Grading of Soybean Leaf Disease Based on Segmented Image Using K-means Clustering

Abstract

Traditional method used for disease scoring scale to grade the plant diseases is mainly based on neckaed eye observation by agriculture expert or plant pathlogiest. In this method percentage scale was exclusively used to define different disease severities in an illustrated series of disease assessment keys for field crops.The assessment of plant leaf diseases using this aaproach which may be subjective, time consuming and cost effective.Also aacurate grading of leaf diseases is essential to the determination of pest control measures. In order to improve this process, here we propose a technique for automatically quantifying the damaged leaf area using k means clustering, which uses square Euclidian distances method for partition of leaf image.For grading of soybean leaf disese which appear on leaves based on segmented diseased region are done automatically by estiamting thae ratio of the unit pixel expressed under diseased region area and unit pixel expressed under Leaf region area.For experiment purpose samples of Bacterial Leaf Blight Septoria Brown spot, Bean Pod Mottle Virus infected soybean leaf images were taken for analysis.Finally estiamated diseased severity and its grading is compared with manual scoring based on conventional illustrated key diagram was conducted. Comparative assessment results showed a good agreement between the numbers of percentage scale grading obtained by manual scoring and by image analysis The result shows that the proposed method is precise and reliable than visual evaluation performed by patahlogiest.

Convolutional Neural Networks for Leaf Image-Based Plant Disease Classification

“…Another study presented a severity grading system using K-means clustering to automatically detect diseases (bacterial leaf blight, Septoria brown spot, and bean pod mottle virus) [8]. The efficacy of the system evaluated by comparing the results with the manual technique.…”

Soybean leaf disease detection and severity measurement using multiclass SVM and KNN classifier

“…The disease severity of the soybean leaf is measured by quantifying the number of white (on) pixel belongs to lesion region called diseased area (Ad) and comparing it with a quantified total number of leaf object pixels. In our work, the binarization method is used to calculate the total leaf area of soybean leaf object (Al) [18]. The severity of disease is given in percentage which is computed using (2).…”

Semi‐automatic leaf disease detection and classification system for soybean culture

“…Experimental results are reported for bacterial blight, rust, brown spot, frog eye, sudden death syndrome, and downy mildew, but the system can calculate the severity of other plants as well. Similarly, another work measures severity of septoria brown spot, leaf blight, and bean pod mottle virus using k-means clustering [18]. The disease severity is estimated by comparing the number of pixels in the infected lesion area with those in the total leaf area.…”

“…PlantVillage is a freely available dataset used by researchers to examine computer vision approaches in solving yield loss problems occurred due to varied diseases. Instead of a constrained laboratory environment as in [18], images in PlantVillage are taken in the fields and thus have a complex background. In this paper, a rule-based system is designed and its validity is evaluated via visual examination.…”

“…Again the number of images used is very large thus the achieved accuracy range can be considered as acceptable. Finally, the authors of [17][18][19] have worked only on the severity and by means of visual comparison, it can rightly be said that the proposed system is computing disease severity in all cases efficiently. For a better evaluation, three approaches [12,13,15] are implemented and compared in Table 6 with the best three candidates of the proposed system on PlantVillage dataset.…”