Abstract:
The use of deep learning in the automation of banana fruit disease diagnosis
is a new technique that has been used to deal with a major problem in
agriculture and food safety. Little or poorly identified diseases can cause
significant losses after harvest, low prices at the market, and dissatisfaction
of customers. In this study, it is proposed to apply the most effective deep
learning methods to banana fruit images to detect the various types of
diseases and distinguish between diseased and healthy fruits. Various
convolutional neural network (CNN) models were investigated, but the
DenseNet121 model presented the most beneficial results. In this model the
accuracy was impressive (99.12) on the first dataset and 98.42 on the second
dataset in determining the disease types of banana fruits. The suggested
method deals with the requirement of scalable, fast, and accurate solutions in
agricultural supply chains, in particular, in the regions where human
inspection is either unreliable or absent. Despite the existing shortcomings,
including the fluctuating climate, disease presentation variations, and
absence of data with annotations, the study demonstrates the prospective of
AI-based systems to transform the assessment of agricultural illnesses. Our
solution provides a cost effective and user friendly application with which
farmers, retailers and consumers can make an informed decision. This study
improves timely detection and management of disease as well as timely
response thus reducing agricultural losses and increasing food distribution
channels. The combination of AI-based technologies and mobile technology
would enhance the diagnosis of diseases in isolated farmlands. This
technology can be used to enhance food security throughout the world and in
the long-term sustainable agricultural processes.
