techniques- A Review

Kem,

Chauhan,

Agnihotri

et al. 2024

IJIRCST

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No matter how we look at it, there are no chances to live without the flora surrounding us. One can be troubled by a broad range of diseases that attack the integrity of his or her health. Basically, all the plant parts are fruits, stems, roots, leaves, and so on. The time and money in terms of having successfully figured out the disease of a plant are much less than that if a diagnosis error has been made. Sustained economic losses caused by plant disease are due to the facilitation of rot production, which involves the reduction of agricultural product yields and quantities. Creating measures that would halt the destruction of crops due to plant diseases is essential since the contributing factor of 70% of agricultural produce to GDP is high. This group of illnesses must be watched closely since the diseases start as soon as the plants have begun their growing process. The conventional approach to surveillance at this point specifically is to carry out an examination, which is quite costly in terms of money. Automated for faster and more effective processing of this operation. Many researchers, by using various methods, have created networks that are mostly exemplified in diverse forms. It is also worthwhile to note that in the field of agriculture, it is very important that the plants are sorted by type. Diagnosis on pathology datasets with the aid of image feature extraction and transformation methods that are appropriate to the illness.

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Section: Annmentioning

confidence: 99%