Detection of rubber (Heavea brasialiensis) leaf diseases using image processing techniques

Rizqi Elmuna Hidayah; Suzani Mohamad Samuri.

Transactions on Science and Technology, 10(2), 48 - 53.

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ABSTRACT
A big threat occurs in rubber plantations in Indonesia. Leaf disease attacks tens of thousands of hectares of rubber land and threatens agricultural sustainability. This situation makes Indonesian farmers vulnerable to the effects of tree disease, which can be fatal if not treated properly. Detection of plant diseases conventionally is very complex and takes a long time. Natural conditions in Kalimantan make it more difficult for farmers to get treatment for the disease. The early detection of leaf diseases using machine learning techniques is foreseen to be necessary. This study uses leaf disease images to detect the type of leaf diseases, whereby image processing techniques are carried out to determine the characteristics of the disease. In the preprocessing stage, the red, green, blue (RGB) color space was changed to hue, saturation, value (HSV). Then, the K-means segmentation is applied with a value of K=3. The gray level co-occurrence matrix (GLCM) performed the extraction to get the carried out using the Adaptive Neuro-Fuzzy Inference System (ANFIS) with 99% accuracy result for training data and 93% for testing data with a Root Mean Square Error (RMSE) value of 0.113263. The results show that machine learning method has the potential to help in minimizing losses, improve plant quality and quantity, and help for early detection so that the best treatment steps can be taken.

KEYWORDS: Agricultural Technology; Classification; Early Detection; Image Processing; Leaf Disease.



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