AIMC Topic: Crops, Agricultural

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Improved early-stage crop classification using a novel fusion-based machine learning approach with Sentinel-2A and Landsat 8-9 data.

Environmental monitoring and assessment
Crop classification during the early stages is challenging because of the striking similarity in spectral and texture features among various crops. To improve classification accuracy, this study proposes a novel fusion-based deep learning approach. T...

An explainable vision transformer with transfer learning based efficient drought stress identification.

Plant molecular biology
Early detection of drought stress is critical for taking timely measures for reducing crop loss before the drought impact becomes irreversible. The subtle phenotypical and physiological changes in response to drought stress are captured by non-invasi...

Evaluating crop yield prediction models in illinois using aquacrop, semi-physical model and artificial neural networks.

Scientific reports
Crop yield is important for agricultural productivity and the country's economy. While crop yield estimation is an essential aspect of modern agriculture, it continues to be one of the most challenging tasks to manage effectively. Corn and soybean ar...

An automated hybrid deep learning framework for paddy leaf disease identification and classification.

Scientific reports
In India, agriculture remains the primary source of livelihood for many people. Pathogen attacks in crops and plants significantly diminish both the yield and quality of production, leading to financial losses. As a result, identifying diseases in cr...

Predicting wheat yield using deep learning and multi-source environmental data.

Scientific reports
Accurate forecasting of crop yields is essential for ensuring food security and promoting sustainable agricultural practices. Winter wheat, a key staple crop in Pakistan, faces challenges in yield prediction because of the complex interactions among ...

Hybrid deep learning optimization for smart agriculture: Dipper throated optimization and polar rose search applied to water quality prediction.

PloS one
Modern sustainable farming demands precise water management techniques, particularly for crops like potatoes that require high-quality irrigation to ensure optimal growth. This study presents a novel hybrid metaheuristic framework that combines Dippe...

Modeling impacts of climate-induced yield variability and adaptations on wheat and maize in a sub-tropical monsoon climate - using fuzzy logic.

Scientific reports
Climate change is causing more frequent and extraordinary extreme weather events that are already negatively affecting crop production. There is a need for improved climate risk assessment by developing smart adaptation strategies for sustainable fut...

AI-driven smart agriculture using hybrid transformer-CNN for real time disease detection in sustainable farming.

Scientific reports
Plant diseases pose a significant threat to global food security, with severe implications for agricultural productivity. Early and accurate detection of these diseases is crucial, yet it remains a challenging task, significantly impacting crop yield...

ST-CFI: Swin Transformer with convolutional feature interactions for identifying plant diseases.

Scientific reports
The increasing global population, coupled with the diminishing availability of arable land, has rendered the challenge of ensuring food security more pronounced. The prompt and precise identification of plant diseases is essential for reducing crop l...

Simulation of emitter discharge along drip laterals under drip fertigation system using artificial neural network.

PloS one
Simulation of emitter discharge under a drip fertigation system is important for capturing the variation in water and nutrient distribution to crops. This is important for an effective design and irrigation management for agricultural crops. Moreover...