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

Abstract

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.

Identification of Maize Leaf Diseases Based On AlexNet and ResNet50 Convolutional Neural Networks

“…Early detection of crop disease plays a crucial role in enabling farmers to implement necessary control measures, including selecting appropriate pesticides, thereby increasing crop yield and improving overall quality (8,9). Farmers commonly rely on visual analysis of disease symptoms on leaves to identify and classify crop diseases (10). However, this technique is subjective, prone to errors, time-consuming, and costly for poor rural farmers who may need to consult a trained plant pathologist to confirm diseases, especially in multiple infections (11).…”