Weed Detection utilizing Quadratic Polynomial and ROI Techniques

Abstract

Machine vision for selective weeding or selective herbicide spraying relies substantially on the ability of the system to analyze weed images and process the extracted knowledge for decision making prior to implementing the identified control action. To control weed, different weed type would require different herbicide formulation. Consequently the weed must be identified and classified accordingly. In this work, weed images were classified as either broad or narrow weed type. A fundamental problem in weed image recognition using planar curve analysis is to detect curve. It is difficult to successfully extract curve from the image of weed edges since the appropriate scale to use for extraction is not known a priori As such, this paper considers a curve detection method based on the quadratic polynomial technique which include the use of the region-of-interests (ROI) technique. The ROI technique creates image subsets by selecting regions of the displayed image. The ROIs are typically used to extract statistics for image operations such as classification. As such, the objective of this paper is to present a novel application of curve detection feature extraction technique in weed classification.

A Survey of Deep Learning Techniques for Weed Detection from Images

“…Before training the models, they applied vegetation separation techniques to remove background and different spectral pre-processing to extract features using Principal Component Analysis (PCA). Ishak et al (2007) extracted different shape features and the feature vectors were evaluated using a single-layer perceptron classifier to distinguish narrow and broad-leafed weeds.…”

“…Abdalla et al (2019),Adhikari et al (2019),Andrea et al (2017),Asad and Bais (2019),Bini et al (2020),Bosilj et al (2020),Brilhador et al (2019),Chechlinski et al (2019), Farooq et al (2019), Fawakherji et al (2019), Hall et al (2018, H Huang et al (2018a), H. Huang et al (2018b,. H Huang et al (2020),Ishak et al (2007),Knoll et al (2019),Kounalakis et al (2019),Lam et al (2020),Liakos et al (2018),Lottes et al (2020),Lottes et al (2018b), Milioto et al (2017,Osorio et al (2020),Patidar et al (2020),Ramirez et al (2020),Rist et al (2019), andSa et al (2018),Skovsen et al (2019),Umamaheswari and Jain (2020)…”

Raspberry Pi based crop and weed classification using Machine Learning Algorithm

“…The bot is designed to take the input from the image captured and the Bot is moved through the steps of the stepper motor calculate using the inverse kinematics. The same has been tried with Dilation and Erosion algorithm in [13] and using the ROI and quadratic polynomial in [14]. In general every work has a image processing unit, weed identification unit and the weed removal unit or a sprayer unit.…”

Automatic Weed Detection and Smart Herbicide Sprayer Robot

“…For the weed, the threshold value will be more because of broad leaves. So we set a condition, that if the threshold value of input image is greater than the set threshold value, our image is weed image, else there is no weed in the image [5]. The threshold value here taken is around 90000.…”