Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant.

Abstract

Instinctive detection of infections by carefully inspecting the signs on the plant leaves is an easier and economic way to diagnose different plant leaf diseases. This defines a way in which symptoms of diseased plants are detected utilizing the concept of feature learning (Sulistyo et al., 2020). The physical method of detecting and analyzing diseases takes a lot of time and has chances of making many errors (Sulistyo et al., 2020). So a method has been developed to identify the symptoms by just acquiring the chili plant leaf image. The methodology used involves image database, extracting the region of interest, training and testing images, symptoms/features extraction of the plant image using moments, building of the symptom vector feature dataset, and finding the correlation and similarity between different symptoms of the plant (Sulistyo et al., 2020). This will detect different diseases of the plant.

Code and data availability

The paper describes a 7,850-image G-4 chili leaf dataset (five classes) and a moment-based feature/classification pipeline, but no public repository, deposit, or authors' URL for the images, feature vectors, or code is provided. The data availability statement only offers the material via the corresponding author, so a

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