Artificial Intelligence (AI) applications related to agriculture have recently gained in use and
attention. They are indeed valuable tools for interpreting data, improving production chains …

Weeds can decrease yields and the quality of crops. Detection, localisation, and
classification of weeds in crops are crucial for developing efficient weed control and …

Precision agriculture relies on effective weed management for high yields and crop quality.
Deep learning (DL)-based techniques show potential for providing effective solutions …

Modern agriculture is characterized by the use of smart technology and precision agriculture
to monitor crops in real time. The technologies enhance total yields by identifying …

Introduction Weeds are one of the main factors affecting crop growth, making weed control a
pressing global problem. In recent years, interest in intelligent mechanical weed-control …

Automatic weed detection and classification can significantly reduce weed management
costs and improve crop yields and quality. Weed detection in crops from imagery is …

Poisonous plants are the third largest category of poisons known globally, which pose a risk
of poisoning and death to humans. Currently, the identification of medicinal and poisonous …

This study explores the efficacy of drone-acquired RGB images and the YOLO model in
detecting the invasive species Siam weed (Chromolaena odorata) in natural environments …

Enhancing model performance in agricultural image analysis faces challenges due to
limited datasets, biological variability, and uncontrolled environments. Deep learning …

Weed is a major problem faced by the agriculture and farming sector. Advanced imaging
and deep learning (DL) techniques have the potential to automate various tasks involved in …