The efficient identification of rice pests and diseases is crucial for preventing crop damage.
To address the limitations of traditional manual detection methods and machine learning …

Crop disease detection is important due to its significant impact on agricultural productivity
and global food security. Traditional disease detection methods often rely on labour …

The increased demand for food is accelerating plant diseases globally. Hence, a manual
process of detection of plant diseases is almost impossible. Artificial intelligence (AI) can …

In addition to causing production losses, plant diseases lead to environmental harm due to
the indiscriminate use of pesticides. Once limited to disease detection, Convolutional Neural …

This method of identifying plant leaf disease generally involves a large team of experts with
extensive knowledge of plant diseases, and it can be expensive, time-consuming, and …

Yellow vein mosaic virus (YVMV) is a destructive plant virus that commonly affects crops,
particularly okra, in India. The virus is transmitted by whiteflies and poses significant …

In order to maintain sustainable agriculture, it is vital to monitor plant health. Since all
species of plants are prone to characteristic diseases, it necessitates regular surveillance to …

Using deep learning-based tools in the field of agriculture for the automatic detection of plant
leaf diseases has been in place for many years. However, optimizing their use in the specific …

Cotton plant disease identification poses challenges due to image acquisition limitations,
diverse illnesses, lack of labelled data, and cost constraints. Despite these obstacles, early …

In India, agricultural production is a crucial factor for economic growth. However, plants can
be attacked by diseases that range from mild to severe, leading to their destruction …