Bacterial Disease Detection of Cherry Plant Using Deep Features

Abstract

Although the cherry plant is widely grown in the world and Turkey, it is a fruit tree that is difficult to grow and maintain. It can be exposed to various pesticide diseases, especially during fruiting. Today, approaches based on expert reviews and analyses are used for the identification of these diseases. In addition, cherry producers are trying to detect diseases with their knowledge based on experience. Computer-aided agricultural analysis systems are also being developed depending on the rapid developments in technology. These systems help to monitor all processes from planting, cultivation, and harvesting of agricultural products and to make decisions to grow the products healthily. One of the most important issues to be detected and monitored with these systems is plant diseases. The features of the cherry plant disease will be determined by using a pre-trained convolutional neural network (CNN) model which is DarkNet-19, within the scope of this study. These machine learning-based features have been used for the detection of bacteria-based diseases commonly seen on the leaves of cherry plants. The acquired features are classified with Linear Discriminant Analysis, K-Nearest Neighbor, and Support Vector Machine classifiers to solve the multi-class problem including diseased (less and very) and healthy plants. The experimental results show that a success rate of 88.1% was obtained in the detection of the disease.

Plant phenotyping relevance

桜の葉の画像特徴をCNNで抽出し、細菌性疾患の症状（植物の病害状態）を分類する計算的フェノタイピング手法が研究の中心である。

Code and data availability

The paper uses a subset of the PlantVillage cherry leaf dataset relabeled into three classes by an agronomist, but no authors' dataset, code, models, or supplements are publicly deposited. The data availability statement says 'Not applicable', and the only dataset reference is the cited prior PlantVillage deposit, nota

No evidence-backed public reproduction asset is currently recorded.