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An in-depth exploration of automated jackfruit disease recognition
Md. Tarek Habib,

Bangladesh extensively depends on agriculture for the economy as well as food security owing to its huge population. In this connection, it becomes very important to efficiently grow plants and increase their yields. Quantity and quality of fruits can be degraded having attacked by various diseases. It is a matter of fact that not even a single research work has been conducted for automated recognition of jackfruit diseases to facilitate those distant farmers who need proper cultivation support. Presuming that our context is the recognition of jackfruit diseases, two challenging problems are mainly raised, i.e. detection of diseases and classification of diseases. In this research, we perform an in-depth investigation of an agro-medical expert system, which proceeds with a digital image acquired with a cellphone or other handheld device and recognizes the disease. Exhaustive experiments have been performed to assess the feasibility of our intended expert system. At first, a discriminatory feature set is selected. k-means clustering segmentation is put into action to detect disease-affected regions of an image of a disease-attacked jackfruit and extract the features from these regions. Then classification of the diseases is accomplished by using nine off-the-shelf classification algorithms in order to thoroughly assess the merits of the classifiers in the index of seven prominent performance metrics. Random forest is found outperforming all other classifiers to the amount of all metrics used by attaining an accuracy approaching to 90%. On the contrary, logistic regression shows not only the poorest result of an accuracy approaching to 75% but also some other poorest metric-values.

Jackfruit disease, Agro-medical expert system, Discriminatory features k-means clustering, Classifier Random forest
Journal or Conference Name
Journal of King Saud University - Computer and Information Sciences
Publication Year