AIMC Topic: Agriculture

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A Single Image Deep Learning Approach to Restoration of Corrupted Landsat-7 Satellite Images.

Sensors (Basel, Switzerland)
Remote sensing is increasingly recognized as a convenient tool with a wide variety of uses in agriculture. Landsat-7 has supplied multi-spectral imagery of the Earth's surface for more than 4 years and has become an important data source for a large ...

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting.

Sensors (Basel, Switzerland)
Advancements in deep learning and computer vision have led to the discovery of numerous effective solutions to challenging problems in the field of agricultural automation. With the aim to improve the detection precision in the autonomous harvesting ...

Automatic Classification Service System for Citrus Pest Recognition Based on Deep Learning.

Sensors (Basel, Switzerland)
Plant diseases are a major cause of reduction in agricultural output, which leads to severe economic losses and unstable food supply. The citrus plant is an economically important fruit crop grown and produced worldwide. However, citrus plants are ea...

Structural Optimisation and Design of a Cable-Driven Hyper-Redundant Manipulator for Confined Semi-Structured Environments.

Sensors (Basel, Switzerland)
Structural optimisation of robotic manipulators is critical for any manipulator used in confined semi-structured environments, such as in agriculture. Many robotic manipulators utilised in semi-structured environments retain the same characteristics ...

Livestock Identification Using Deep Learning for Traceability.

Sensors (Basel, Switzerland)
Farm livestock identification and welfare assessment using non-invasive digital technology have gained interest in agriculture in the last decade, especially for accurate traceability. This study aimed to develop a face recognition system for dairy f...

Exploring the role of green and Industry 4.0 technologies in achieving sustainable development goals in food sectors.

Food research international (Ottawa, Ont.)
In recent years, the rapid increase in the global population, the challenges associated with climate change, and the emergence of new pandemics have all become major threats to food security worldwide. Consequently, innovative solutions are urgently ...

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects.

Sensors (Basel, Switzerland)
Controlled environment agriculture (CEA) is an unconventional production system that is resource efficient, uses less space, and produces higher yields. Deep learning (DL) has recently been introduced in CEA for different applications including crop ...

Adaptive Multi-ROI Agricultural Robot Navigation Line Extraction Based on Image Semantic Segmentation.

Sensors (Basel, Switzerland)
Automated robots are an important part of realizing sustainable food production in smart agriculture. Agricultural robots require a powerful and precise navigation system to be able to perform tasks in the field. Aiming at the problems of complex ima...

Transformer and group parallel axial attention co-encoder for medical image segmentation.

Scientific reports
U-Net has become baseline standard in the medical image segmentation tasks, but it has limitations in explicitly modeling long-term dependencies. Transformer has the ability to capture long-term relevance through its internal self-attention. However,...

Deep Intelligence-Driven Efficient Forecasting for the Agriculture Economy of Computational Social Systems.

Computational intelligence and neuroscience
In the vision of smart cities, everything is highly connected with the aid of computational intelligence. Therefore, the cyber-physical society has been named a computational social system for a long time. Due to the high relation with vast populatio...