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During the last few years, Unmanned Aerial Vehicles (UAVs) technologies are widely used to improve agriculture productivity while reducing drudgery, inspection time, and crop management cost. Moreover, they are able to cover large areas in a matter of a few minutes. Due to the impressive technological advancement, UAV-based remote sensing technologies are increasingly used to collect valuable data that could be used to achieve many precision agriculture applications, including crop/plant classification. In order to process these data accurately, we need powerful tools and algorithms such as Deep Learning approaches. Recently, Convolutional Neural Network (CNN) has emerged as a powerful tool for image processing tasks achieving remarkable results making it the state-of-the-art technique for vision applications. In the present study, we reviewed the recent CNN-based methods applied to the UAV-based remote sensing image analysis for crop/plant classification to help researchers and farmers to decide what algorithms they should use accordingly to their studied crops and the used hardware. Fusing different UAV-based data and deep learning approaches have emerged as a powerful tool to classify different crop types accurately. The readers of the present review could acquire the most challenging issues facing researchers to classify different crop types from UAV imagery and their potential solutions to improve the performance of deep learning-based algorithms.

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The agricultural crop productivity can be affected and reduced due to many factors such as weeds, pests, and diseases. Traditional methods that are based on terrestrial engines, devices, and farmers’ naked eyes are facing many limitations in terms of accuracy and the required time to cover large fields. Currently, precision agriculture that is based on the use of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) provides an effective solution to achieve agriculture applications, including plant disease identification and treatment. In the last few years, plant disease monitoring using UAV platforms is one of the most important agriculture applications that have gained increasing interest by researchers. Accurate detection and treatment of plant diseases at early stages is crucial to improving agricultural production. To this end, in this review, we analyze the recent advances in the use of computer vision techniques that are based on deep learning algorithms and UAV technologies to identify and treat crop diseases.

The objectives of this work are focused on the application of strained silicon on MOSFET transistor. To do this, impact and benefits obtained with the use of strained silicon technology on p-channel MOSFETs are presented. This research attempt to create conventional and two-strained silicon MOSFETs fabricated from the use of TCAD, which is a simulation tool from Silvaco. In our research, two-dimensional simulation of conventional MOSFET, biaxial strained PMOSFET and dual channel strained P-MOSFET has been achieved to extract their characteristics. ATHENA and ATLAS have been used to simulate the process and validate the electronic characteristics. Our results allow showing improvements obtained by comparing the three structures and their characteristics. The maximum of carrier mobility improvement is achieved with percentage of 35.29 % and 70.59 % respectively, by result an improvement in drive current with percentage of 36.54 % and 236.71 %, and reduction of leakage current with percentage of 59.45 % and 82.75 %, the threshold voltage is also enhaced with percentage of: 60 % and 61.4%. Our simulation results highlight the importance of incorporating strain technology in MOSFET transistors.