Tea leaf diseases are a major challenge to agricultural productivity, with far-reaching
implications for yield and quality in the tea industry. The rise of machine learning has …

Artificial intelligence (AI) is revolutionizing approaches in plant disease management and
phytopathological research. This review analyzes current applications and future directions …

In recent studies, convolutional neural networks (CNNs) are mostly used as dynamic
techniques for visualization-based malware classification and detection. Though vision …

Intelligent agriculture is critical for guiding agricultural production and enhancing efficiency
through early disease diagnosis, yield estimation, automatic harvest, and postharvest …

For some nations, including India, agriculture is the main source of income. Crop/plant
infections are important factors in reduced production quantity and quality, which leads to …

Invading pests and diseases always degrade the quality and quantity of plants. Early and
accurate identification of plant diseases is critical for plant health and growth. This work …

Rice paddy diseases significantly reduce the quantity and quality of crops, so it is essential
to recognize them quickly and accurately for prevention and control. Deep learning (DL) …

Abstract The emergence of Vision Transformers (ViTs) has marked a significant shift in the
field of computer vision, presenting new methodologies that challenge traditional …

Researchers have designed various models trained on public or private datasets for plant
disease detection to help farmers remedy crop yield losses on their farms due to plant …

Apple tree leaf diseases (ATLDs) can be accurately identified and addressed early to
prevent the diseases from spreading, minimize the need for chemical pesticides and …