  Cashew is one of the most extensively consumed nuts in the world, and it is
also known as a cash crop. A tree may generate a substantial yield in a few
months and has a lifetime of around 70 to 80 years. Yet, in addition to the
benefits, there are certain constraints to its cultivation. With the exception
of parasites and algae, anthracnose is the most common disease affecting trees.
When it comes to cashew, the dense structure of the tree makes it difficult to
diagnose the disease with ease compared to short crops. Hence, we present a
dataset that exclusively consists of healthy and diseased cashew leaves and
fruits. The dataset is authenticated by adding RGB color transformation to
highlight diseased regions, photometric and geometric augmentations, and
RaLSGAN to enlarge the initial collection of images and boost performance in
real-time situations when working with a constrained dataset. Further, transfer
learning is used to test the classification efficiency of the dataset using
algorithms such as MobileNet and Inception. TensorFlow lite is utilized to
develop these algorithms for disease diagnosis utilizing drones in real-time.
Several post-training optimization strategies are utilized, and their memory
size is compared. They have proven their effectiveness by delivering high
accuracy (up to 99%) and a decrease in memory and latency, making them ideal
for use in applications with limited resources.
