Attention embedded residual CNN for disease detection in tomato leaves

TL;DR: A pre-trained convolutional neural network (CNN) based method for identifying and classifying the leaf diseases in tomato crops using transfer learning is presented and MobileNet outperformed all other models with overall classification accuracy of 96%.

TL;DR: This paper presents exhaustive experimental evaluations on ensemble model to tune hyper-parameters named learning rate, optimizer and no of epochs, which can be directly utilized while employing the ensemble model for cotton plant leaves disease detection and prediction.

TL;DR: ISNR model is used for super-resolving potato leaf images and identifying disease. It achieves high PSNR and SSIM values and accurate classification accuracies.

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.