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  • Automatic Counting and Location Labeling of Rice Seedlings from . . .
    Traditional counting of rice seedlings in agriculture is often labor-intensive, time-consuming, and prone to errors Therefore, agricultural automation has gradually become a prominent solution In this paper, UVA detection, combining deep learning with unmanned aerial vehicle (UAV) sensors, contributes to precision agriculture We propose a YOLOv4-based approach for the counting and location
  • Automatic Counting of in situ Rice Seedlings from UAV Images . . . - MDPI
    The number of rice seedlings in the field is one of the main agronomic components for determining rice yield This counting task, however, is still mainly performed using human vision rather than computer vision and is thus cumbersome and time-consuming A fast and accurate alternative method of acquiring such data may contribute to monitoring the efficiency of crop management practices, to
  • Computers and Electronics in Agriculture - ResearchGate
    various agricultural viewpoints but remains challenging because rice seeds have a similar visual appearance Thus, studies on rice seed cultivar classification play a key role in achieving a
  • Computers and Electronics in Agriculture - Sci-Hub
    carry forward the research for identification of rice diseases which appear in the western region of Odisha In the western tract of Odisha, mostly four types of rice diseases have appeared, i e bacterial blight, blast, brown spot and tungro Generally, the identification of rice diseases is supervised either by vi-
  • Rice Seedling Detection in UAV Images Using Transfer Learning and . . .
    To meet demand for agriculture products, researchers have recently focused on precision agriculture to increase crop production with less input Crop detection based on computer vision with unmanned aerial vehicle (UAV)-acquired images plays a vital role in precision agriculture In recent years, machine learning has been successfully applied in image processing for classification, detection
  • Computers and Electronics in Agriculture - ResearchGate
    Computers and Electronics in Agriculture rice (Oryza sativa), and alfalfa (Jia et al , 2022; Rahman and Cho, selected seedlings were then cleaned and dried to remove excess water
  • Computers and Electronics in Agriculture - Sci-Hub
    sification of rice varieties is given in Fig 1 2 1 Datasets Datasets belonging to five rice varieties as Arborio, Basmati, Ipsala, Jasmine and Karacadag, which are often cultivated in Turkey, were used in the study The first image dataset consists of 75,000 rice grain images, 15,000 from each varieties In RGB images contained in this
  • Deep Learning-Based Seedling Row Detection and Localization . . . - MDPI
    Accurately and precisely obtaining field crop information is crucial for evaluating the effectiveness of rice transplanter operations However, the working environment of rice transplanters in paddy fields is complex, and data obtained solely from GPS devices installed on agricultural machinery cannot directly reflect the specific information of seedlings, making it difficult to accurately


















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