Morphology-based guidance line extraction for an autonomous weeding robot in paddy fields

Choi, Keun Ha; Han, Shuangfeng; Han, Sang Hoon; Park, Kwang-Ho; Kim, Kyung-Soo; Kim, Soohyun

doi:10.1016/j.compag.2015.02.014

Cited by 88 publications

(41 citation statements)

References 21 publications

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“…Imaging techniques are based on camera systems (CCD camera, bi-, multi- or hyper-spectral) with appropriate optical components and post-processing software. Plant contours can be detected if plants are freestanding [ 27 ]. The use of IR-channel shows good results to differentiate between soil and plants [ 28 , 29 , 30 ].…”

Section: State Of the Artmentioning

confidence: 99%

A True-Color Sensor and Suitable Evaluation Algorithm for Plant Recognition

Schmittmann

Lammers²

2017

Sensors

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Plant-specific herbicide application requires sensor systems for plant recognition and differentiation. A literature review reveals a lack of sensor systems capable of recognizing small weeds in early stages of development (in the two- or four-leaf stage) and crop plants, of making spraying decisions in real time and, in addition, are that are inexpensive and ready for practical use in sprayers. The system described in this work is based on free cascadable and programmable true-color sensors for real-time recognition and identification of individual weed and crop plants. The application of this type of sensor is suitable for municipal areas and farmland with and without crops to perform the site-specific application of herbicides. Initially, databases with reflection properties of plants, natural and artificial backgrounds were created. Crop and weed plants should be recognized by the use of mathematical algorithms and decision models based on these data. They include the characteristic color spectrum, as well as the reflectance characteristics of unvegetated areas and areas with organic material. The CIE-Lab color-space was chosen for color matching because it contains information not only about coloration (a- and b-channel), but also about luminance (L-channel), thus increasing accuracy. Four different decision making algorithms based on different parameters are explained: (i) color similarity (ΔE); (ii) color similarity split in ΔL, Δa and Δb; (iii) a virtual channel ‘d’ and (iv) statistical distribution of the differences of reflection backgrounds and plants. Afterwards, the detection success of the recognition system is described. Furthermore, the minimum weed/plant coverage of the measuring spot was calculated by a mathematical model. Plants with a size of 1–5% of the spot can be recognized, and weeds in the two-leaf stage can be identified with a measuring spot size of 5 cm. By choosing a decision model previously, the detection quality can be increased. Depending on the characteristics of the background, different models are suitable. Finally, the results of field trials on municipal areas (with models of plants), winter wheat fields (with artificial plants) and grassland (with dock) are shown. In each experimental variant, objects and weeds could be recognized.

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Section: State Of the Artmentioning

confidence: 99%

A True-Color Sensor and Suitable Evaluation Algorithm for Plant Recognition

Schmittmann

Lammers²

2017

Sensors

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“…About the last step, a variety of navigation line detection methods have been proposed in recent years. Generally, these methods are classified into several categories according to their detection principles, such as HT (Hough transform), LR (linear regression), SA (speckle analysis), SV (stereo vision), and HF (horizontal fringes) [16][17][18][19][20]. It is worth noting that the crop row is approximated as a straight line in the above methods.…”

Section: Introductionmentioning

confidence: 99%

Research on Vision-Based Navigation for Plant Protection UAV under the Near Color Background

et al. 2019

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GPS (Global Positioning System) navigation in agriculture is facing many challenges, such as weak signals in orchards and the high cost for small plots of farmland. With the reduction of camera cost and the emergence of excellent visual algorithms, visual navigation can solve the above problems. Visual navigation is a navigation technology that uses cameras to sense environmental information as the basis of an aircraft flight. It is mainly divided into five parts: Image acquisition, landmark recognition, route planning, flight control, and obstacle avoidance. Here, landmarks are plant canopy, buildings, mountains, and rivers, with unique geographical characteristics in a place. During visual navigation, landmark location and route tracking are key links. When there are significant color-differences (for example, the differences among red, green, and blue) between a landmark and the background, the landmark can be recognized based on classical visual algorithms. However, in the case of non-significant color-differences (for example, the differences between dark green and vivid green) between a landmark and the background, there are no robust and high-precision methods for landmark identification. In view of the above problem, visual navigation in a maize field is studied. First, the block recognition method based on fine-tuned Inception-V3 is developed; then, the maize canopy landmark is recognized based on the above method; finally, local navigation lines are extracted from the landmarks based on the maize canopy grayscale gradient law. The results show that the accuracy is 0.9501. When the block number is 256, the block recognition method achieves the best segmentation. The average segmentation quality is 0.87, and time is 0.251 s. This study suggests that stable visual semantic navigation can be achieved under the near color background. It will be an important reference for the navigation of plant protection UAV (Unmanned Aerial Vehicle).

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“…In the study of Bao et al [6] , a trapezoid prediction model was first used to detect the edge shape characteristics of the road, and then an improved support vector machine (SVM) classifier was utilized to recognize the road connected area. Choi et al [7] focused on the study of texture, morphology and other geometric features of the work area to overcome the impact of environmental changes on road images. In addition, neural network has also been introduced into the study of route identification and obstacle location [8,9] .…”

Section: Introductionmentioning

confidence: 99%

Depth recovery for unstructured farmland road image using an improved SIFT algorithm

Yao¹,

Dong²,

Yang³

et al. 2019

International Journal of Agricultural and Biological Engineering

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Road visual navigation relies on accurate road models. This study was aimed at proposing an improved scale-invariant feature transform (SIFT) algorithm for recovering depth information from farmland road images, which would provide a reliable path for visual navigation. The mean image of pixel value in five channels (R, G, B, S and V) were treated as the inspected image and the feature points of the inspected image were extracted by the Canny algorithm, for achieving precise location of the feature points and ensuring the uniformity and density of the feature points. The mean value of the pixels in 5×5 neighborhood around the feature point at an interval of 45º in eight directions was then treated as the feature vector, and the differences of the feature vectors were calculated for preliminary matching of the left and right image feature points. In order to achieve the depth information of farmland road images, the energy method of feature points was used for eliminating the mismatched points. Experiments with a binocular stereo vision system were conducted and the results showed that the matching accuracy and time consuming for depth recovery when using the improved SIFT algorithm were 96.48% and 5.6 s, respectively, with the accuracy for depth recovery of-7.17%-2.97% in a certain sight distance. The mean uniformity, time consuming and matching accuracy for all the 60 images under various climates and road conditions were 50%-70%, 5.0-6.5 s, and higher than 88%, respectively, indicating that performance for achieving the feature points (e.g., uniformity, matching accuracy, and algorithm real-time) of the improved SIFT algorithm were superior to that of conventional SIFT algorithm. This study provides an important reference for navigation technology of agricultural equipment based on machine vision.

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Morphology-based guidance line extraction for an autonomous weeding robot in paddy fields

Cited by 88 publications

References 21 publications

A True-Color Sensor and Suitable Evaluation Algorithm for Plant Recognition

A True-Color Sensor and Suitable Evaluation Algorithm for Plant Recognition

Research on Vision-Based Navigation for Plant Protection UAV under the Near Color Background

Depth recovery for unstructured farmland road image using an improved SIFT algorithm

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