Aerial image processing and object recognition

Hama tikus adalah hama yang dapat menyebabkan kegagalan panen tanaman padi. Metode yang digunakan untuk mengukur besaran serangan hama tikus adalah metode pengambilan contoh dan pendekatan foto udara. Namun dari kedua metode ini tingkat serangan yang dihasilkan belum diketahui korelasinya. Maka dari itu dilakukannya penelitian ini untuk mendapatkan hubungan antara intensitas dan luas serangan hama tikus tanaman padi. Tahapan penelitian ini adalah survei lokasi yang terserang hama tikus, persiapan alat, pengambilan foto udara, pengambilan sampel untuk perhitungan intensitas serangan, pengolahan citra, perhitungan luas serangan, analisis regresi dan validasi. Intensitas serangan dihitung menggunakan perhitungan secara mutlak, sedangkan luas serangan dihitung menggunakan metode pengolahan citra foto udara yang dikembangkang oleh Widodo. Analisis regresi menunjukan bahwa hubungan antara intensitas serangan dengan luas serangan memiliki koefisien determinasi 0,889 dan persamaan regresi yang diperoleh y = 1,138x dengan faktor kesalahan 8,947%. Intensitas serangan hama tikus tanaman padi menggunakan metode pengambilan contoh berhubungan linier dengan luas serangan hasil analisis foto udara yang dikembangkan oleh Widodo. Rat pests are pests that can cause crop failure in rice plant. The method used to calculate the number of rodent pest attacks is the method of sampling and obtaining aerial photographs. But from these two methods the level of attack produced is not known to correlate. So this study purpose to obtain a relationship between intensity of attack with area of attack rat pest of rice plants. The stages of this study were location surveys that were attacked by rat pests, preparation of tools, aerial photography, and sampling for the calculation of attack intensity, image processing, area attack, regression analysis and validation. The intensity of attacks is calculated using total calculations, while broad attacks are calculated using the aerial image processing method developed by Widodo. Regression analysis shows the relationship between the intensity of ??attack with the area of ??attack has a determination coefficient of 0.889 and the regression coefficient obtained y = 1.138x with an error factor of 8.947%. The intensity of rat pest attacks using linear related sampling methods with broad attack results from aerial photo analysis developed by Widodo.

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AN ABDUCTIVE REASONING BASED IMAGE INTERPRETATION SYSTEM

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001496000372 ◽

1996 ◽

Vol 10 (06) ◽

pp. 613-641

Author(s):

SANTANU CHAUDHURY ◽

ARBIND GUPTA ◽

GUTURU PARTHASARATHY ◽

S. SUBRAMANIAN

Keyword(s):

Object Recognition ◽

Domain Knowledge ◽

A Priori ◽

Image Interpretation ◽

Abductive Reasoning ◽

Recognition Problem ◽

Aerial Image ◽

Interpretation System ◽

Different Types

This paper describes an abductive reasoning based inferencing engine for image interpretation. The inferencing strategy finds an acceptable and consistent explanation of the features detected in the image in terms of the objects known a priori. The inferencing scheme assumes representation of the domain knowledge about the objects in terms of local and/or relational features. The inferencing system can be applied for different types of image interpretation problems like 2-D and 3-D object recognition, aerial image interpretation, etc. In this paper, we illustrate functioning of the system with the help of a 2-D object recognition problem.

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Position, rotation and scale invariant object recognition by digital image processing

Signal Processing ◽

10.1016/0165-1684(86)90011-3 ◽

1986 ◽

Vol 11 (3) ◽

pp. 311-312

Author(s):

Licia Capodiferro

Keyword(s):

Image Processing ◽

Object Recognition ◽

Digital Image Processing ◽

Digital Image ◽

Scale Invariant ◽

Invariant Object Recognition

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High-speed aerial image processing system based on DSP

10.1117/12.441686 ◽

2001 ◽

Author(s):

Haiju Lei ◽

Dehua Li ◽

Hanping Hu ◽

Zhaonan Guo

Keyword(s):

Image Processing ◽

High Speed ◽

Processing System ◽

Aerial Image ◽

Image Processing System

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PLANT LEAF IMAGE RECOGNITION SYSTEM

International Journal of Multidisciplinary Research Configuration ◽

10.52984/ijomrc1108 ◽

2021 ◽

Vol 1 (1) ◽

pp. 35-44

Author(s):

Gaurav Kulkarni ◽

◽

Chandrashekhar Kumbhar

Keyword(s):

Image Processing ◽

Object Recognition ◽

Image Recognition ◽

Recognition System ◽

Vital Role ◽

Rare Plant ◽

Plant Recognition ◽

Identification Techniques ◽

Leaf Image ◽

End Results

Plants play a vital role in our day-to-day life. Hence, a good understanding of plants is needed to help in identifying new or rare plant species. Such identification will in turn improve the drug industry, balance the ecosystem as well as the agricultural productivity and sustainability. We often come across various plants with different variety of leaves and flowers every single day. We try to recognize it, but we fail. So we need some system which can tell us about the leaf/flower instantly. So, to solve such problems, we introduce a plant recognition system (PRS) which tells you the details about a leaf by just uploading the image of the leaf. For this system, we use image processing and some identification techniques which can recognize the leaf by its structure, colour, shape etc and fetch the details about it and provide the details of it to the user. This paper gives a understanding about the different methods used under image processing and various methods and algorithm used to identify that leaf in a short and simple way. Object recognition and detection are techniques with similar end results and implementation approaches. Therefore, it requires heavy pre-processing and implements various processes to obtain the end results.

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