Disease Detection System in Tobacco Leaves based
on Edge Detection with Decision Tree Classification

Dublin Core


Disease Detection System in Tobacco Leaves based
on Edge Detection with Decision Tree Classification


Disease Detection


Indonesia has long been known as an agricultural country, one of Indonesia's best agricultural
products is tobacco. Tobacco with good quality can be seen from the leaves, in fact the diseases that attack
tobacco are of various types which can be seen from the changes in the tobacco leaves starting from the
seeding and planting period. In the tobacco growing period, it is divided into two major parts, namely
seeding period and the planting period, so that diseases that attack tobacco are also divided into two, namely
diseases that attack during seeding and planting. This research is limited to diseases that attack tobacco at
the time of planting, because at the time of seedling the tobacco has not yet produced leaves. When tobacco
enters the planting period, at this time tobacco leaves begin to form. Good care is needed at this time such
as fertilization, nutrition, vitamins, and pest control in order to obtain healthy tobacco so that tobacco is not
susceptible to disease. Tobacco that lacks nutritional intake will be susceptible to diseases including fungi,
bacteria, and viruses. The disease attack on tobacco has its own characteristics that appear on tobacco
leaves. Early detection of the disease is very important so that disease control can be precise and the spread
of the disease can be prevented so as not to cause endemic. In this research, an early detection system of
tobacco leaf disease based on image processing will be designed. Normalization image, grayscale
technique, folllowed by edge detection will be applied in these image so that from here the entropy, energy,
and inertia values of the image can be obtained using statistical measures, and the last one using the decision
tree classification technique can be classified as uninfected leaf or infected leaf. In this study, feature
extraction from images of tobacco leaves that are not infected with the virus using grayscale techniques
followed by edge detection produces an average statistical measure with entropy (h) values between 2,341
to 2,676, energy (e) values between 6,112 to 6,665, and inertia values. (i) between 3,322 to 3,576, while
for leaves infected with the virus the average value of entropy (h) is between 4,543 to 5,576, the average
value of energy (e) is between 12,212 to 13,455, and the average value of inertia (i) between 5,343 to 6,597.
Keywords— Tobacco leaves; Tobacco Mozaic Virus (TMV); Edge Detection; Decision Tree.