Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review

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Tugrul B., Elfatimi E., Eryigit R.

AGRICULTURE-BASEL, cilt.12, sa.8, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 12 Sayı: 8
  • Basım Tarihi: 2022
  • Doi Numarası: 10.3390/agriculture12081192
  • Dergi Adı: AGRICULTURE-BASEL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Agricultural & Environmental Science Database, CAB Abstracts, Food Science & Technology Abstracts, Veterinary Science Database, Directory of Open Access Journals
  • Anahtar Kelimeler: machine learning, deep learning, plant leaf diseases, DEEP LEARNING-MODELS, IDENTIFICATION MODEL, CLASSIFICATION, AGRICULTURE, LEAVES, RECOGNITION, ALGORITHM, CNN
  • Ankara Üniversitesi Adresli: Evet

Özet

Rapid improvements in deep learning (DL) techniques have made it possible to detect and recognize objects from images. DL approaches have recently entered various agricultural and farming applications after being successfully employed in various fields. Automatic identification of plant diseases can help farmers manage their crops more effectively, resulting in higher yields. Detecting plant disease in crops using images is an intrinsically difficult task. In addition to their detection, individual species identification is necessary for applying tailored control methods. A survey of research initiatives that use convolutional neural networks (CNN), a type of DL, to address various plant disease detection concerns was undertaken in the current publication. In this work, we have reviewed 100 of the most relevant CNN articles on detecting various plant leaf diseases over the last five years. In addition, we identified and summarized several problems and solutions corresponding to the CNN used in plant leaf disease detection. Moreover, Deep convolutional neural networks (DCNN) trained on image data were the most effective method for detecting early disease detection. We expressed the benefits and drawbacks of utilizing CNN in agriculture, and we discussed the direction of future developments in plant disease detection.