Deep learning for apple diseases: classification and identification

Abstract

Diseases and pests cause huge economic loss to the apple industry every year. The identification of various apple diseases is challenging for the farmers as the symptoms produced by different diseases may be very similar, and may be present simultaneously. This paper is an attempt to provide the timely and accurate detection and identification of apple diseases. In this study, we propose a deep learning based approach for identification and classification of apple diseases. The first part of the study is dataset creation which includes data collection and data labelling. Next, we train a Convolutional Neural Network (CNN) model on the prepared dataset for automatic classification of apple diseases. CNNs are end-to-end learning algorithms which perform automatic feature extraction and learn complex features directly from raw images, making them suitable for wide variety of tasks like image classification, object detection, segmentation etc. We applied transfer learning to initialize the parameters of the proposed deep model. Data augmentation techniques like rotation, translation, reflection and scaling were also applied to prevent overfitting. The proposed CNN model obtained encouraging results, reaching around 97.18% of accuracy on our prepared dataset. The results validate that the proposed method is effective in classifying various types of apple diseases and can be used as a practical tool by farmers.

Enhancing Plant Disease Classification through Manual CNN Hyperparameter Tuning

“…While (38) achieved an average accuracy of 97,2 % by using transfer learning for automatic apple disease identification and classification on them prepared dataset. In addition, (39) achieved an accuracy of 98 % in classifying the Corn leaf images into four different categories. Also, (40) achieved an accuracy of 98 % by concatenating CNN-extracted features with ANN and the proposed method in (41) identify the crop species with 96,76 % accuracy.…”

De-Noising of Apple Fruit Images using Dilated Convolution Neural Network model with Mixed Activation Function

“…In real-world scenarios, images might contain various types of noise, such as blur, sensor noise, or other artifacts. There may be research gaps in developing robust preprocessing methods to denoise images effectively, ensuring that the model's performance is not adversely affected by these issues [5].…”

“…The biggest advantage of Deep Learning techniques is that they do not rely on hand-crafted features. Rather, these networks learn features while training without any human intervention [5]. The accuracy of disease detection and other classification category models has recently improved greatly because of recent developments.…”

D-KAP: A Deep Learning-based Kashmiri Apple Plant Disease Prediction Framework

“…Raut and Fulsunge [11] introduced a plant disease detection model where the RELIEF-F algorithm is used for feature extraction. Khan and Banday [12] used CNN to extract the features from the input leaf image. The CNN is trained on their private leaf datasets.…”