The world population and the number of people affected by hunger constantly increases. Precision farming offers new solutions to a modern and more fertile production in agriculture. Early and in-place disease detection is one of the fields that recent studies have focused on. The present paper introduces a new approach to transfer learning in that training, validating and testing of the model have been made on images from different sources to see its effectiveness. Several optimization methods including the adaptation of a recent custom PowerSign optimization algorithm are compared in the study. Accordingly, the model with Adagrad optimizer produced more consistent training, validation and testing accuracies as 92%, 91% and 91%, respectively. The final model is transformed into a mobile application and tested on the field. The app showed high accuracy in the real environment on condition that the phone camera should be kept close to the leaf and focus should be clear on the image.
Precision agriculture, disease detection, deep learning, image processing
"Apple leaf disease detection and classification based on transfer learning,"
Turkish Journal of Agriculture and Forestry: Vol. 45:
6, Article 8.
Available at: https://journals.tubitak.gov.tr/agriculture/vol45/iss6/8