AIMC Topic: Crops, Agricultural

Clear Filters Showing 61 to 70 of 243 articles

An intelligent framework for crop health surveillance and disease management.

PloS one
The agricultural sector faces critical challenges, including significant crop losses due to undetected plant diseases, inefficient monitoring systems, and delays in disease management, all of which threaten food security worldwide. Traditional approa...

Predicting land suitability for wheat and barley crops using machine learning techniques.

Scientific reports
Ensuring food security to meet the demands of a growing population remains a key challenge, especially for developing countries like Ethiopia. There are various policies and strategies designed by the government and stakeholders to confront the chall...

Knowledge-guided adaptive spatial-temporal graph contrastive learning framework: Regional crop diseases prediction based on electronic medical records.

Neural networks : the official journal of the International Neural Network Society
The occurrence of crop diseases exhibits nonlinear and dynamic spatial-temporal correlations. How to realize real-time and accurate regional disease prediction is an emerging challenge in smart agriculture. Existing research is hindered by difficulti...

Improved integrated framework for flooded crop damage and recovery assessment: A multi-source earth observation and participatory mapping in Hadejia, Nigeria.

Journal of environmental management
Flooding has increasingly significant adverse effects on global food security, and there is a lack of a framework to effectively integrate remote sensing with survey data for accurate damage and recovery assessment. Also, optical satellite images for...

A dual-branch model combining convolution and vision transformer for crop disease classification.

PloS one
Computer vision holds tremendous potential in crop disease classification, but the complex texture and shape characteristics of crop diseases make disease classification challenging. To address these issues, this paper proposes a dual-branch model fo...

Fine extraction of multi-crop planting area based on deep learning with Sentinel- 2 time-series data.

Environmental science and pollution research international
Accurate and timely access to the spatial distribution of crops is crucial for sustainable agricultural development and food security. However, extracting multi-crop areas based on high-resolution time-series data and deep learning still faces challe...

Deep learning based abiotic crop stress assessment for precision agriculture: A comprehensive review.

Journal of environmental management
Abiotic stresses are a leading cause of crop loss and a severe peril to global food security. Precise and prompt identification of abiotic stresses in crops is crucial for effective mitigation strategies. In recent years, Deep learning (DL) technique...

Novel hybrid transfer neural network for wheat crop growth stages recognition using field images.

Scientific reports
Wheat is one of the world's most widely cultivated cereal crops and is a primary food source for a significant portion of the population. Wheat goes through several distinct developmental phases, and accurately identifying these stages is essential f...

Hybrid vision GNNs based early detection and protection against pest diseases in coffee plants.

Scientific reports
Agriculture is an essential foundation that supports numerous economies, and the longevity of the coffee business is of paramount significance. Controlling and safeguarding coffee farms from harmful pests, including the Coffee Berry Borer, Mealybugs,...

Hybrid deep learning model for density and growth rate estimation on weed image dataset.

Scientific reports
Agriculture research is particularly essential since crop production is a challenge for farmers in India and around the world. 37% of the crop is impacted by invasive plants (weeds). Those unwelcome plants that interbreed with cultivated crops and de...