Attention embedded residual CNN for disease detection in tomato leaves

TL;DR: Zhang et al. as mentioned in this paper compared various famous CNN designs and found that the EfficientNet performs better in multilabel plant illness classification tasks, and tracked down that skip associations, spatial convolutions, and more limited secret layer network impact better outcomes in plant illness characterization.

TL;DR: In this article , a hybrid approach has been proposed via concatenation of Convolutional Neural Network (CNN) and Support Vector Machine (SVM), which is used for the extraction of utilitarian features from the input data and these are then classified using an optimized SVM classifier and results are recorded in terms of accuracy.

TL;DR: The lightweight nature and improved prediction performance make the proposed network suitable for being integrated with IoT devices to assist the stakeholders in identifying plant diseases at the field level.

TL;DR: A deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions, and combines each of these meta-architectures with “deep feature extractors” such as VGG net and Residual Network.

TL;DR: The use of individual lesions and spots for the task, rather than considering the entire leaf, allows the identification of multiple diseases affecting the same leaf and indicates that, as long as enough data is available, deep learning techniques are effective for plant disease detection and recognition.

TL;DR: The role of number of images and significance of hyperparameters namely minibatch size, weight and bias learning rate in the classification accuracy and execution time have been analyzed.

TL;DR: A Convolutional Neural Network model and Learning Vector Quantization algorithm based method for tomato leaf disease detection and classification and results validate that the proposed method effectively recognizes four different types of tomato leaf diseases.