Detection of Two Types of Weed through Machine Vision System: Improving Site-Specific Spraying

Journal Title: Journal of Agricultural Machinery - Year 2018, Vol 8, Issue 1

Abstract

Introduction With increase in world population, one of the approaches to provide food is using site-specific management system or so-called precision farming. In this management system, management of crop production inputs such as fertilizers, lime, herbicides, seed, etc. is done based on farm location features, with the aim of reducing waste, increasing revenues and maintaining environmental quality. Precision farming involves various aspects and is applicable on farm fields at all stages of tillage, planting, and harvesting. Today, in line with precision farming purposes, and to control weeds, pests, and diseases, all the efforts of specialists in precision farming is to reduce the amount of chemical substances in products. Although herbicides improve the quality and quantity of agricultural production, the possibility of applying inappropriately and unreasonably is very high. If the dose is too low, weed control is not performed correctly. Otherwise, If the dosage is too high, herbicides can be toxic for crops, can be transferred to soil and stay in it for a long time, and can penetrate to groundwater. By applying herbicides to variable rate, the potential for significant cost savings and reduced environmental damage to the products and environment will be possible. It is evident that in large-scale modern agriculture, individual management of each plant without using some advanced technologies is not possible. using machine vision systems is one of precision farming techniques to identify weeds. This study aimed to detect three plant such as Centaurea depressa M.B, Malvaneglecta and Potato plant using machine vision system. Materials and Methods In order to train algorithm of designed machine vision system, a platform that moved with the speed of 10.34 was used for shooting of Marfona potato fields. This platform was consisted of a chassis, camera (DFK23GM021,CMOS, 120 f/s, Made in Germany), and a processor system equipped with Matlab 2015 version. The video camera was installed in 60-centimeter height above the ground level. Therefore, all plants in the camera field of view (whether on the crops row or between the rows) were analyzed. This study conducted on 4 hectares of potato fields in Kermanshah–Iran (longitude: 7.03 E; latitude: 4.22 N). The most suitable color space for segmentation plants was HSV color space and most suitable channel of applying threshold was the H channel. In this study, features in two areas of color features, texture features based on gray co-occurrence matrix were extracted. Ultimately, 126 color features and 80 texture features were extracted from each object. In final six features among 206 features were selected. Results and Discussion Among 206 extracted features, six effective features including the additional second component of the YCbCr color space, green index minus blue in RGB color space, sum entropy in the neighborhood of 45 degree, diagonal moment in the neighborhood of 0 degree, entropy in the neighborhood of 45 degree, additional third component index in CMY color space were selected using hybrid ANN-PSO. This means that, two set features have the same effect over plants. The result shows that hybrid ANN-SAGA classified Centaurea depressa M.B, Malvaneglecta and Potato plant with 99.61% accuracy. This accuracy is high and this meant that 1. These plants have different 6 selected features, 2. The classifier is very powerful to classify. Conclusion 1. Plants with similar features make the classification process complicated and less accurate. 2. The presence of shadow on the plants’ leaves reduces the accuracy of the classification.

Authors and Affiliations

S. Sabzi,Y. Abbaspour Gilandeh,H. Javadikia,

Keywords

Related Articles

Investigating the Effect of Increasing Nano Cellulose to Diesel Fuel on Emission and Performance of Internal Combustion Engine

IntroductionToday, the number of diesel engines is increasing due to their high efficiency and low greenhouse gases. In the present study, the effect of adding nano cellulose as nanoparticles to diesel fuel on the perfor...

Construction and Assessment of an on the Go Soil Electrical

The issue of soil salinity is one of the snags for increasing agricultural productivity, which must be inhibited by appropriate devise and scientific management. One way to identify salty areas of farm lands is to prepar...

Study and modeling of changes in volumetric efficiency of helix conveyors at different rotational speeds and inclination angels by ANFIS and statistical methods

Introduction Spiral conveyors effectively carry solid masses as free or partly free flow of materials. They create good throughput and they are the perfect solution to solve the problems of transport, due to their simple...

Investigation of the Effects of Tire Inflation Pressure and Forward Speed of Driven Wheel on Horizontal Impact of Passing Rectangular Obstacle

The tire-mechanics models have been developed for the study of wheel movement on the road or soil surface while these models are unlikely to describe the motion of wheels on uneven surfaces. Due to dynamical complexity o...

Optimization of hydrodynamic cavitations reactor efficiency for biodiesel production by response surface methods (Case study: Sunflower oil)

Introduction Biofuels are considered as one of the largest sources of renewable fuels or replacement of fossil fuels. Combustion of plant-based fuels is the indirect use of solar energy. Biofuels significantly have less...

Download PDF file
  • EP ID EP717889
  • DOI -
  • Views 49
  • Downloads 0

How To Cite

S. Sabzi, Y. Abbaspour Gilandeh, H. Javadikia, (2018). Detection of Two Types of Weed through Machine Vision System: Improving Site-Specific Spraying. Journal of Agricultural Machinery, 8(1), -. https://europub.co.uk/articles/-A-717889