| 000 | a | ||
|---|---|---|---|
| 999 |
_c17493 _d17493 |
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| 003 | OSt | ||
| 005 | 20220912100257.0 | ||
| 008 | 220912b xxu||||| |||| 00| 0 eng d | ||
| 040 |
_aAIKTC-KRRC _cAIKTC-KRRC |
||
| 100 |
_917849 _aGolla, Mahalaxmi |
||
| 245 | _aCategorization of leaf ailments using deep learning techniques: a review | ||
| 250 | _aVol.14(1), Feb | ||
| 260 |
_aHyderabad _bIUP Publications _c2022 |
||
| 300 | _a50-65p. | ||
| 520 | _aComputerized image processing techniques are extremely useful in agriculture. The technology can help detect plant diseases and improve cultivation quality. The study examines the advantages and disadvantages of previous research on the subject. To find the most effective image processing methods for diagnosing plant diseases, cutting-edge techniques are examined. To find plant pathogens, many computerized image processing methods are used. This review compares the results and many different approaches to develop algorithms such as Support Vector Machines (SVM) and Deep Learning Neural Networks (DLNN), which are important in the detection and classification of leaf diseases. | ||
| 650 | 0 |
_94619 _aEXTC Engineering |
|
| 700 |
_917848 _aTirupal, T. |
||
| 773 | 0 |
_dHyderabad IUP Publications _x0975-5551 _tIUP Journal of telecommunications |
|
| 856 |
_uhttps://iupindia.in/0222/Telecommunications/Categorization_of_Leaf.asp _yClick here |
||
| 942 |
_2ddc _cAR |
||