Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives.

TL;DR: This study evaluates deep learning techniques and chlorophyll fluorescence imaging for detecting salt stress in soybean seedlings, finding ResNet50 outperforms other models with 98.61% accuracy when fusing features from three image types, enhancing precision agriculture and crop management.

TL;DR: In this article , a semantic segmentation model based on the modified DeepLabv3 network for near real-time DM segmentation in high resolution images was developed. But the model is not suitable for the use of agricultural robots for field operations.

TL;DR: Instance segmentation opens many possibilities for optimized weed removal actions, and Weed electrification, for instance, could benefit from the targeted adjustment of the voltage, frequency, and location of the electrode to the plant.

TL;DR: In this paper , a coordinate attention-based multiple feature fusion network (CAMFFNet) was proposed for detecting tobacco disease under field conditions, which achieved an accuracy of 89.71 % on the tobacco disease test set.

TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.

TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.

TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.

TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.