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Crops, Agricultural

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Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning.

Sensors (Basel, Switzerland)
Weeds can cause significant yield losses and will continue to be a problem for agricultural production due to climate change. Dicamba is widely used to control weeds in monocot crops, especially genetically engineered dicamba-tolerant (DT) dicot crop...

Designing artificial synthetic promoters for accurate, smart, and versatile gene expression in plants.

Plant communications
With the development of high-throughput biology techniques and artificial intelligence, it has become increasingly feasible to design and construct artificial biological parts, modules, circuits, and even whole systems. To overcome the limitations of...

Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery.

Sensors (Basel, Switzerland)
Climate change and the COVID-19 pandemic have disrupted the food supply chain across the globe and adversely affected food security. Early estimation of staple crops can assist relevant government agencies to take timely actions for ensuring food sec...

PhenoBot: an automated system for leaf area analysis using deep learning.

Planta
A low-cost dynamic image capturing and analysis pipeline using color-based deep learning segmentation was developed for direct leaf area estimation of multiple crop types in a commercial environment. Crop yield is largely driven by intercepted radiat...

Robotics-assisted, organic agricultural-biotechnology based environment-friendly healthy food option: Beyond the binary of GM versus Organic crops.

Journal of biotechnology
Human society cannot afford the luxury of the business-as-usual approach when dealing with the emerging challenges of the 21st century. The challenges of food production to meet the pace of population growth in an environmentally-sustainable manner h...

Deep learning system for paddy plant disease detection and classification.

Environmental monitoring and assessment
Automatic detection and analysis of rice crop diseases is widely required in the farming industry, which can be utilized to avoid squandering financial and other resources, reduce yield losses, and improve treatment efficiency, resulting in healthier...

Use of synthetic images for training a deep learning model for weed detection and biomass estimation in cotton.

Scientific reports
Site-specific treatment of weeds in agricultural landscapes has been gaining importance in recent years due to economic savings and minimal impact on the environment. Different detection methods have been developed and tested for precision weed manag...

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...

An Innovative Fusion-Based Scenario for Improving Land Crop Mapping Accuracy.

Sensors (Basel, Switzerland)
The accuracy of land crop maps obtained from satellite images depends on the type of feature selection algorithm and classifier. Each of these algorithms have different efficiency in different conditions; therefore, developing a suitable strategy for...

Study of Multiscale Fused Extraction of Cropland Plots in Remote Sensing Images Based on Attention Mechanism.

Computational intelligence and neuroscience
Cropland extraction from remote sensing images is an essential part of precise digital agriculture services. This paper proposed an SSGNet network of multiscale fused extraction of cropland based on the attention mechanism to address issues with comp...