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Agriculture

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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...

Evaluation of deep learning and transform domain feature extraction techniques for land cover classification: balancing through augmentation.

Environmental science and pollution research international
The identification of features that can improve classification accuracy is a major concern in land cover classification research. This paper compares deep learning and transform domain feature extraction techniques for land cover classification of SA...

Water demand in watershed forecasting using a hybrid model based on autoregressive moving average and deep neural networks.

Environmental science and pollution research international
Increasing water demand is exacerbating water shortages in water-scarce regions (such as India, China, and Iran). Effective water demand forecasting is essential for the sustainable management of water supply systems in watersheds. To alleviate the c...

A Cloud Enabled Crop Recommendation Platform for Machine Learning-Driven Precision Farming.

Sensors (Basel, Switzerland)
Modern agriculture incorporated a portfolio of technologies to meet the current demand for agricultural food production, in terms of both quality and quantity. In this technology-driven farming era, this portfolio of technologies has aided farmers to...

Can Machine Learning classifiers be used to regulate nutrients using small training datasets for aquaponic irrigation?: A comparative analysis.

PloS one
With the recent advances in the field of alternate agriculture, there has been an ever-growing demand for aquaponics as a potential substitute for traditional agricultural techniques for improving sustainable food production. However, the lack of dat...

Suitability Evaluation of Crop Variety via Graph Neural Network.

Computational intelligence and neuroscience
With the continuous growth of the global population, insufficient food production has become an urgent problem to be solved in most countries. At present, using artificial intelligence technology to improve suitability between land and crop varieties...