The digital transformation of agriculture has evolved various aspects of management into
artificial intelligent systems for the sake of making value from the ever-increasing data …

The rapid advances in Deep Learning (DL) techniques have enabled rapid detection,
localisation, and recognition of objects from images or videos. DL techniques are now being …

Weeds are among the major threats to cotton production. Overreliance on herbicides for
weed control has accelerated the evolution of herbicide-resistance in weeds and caused …

Fruit detection forms a vital part of the robotic harvesting platform. However, uneven
environment conditions, such as branch and leaf occlusion, illumination variation, clusters of …

Solanum rostratum Dunal is a common invasive alien weed that can damage native
ecosystems and biodiversity. Detecting Solanum rostratum Dunal at an early stage of growth …

Weeds are a crucial threat to agriculture, and in order to preserve crop productivity,
spreading agrochemicals is a common practice with a potential negative impact on the …

Conventional weed management approaches are inefficient and non-suitable for integration
with smart agricultural machinery. Automatic identification and classification of weeds can …

Knowing precise location and having accurate information about weed species is a
prerequisite for develo** an effective site-specific weed management (SSWM) system …

Weeds are one of the main hazards affecting the yield and quality of rice. In farmland
ecosystem, weeds compete with rice for resources such as light, water, soil and space, and …

Weed detection in crops is a new frontier of precision agriculture, which will enable the
distinction between desirable and undesirable plants. Accurate and efficient weed detection …