Detection of Strawberry Plant Disease Based on Leaf Spot Using Color Segmentation

Abstract

Strawberry plant is a fruit plants that have a high enough value. Strawberry fruit contains high amounts of fiber, vitamin C, folic acid, potassium and antioxidants. Cultivating strawberries is an easy task because strawberry plants are often affected by both micro-organisms, pests and bacteria. To reduce the spread of disease in strawberry plants, the initial introduction of strawberry disease will be carried out using digital image processing. Digital images of leaf are processed to determine the health status of the strawberry plant. The process carried out on digital images includes image improvement, color segmentation from RGB color space into HSV color space, regional segmentation to determine the area of deformed leaves and intact leaves. The image processing shows that 85% accuracy of detection is obtained.

Firma espectral de peca de la hoja (Mycosphaerella fragariae (Tul.)) en plantas de fresa (Fragaria x ananassa Duch) relacionada a índices NDVI y NDRE

“…4 illustrates the use given to the software Compu eye leaf and symptom area [21], on the left side a first detection of abnormal spots in the green cover of the leaf is observed, which are identified by the software at starting from a learning of a range of colors given by the user, later, as illustrated on the right side, the area corresponding to green is located, understanding this as a healthy area and gives a percentage of the diseased area compared to the healthy area in terms of percentage. This processing is like other works carried out to detect changes in the leaves from digital images and determine their health with 85% precision [24]. The results of the affected area in detached leaflets are presented from the linear regression with respect to the behavior of the NDVI (Fig.…”

Improved Vision-Based Detection of Strawberry Diseases Using a Deep Neural Network

“…It is difficult to compare the performance of our proposed method using relative terms, as we have attempted to detect different set of diseases with different data than other researchers. For example, ( Nie et al, 2019 ) have achieved 99.95% accuracy in identifying four diseases, while others ( Kusumandari et al, 2019 ; Shin et al, 2020 ) have sought to identify only a single disease.…”

Designing a Mobile Application for Identifying Strawberry Diseases with YOLOv8 Model Integration

“…In addition, methods for detecting strawberry diseases are based on leaf color. Dwi Esti Kusumandari et al [21] proposed using digital image processing to analyze diseases of strawberry plants based on leaf color. Digital images of mulberry leaves will be processed to determine health status.…”