Abstract
The agricultural sector has become an important income source for our
country. In terms of nutrient absorption, plant diseases affecting the agricultural yield
are creating a great hazard. In agriculture, recognizing infectious plants seems
challenging due to the premise of the needed infrastructure. To prevent the spread of
diseases, the identification of infectious leaves in the plant is observed to be a
necessary step. This work aims to propose a machine learning technique on the ANN
method for plant diseases identification and classification. This paper proposes a novel
hybrid algorithm, called Black Widow Optimization Algorithm with Mayfly
Optimization Algorithm (BWO-MA), for solving global optimization problems.
In this paper, a BWO-MA with Artificial Neural Networks (ANN) based diagnostic
model for earlier diagnosis of plant diseases is developed. Comparison has been done
with existing machine learning methods with the proposed BWO-MA-based ANN
architecture to accommodate greater performance. The comprehensive analysis showed
that our proposal achieved splendid state-of-the-art performance.