Table 1 Certain advancements and their limitations in the plant disease detection model.

Moupojou et al.¹⁹

Deep learning models

It has the capability to identify the impact on the individual leaves

It has also assured the quality of the entire detection process

But the modelling of a global ensemble, along with the segmentation process, is limited in this model

Saleem et al.²⁰

Deep learning

This method has been embedded into the robotic system for deploying over the disease control approach

It has also been utilized for developing a cost-effective protection system

But, more in-depth validation needs to be explored to strengthen the process

Patil et al.²¹

CNN

The complex background of the images is removed using this model

The model needs to be extended for detecting nutritional deficiency

Hosny et al.²²

CNN

This model has the ability to offer better determination with accurate outcomes for detection

It required less number of parameters

But the application of practical crop disease detection is restricted

Amin et al.²³

CNN

It has acquired small parameters for retrieving the features as well as integrated the sets of features that offer more robustness to the given model

Detecting the disease by means of a digital imaging process needs to be developed

Vishnoi et al.²⁴

CNN

To carry out the identification process with the aid of the leaf image

It is more consistent as well as reliable

Better image variability is required

Zhao et al.²⁵

DoubleGAN

It has been effectively utilized in the field of image generation

It has also detected unhealthy leaves easily

But, the high-resolution images along with less number of samples are limited

Ahmad et al. ²⁶

Ensemble

This method has played an essential role in enhancing the entire performance

It has offered better feasibility

But it needs to be improved for practical applications