Leaf Disease Classification Using Artificial Neural Network

Abstract

Nowadays, herb plants are importance to medical field and can give benefit to human. In this research, Phyllanthus Elegans Wall (Asin-Asin Gajah) is used to analyse and to classify whether it is healthy or unhealthy leaf. This plant was chosen because its function can cure breast cancer. Therefore, there is a need for alternative cure for patient of breast cancer rather than use the technology such as Chemotherapy, surgery or use of medicine from hospital. The purpose of this research to identify the quality of leaf and using technology in agriculture field. The process to analysis the leaf quality start from image acquisition, image processing, and classification. For image processing method, the most important for this part is the segmentation using HSV to input RGB image for the color transformation structure. The analysis of leaf disease image is applied based on colour and shape. Finally, the classification method use feed-forward Neural Network, which uses Back-propagation algorithm. The result shows comparison between Multi-layer Perceptron (MLP) and Radial Basis Function (RBF) and comparison between MLP and RBF shown in percentage of accuracy. MLP and RBF is algorithm for Neural Network. Conclusively, classifier of Neural Network shows better performance and more accuracy.

Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network

“…These use highly accurate methods for identifying plant disease in tomato leaves. In addition, researchers have proposed many deep learning-based solutions in disease detection and classification, as discussed below in [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ].…”

“…A technique has been proposed to detect and classify plant leaf disease with an accuracy of 93.75% [ 22 ]. The image processing technology and classification algorithm detect and classify plant leaf disease with better quality [ 23 ]. Here, an 8-mega-pixel smartphone camera is used to collect sample data and divides it into 50% healthy and 50% unhealthy categories.…”

Kenaf plant pest and disease detection using faster regional based convolutional neural network

“…Recall is the success of the system in predicted true positive data from all data that are actually positive, namely data that are predicted to be true positive (TP) and predicted false negative (FN). The equations for precision and recall are shown in (4) and (5). The bounding box issued by the system is a prediction of the boundary of the ground truth and IoU coordinates which are used to set this limit based on the specified threshold.…”

“…Several studies have been conducted by using classification methods to detect plant diseases. These methods include back-propagation neural network that achieves 91% accuracy [4] and 92% accuracy [5], and extreme learning machine which gets 66.67% accuracy [6] and 89.19% accuracy [7]. Support vector machine is used which gets 97.6% accuracy [8] and 92.86% accuracy [9].…”

Automatic Detection and Classification of Brinjal Leaf DiseaSES

“…Segmentation is done by HSV and Multilayer Perception (MLP). Radial Basis Function (RBF) classifiers are used and the comparison between PLP and RBF is done to get better accuracy [7]. Fuzzy logic and Probabilistic Neural Network (PNN) is developed to extract and categorize the problem using PNN and the precision obtained is 91.46% [8] [9].Otsu Thresholding based segmentation is done to form the binary mask in the affected region.…”