Recent advancements in deep learning (DL)-based model optimization techniques have
resulted in better weed identification accuracy. However, optimizing these models to identify …

In the present study, we used device visualization in tandem with deep learning to detect
weeds in the wheat crop system in actual time. We selected the PMAS Arid Agriculture …

The Indian economy relies heavily on the cultivation of rice. As the global population grows,
so does the need for rice farming. To maximise rice crop growth, early detection of pests is …

One of the contributions of computer vision is precision agriculture, which uses high
technology to ensure good production and profitability in agribusiness. Its techniques can be …

Pests are divided into two as herbal and animal pests in agriculture, and detection and use
of minimum pesticides are quite challenging task. Last three decades, researchers have …

In the face of a focus on development, a farming residue a most powerful sector of an Indian
financial management system both regarding the benefaction to gross domestic product …

Pest infection is the most crucial problem in vegetable plants. One way to control pest
infection is to use proper pesticides. Early detection of the pest or the initial presence of …

The use of deep learning models to identify lesions on cotton leaves on the premise of
images of the crop within the field is proposed in this article. Its cultivation in tropical regions …

Abstract Development of automated in-row weed control is one of the costliest and
complicated tasks in agricultural industry despite the rapid development of agricultural …