PSFD-Musa: A dataset of banana plant, stem, fruit, leaf, and disease

Medhi, Epsita; Deb, Nabamita

doi:10.1016/j.dib.2022.108427

Cited by 13 publications

(2 citation statements)

References 4 publications

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“…It helps in reducing risk of heart diseases when used regularly and is recommended for patients suffering from high blood pressure, arthritis, ulcer, gastroenteritis and kidney disorders. Banana is considered a foodie or heavy feeder plant, so soil fertility is really important for it [3] .…”

Section: Data Descriptionmentioning

confidence: 99%

A fully labelled image dataset of banana leaves deficient in nutrients

Sunitha¹,

Bhandari²,

Channakeshava³

et al. 2023

Data in Brief

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Section: Data Descriptionmentioning

confidence: 99%

A fully labelled image dataset of banana leaves deficient in nutrients

Sunitha¹,

Bhandari²,

Channakeshava³

et al. 2023

Data in Brief

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“…[16] These algorithms can do this by recognising various diseases and defects in the fruit, as well as by overcoming limitations such as increasing the performance detection rate accuracy and decreasing the computation time. [17] This database is available for free download in its entirety as two separate packages from the repository maintained by the Laboratory of Technological Research in Pattern Recognition located on the campus of the Catholic University of Maule. [18][19]The RGB picture bundle includes 286 raw original photographs from raspberry trays, each with a resolution of 3948 by 2748 pixels.…”

Section: Literature Surveymentioning

confidence: 99%

Construction of binary classification model by ML with IP on the CIFAR10 dataset

Madhanmohan¹,

E.²

2022

INDJCSE

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Learning through machines is currently considered to be one of the most popular topics on a global scale. It is even possible to say this about the modern electricity that is used in the world today. To be more specific about what machine learning is, however, we can say that it is just one method of teaching the machine by providing it with a significant amount of data. Machine Learning algorithms allow you to do image processing at scale, and with great detail. This work finds that both the ASC and the J48 have a high level, in addition to the same level of accuracy, which is 90%; both the ASC and the J48 have a high level, in addition to the same level of precision, which is 0.92. The ASC and J48 both have a high level of performance as well as the same recall level, which is 0.90; the ASC and J48 both have a high level of performance as well as the same ROC level, which is 0.98; and the ASC and J48 both have a high level of performance as well as the same PRC level, which is 0.97. The ASC and J48 both have the same high F-Measure value, which is 0.90; the ASC and J48 both have the same high Kappa value, which is 0.80; the ASC and J48 both have the same high MCC value, which is 0.82; and the ASC and J48 both have the same best performance, which is 0.10 of MAE. Both the ASC and the J48 have achieved the same level of performance, which is RMSE of 0.29. Both the ASC and the J48 have achieved the same best performance, which is equal to 20% of RAE. Both the ASC and the J48 have achieved the same level of performance, which is 57.27% of RRSE. Low-level accuracy, which is 60%, is possessed by the IBK. The precision of the FLDA is only at a low level, which is 0.65. Low level recall, which is 0.60, is exhibited by the IBK. The PRC for the IBK is quite low, coming in at 0.60. IBK has a PRC that is relatively low, coming in at 0.57. The F-Measure for the FLDA is the lowest possible, coming in at 0.55. The Kappa level of the IBK is quite low, coming in at 0.20. The MCC level in the IBK is the lowest possible, coming in at 0.25. The worst performance level for the IBK is 0.41 of MAE; the worst performance level for the IBK is 0.60 of RMSE; the worst performance level for the IBK is 82% of RAE; and the worst performance level for the IBK is 120.42% of RRSE.

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