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• IMAGE RECOGNITION OF AGRICULTURAL CROPS, PLANTS AND FORESTS

  I. B. Abbasov, Ratnadeep R. Deshmukh

  2020-10-11

  Abstract ▼

  The paper provides an overview of some studies on the recognition of images of crops,
  plants and forests. These image recognition systems use various methods of pre-processing, computer
  vision, and deep learning. Recently recognition systems based on mobile devices are increasing,
  which increases their availability and wide distribution. The articles on recognition,
  classification of fruits and fruits in orchards, the creation of a data bank of these agricultural
  products (apples, pears, kiwi) to assess ripening and yield are considered. The works devoted to
  the automation of harvesting grain crops are described on the example of the work of a combine
  harvester using machine vision. Crop production plays an important role in providing feed for
  animal husbandry; articles on the recognition of agricultural plants based on leaf images are
  analyzed. Also, by the condition of the leaves of potato bushes, you can determine their disease,
  assess the condition of the soil. The work on the development of mobile systems for monitoring and
  recognition of the process of growing mushrooms based on the "green house" technology for
  farms is presented. Using remote diagnostics, you can analyze and monitor the state of the surface
  of land and seas. For remote environmental monitoring of the landscape of the earth's surface,
  work is described on the recognition, classification of forests, water resources using hyperspectral
  analysis of satellite images.

• LULC-ANALYSIS OF LAND-USE WITH THE HELP OF UNSUPERVISED CLASSIFICATION

  Ranjana Waman Gore , Ratnadeep R. Deshmukh, Priyanka U. Randive, Mishra Abhilasha , I. B. Abbasov

  2020-10-11

  Abstract ▼

  Land-use and vegetation cover are the natural state of the earth's surface. Remote sensing is a
  very important land use study (LULC) method. Various classification methods are used to analyze land
  cover in remote sensing. These methods do not require prior information on land cover or land use
  types. Two classification methods are most commonly used to analyze remote sensing images. These
  include controlled classification and uncontrolled classification. The objectives of the proposed work
  are to use unsupervised classification methods to find clusters, determine land use types, and compare
  these methods with interactive analysis of self-organization data (ISODATA). Hyperion sensor images
  were used for land use analysis. The Hyperion sensor has two hundred and forty-two bands, but fewbands provide useful information for spectral analysis. Therefore, bands that do not contain useful information
  are identified and removed. After processing the input image according to this algorithm, out
  of 242 bands, only one hundred and sixty-five bands remain. This takes into account radiometric calibration
  and an important correction of atmospheric factors. Then, based on the results of processing
  using the proposed methods, clusters are formed to study land use using a hyperspectral image. To form
  clusters, the pixels were grouped based on the selected data. Pixels from the same cluster have more
  similarity, while pixels from different clusters differ from each other. Based on the results, it is concluded
  that the clustering method (k-means) allows better identification or prediction of land use based on a
  high-resolution hyperspectral image than the Interactive Self-Organization Data Analysis (ISODATA)
  method. The output image, which is the result of clustering, can be used to identify different types of land
  use objects. The LULC classes predicted are Water Body, Agriculture Land, other Vegetation, Built Up
  or settlement, Bare Land and Rocky region.

• MODERN AVAILABLE PALMPRINT DATABASES: A REVIEW

  Snehal S. Datwase, R.R. Deshmukh, Rohit S. Gupta

  27-37

  2025-07-31

  Abstract ▼

  The palm print is a unique and very useful biometric. A lot of research has been done on this
  topic over the past few decades. Various algorithms and systems have been developed and successfully
  implemented. Since this method does not provide more advanced information for personality
  recognition, multispectral or hyperspectral imaging and handprint recognition could be a
  potential answer to these systems. Biometric technologies have been widely used in the security
  industry for authentication and identification over the past few years. An improved recognition
  system is required to improve accuracy and speed. This article reviews some modern handprint
  databases and describes the methods used and their accuracy. Face, fingerprint, iris, palm print,
  hands are physiological biometric data. Of all biometrics, physiological biometrics offers the most
  benefits. The PolyU-IITD non-contact palm image database compiled with a handheld camera
  includes residents of India and China. The database of IIT Touchless Palmprints is sourced from
  Delhi India students and teachers and consists of complete hand images. The database of
  hyperspectral fingerprints created by the Hong Kong Polytechnic University was collected in the Biometric Research Laboratory Department using Meadowlark liquid crystal filters. The Multispectral
  Fingerprint Database, Hyperspectral Database was compiled by Chinese research teams
  of scientists. The polyU fingerprint database was collected from 193 people and contains
  386 palms. The Chinese Academy of Sciences has developed the CASIA handprint database with
  its own handprint recognition device. The XJTU fingerprint database is collected using iPhone 6S,
  HUAWEI mate8, LG G4, Samsung Galaxy Note5 and MI8 gadgets. A literature review of current
  research in this area is also presented. The advantages of hyperspectral images compared to multispectral
  images are noted, hyperspectral images of palm prints are very difficult to fake