PlantDiseaseNet: convolutional neural network ensemble for plant disease and pest detection

Turkoglu M., Yanikoglu B., HANBAY D.

SIGNAL IMAGE AND VIDEO PROCESSING, vol.16, no.2, pp.301-309, 2022 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 16 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.1007/s11760-021-01909-2
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, zbMATH
  • Page Numbers: pp.301-309
  • Keywords: Plant disease and pest system, Deep features, Support vector machine, CNN, Fusion ensemble, SYSTEM, IDENTIFICATION, RECOGNITION, TEXTURE, COLOR, SHAPE
  • Inonu University Affiliated: Yes


Plant diseases and pests cause significant losses in agriculture, with economic, ecological and social implications. Therefore, early detection of plant diseases and pests via automated methods are very important. Recent machine learning-based studies have become popular in the solution of agricultural problems such as plant diseases. In this work, we present two classification models based on deep feature extraction from pre-trained convolutional neural networks. In the proposed models, we fine-tune and combine six state-of-the-art convolutional neural networks and evaluate them on the given problem both individually and as an ensemble. Finally, the performances of different combinations based on the proposed models are calculated using a support vector machine (SVM) classifier. In order to verify the validity of the proposed model, we collected Turkey-PlantDataset, consisting of unconstrained photographs of 15 kinds of disease and pest images observed in Turkey. According to the obtained performance results, the accuracy scores are calculated as 97.56% using the majority voting ensemble model and 96.83% using the early fusion ensemble model. The results demonstrate that the proposed models reach or exceed state-of-the-art results for this problem.