  The use of unmanned aerial vehicles (UAV) is revolutionizing the agricultural
industry. Cashews are grown by approximately 70% of small and marginal farmers,
and the cashew industry plays a critical role in their economic development. To
take timely counter measures against plant diseases and infections, it is
imperative to monitor and detect diseases as early as possible and take
suitable measures. Using UAVs, such as those that are equipped with artificial
intelligence, can assist farmers by providing early detection of crop diseases
and precision pesticide application. An edge computing paradigm of Artificial
Intelligence is employed to process this image in order to make decisions with
the least amount of latency possible. As a result of these decisions, the stage
of infestation, the crops affected, the method of prevention of spreading the
disease, and what type and amount of pesticides need to be applied can be
determined. UAVs equipped with sensors detect disease patterns quickly and
accurately over large areas. Combined with AI algorithms, these machines can
analyse data from a variety of sources such as temperature, humidity, CO2
levels and soil composition. This allows them to recognize disease symptoms
before they become visible. Early detection allows for more effective control
strategies that can reduce costs caused by lost production due to infestations
or crop failure. Using an end-to-end training architecture, mobileNetV2
determines how to classify anthracnose disease in cashew leaves. A standard
PlantVillage dataset is used for performance evaluation and for
standardization. Additionally, samples captured with a drone present a variety
of image samples captured in a variety of conditions, which complicates the
analysis. According to our analysis, we were able to identify the anthracnose
with 95% accuracy and the healthy leaves with 99% accuracy.
