Smart agriculture has taken more attention during the last decade due to the bio-hazards of climate change impacts, extreme weather events, population explosion, food security demands and natural resources shortage. The Egyptian government has taken initiative in dealing with plants diseases especially tomato which is one of the most important vegetable crops worldwide that are affected by many diseases causing high yield loss. Deep learning techniques have become the main focus in the direction of identifying tomato leaf diseases. This study evaluated different deep learning models pre-trained on ImageNet dataset such as ResNet50, InceptionV3, AlexNet, MobileNetV1, MobileNetV2 and MobileNetV3.To the best of our knowledge MobileNetV3 has not been tested on tomato leaf diseases. Each of the former deep learning models has been evaluated and optimized with different techniques. The evaluation shows that MobileNetV3 Small has achieved an accuracy of 98.99% while MobileNetV3 Large has achieved an accuracy of 99.81%. All models have been deployed on a workstation to evaluate their performance by calculating the prediction time on tomato leaf images. The models were also deployed on a Raspberry Pi 4 in order to build an Internet of Things (IoT) device capable of tomato leaf disease detection. MobileNetV3 Small had a latency of 66 ms and 251 ms on the workstation and the Raspberry Pi 4, respectively. On the other hand, MobileNetV3 Large had a latency of 50 ms on the workstation and 348 ms on the Raspberry Pi 4.

No abstract

Background: Undoubtedly one of the most successful recent developments in the treatment of heart failure (HF) is cardiac resynchronization therapy (CRT). CRT aims to provide the failing heart with a mechanical advantage that can significantly reduce symptoms and mortality by treating ventricular dyssynchrony, a problem that affects up to onethird of patients with highly symptomatic systolic HF. Objectives: The aim of the current study was to evaluate the effect of different right ventricular (RV) lead positions on QRS complex duration post CRT device implantation in patients indicated for CRT as a treatment of chronic heart failure. Patients and methods: This clinical trial included 100 patients who underwent CRT device implantation as a treatment for heart failure, divided into 2 groups according to the site of RV lead implantation after confirmation of the RV lead position; 54 patients had the RV lead implanted in the RV Apex (RVA n=54) and 46 patients had the RV lead implanted in the RV Septum (RVS n=46). Results: There was no significant difference between the two groups regarding clinical response (NYHA Class) (Pvalue = 0.583), left ventricular ejection fraction (LVEF) (Δ EF 6.26 ± 1.64 in RVS group vs. 6.07 ± 1.43 in RVA group, P-value = 0.575) LVES diameter (47.70 ± 8.03 in RVS group vs. 45.39 ± 7.48 in RVA group, P-value = 0.141) or QRS complex narrowing (Δ QRS 60.93 ± 14.68 in RVS group vs. 54.07 ± 13.12 in RVA group, P-value = 0.182). Conclusion: Our results demonstrate that septal RV pacing in CRT is non-inferior to apical RV pacing regarding the primary objective of the study regarding clinical outcome, narrowing of QRS complex (Δ QRS) or LV reverse remodeling.