Abstract:
Mangoes are grown in different countries for economic value, but tree’s leaves are prone to getting
certain diseases that may be destructive on the economical crops. This paper therefore presents in
this research the use of machine learning to provide a novel approach towards the identification
and categorization of diseases on mango leaves. Primarily, the use of image analysis and feature
extraction enables us to perceive the pictorial characteristics associated with all the diseases using
the Histogram of Oriented Gradients (HOG) features. Combating challenges such as the inequity
of the mango leaf images’ feature representation, distribution imbalance between classes, and
model optimization, the proposed study employs a dataset with 4,000 images collected from
multiple plantations. The results obtained by the experiment show the works of Logistic
Regression, SVM, Random Forest, and XGBoost algorithms in distinguishing the healthy and the
malignant mango leaves more accurately. Also, the implications of this research on supporting the
sustainability of agriculture are discussed, especially regarding the effectiveness of the proposed
classification models for early diagnosis and mitigation of diseases in mango production.
Altogether, this research study is significant to the development of the methodologies within the
field of precision agriculture, as well as stress the necessity of using the machine learning approach
to the problem of crop disease detection.
