Abstract:
"
Agriculture has long played a vital role in Sri Lankan culture, and cassava crop cultivation plays
a big role in the country's economy. Farmers are currently accustomed to using old procedures
and approaching officers to diagnose ailments. Most of the time, due to the complexity of the
disease, the time it takes to diagnose it is too long, resulting in too late to minimize the losses,
and occasionally a large number of chemicals are required to recover the culture. Cassava
Bacterial Blight, Cassava Brown Streak Disease, Cassava Green Mottle, and Cassava Mosaic
Disease are currently a collection of common illnesses that are wreaking havoc the harvests.
Several existing algorithms were examined, and the optimal technique for this task was chosen,
which is the correct diagnosis of four diseases based on data collected by several government
institutes involved in cassava farming. Because reaching out to officials is also hampered by the
present COVID-19 Pandemic, the project's results will secure farmers' ability to detect cassava
leaf diseases without relying on outsiders. With the assistance of officials, an existing dataset
including the diseases stated was located and processed for this assignment. The approach of
using a CNN model named and keras, which is training using Inception V3s mode. The solution
is then embedded in an web application that allows uploading images directly from the computer
device, allowing for precise and quick visual identification as well as detailed instructions on the
diseases that affect cassava harvest each year. The author was able to successfully build the
requested solution, which allows for easy differentiation between the targeted diseases and also
includes community elements that can be used to contact officials."
