Convolutional Neural Networks for the Detection of Multiclass Plant Diseases


Authors : Dona Joby; Christeena Shaju; Fasna T A; Avani Das

Volume/Issue : Volume 7 - 2022, Issue 5 - May

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3HZ2NnS

DOI : https://doi.org/10.5281/zenodo.6734744

Most industrialised countries' economies are based on agriculture. Crop production is one of the most influential factors in a country's domestic market scenario. Agricultural output is also a crucial component of every country's economic development. Agriculture is critical because it offers raw materials, work, and food to a diverse population. Overuse of chemical fertilisers, pollution of water supplies with chemicals, irregular rainfall patterns, shifting soil fertility, and other factors are among them. Apart from these challenges, diseaserelated loss of a significant section of output is one of the most prominent roadblocks across the world. The presence of illnesses in the grown plants decreases a major share of the yield after delivering efficient resources to the fields. As a result, scientists have been working on a new project. As a result, scientists are focused their efforts on developing effective ways for detecting illness in plants. Plant diseases are a major problem for small-scale farmers because they disrupt the food supply. To provide efficient processes for diagnosis and avoidance of destruction, it is necessary to identify the kind of plant disease existing as soon as possible. Significant progress has been achieved in discovering plant diseases that impact a range of crops in different regions of the world in recent years. Image capture, preprocessing, and segmentation are all steps in the process of detecting plant diseases. It's additionally enhanced by a number of feature extraction and classification methods.

Keywords : Plant disease, VGG16, InceptionV3, Resnet50, Hybrid model.

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