Deep Learning with Unsupervised Data Labeling for Weeds Detection on UAV Images

Bah, Mamadou Dian; Hafiane, Adel; Canals, Raphaël

doi:10.20944/preprints201809.0088.v1

Cited by 83 publications

(9 citation statements)

References 30 publications

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“…Without any prior knowledge on the species present in the field, a naive Bayesian classifier and a Gaussian mixture clustering algorithm discriminated 85% of the weed of multiple species [81]. Two weed species were discriminated with an overall accuracy of 98.40% based on the Bayes classifier and the execution time for each image is about 35 millisecond [82]. Then, an automated image classification system differentiated crops and weeds in sugarcane fields with 92.9% accuracy over a processing time of 20 millisecond [83].…”

Section: Rgb Imagingmentioning

confidence: 99%

Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review

2020

Smart Cities

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Crop productivity is readily reduced by competition from weeds. It is particularly important to control weeds early to prevent yield losses. Limited herbicide choices and increasing costs of weed management are threatening the profitability of crops. Smart agriculture can use intelligent technology to accurately measure the distribution of weeds in the field and perform weed control tasks in selected areas, which cannot only improve the effectiveness of pesticides, but also increase the economic benefits of agricultural products. The most important thing for an automatic system to remove weeds within crop rows is to utilize reliable sensing technology to achieve accurate differentiation of weeds and crops at specific locations in the field. In recent years, there have been many significant achievements involving the differentiation of crops and weeds. These studies are related to the development of rapid and non-destructive sensors, as well as the analysis methods for the data obtained. This paper presents a review of the use of three sensing methods including spectroscopy, color imaging, and hyperspectral imaging in the discrimination of crops and weeds. Several algorithms of machine learning have been employed for data analysis such as convolutional neural network (CNN), artificial neural network (ANN), and support vector machine (SVM). Successful applications include the weed detection in grain crops (such as maize, wheat, and soybean), vegetable crops (such as tomato, lettuce, and radish), and fiber crops (such as cotton) with unsupervised or supervised learning. This review gives a brief introduction into proposed sensing and machine learning methods, then provides an overview of instructive examples of these techniques for weed/crop discrimination. The discussion describes the recent progress made in the development of automated technology for accurate plant identification as well as the challenges and future prospects. It is believed that this review is of great significance to those who study automatic plant care in crops using intelligent technology.

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Section: Rgb Imagingmentioning

confidence: 99%

Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review

2020

Smart Cities

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“…The composition of weeds is a mixture of grasses, sedges and broadleaves which often change according to the crop growth stages which provide specific climatic and environmental condition suitable for specific weed growth [5]. The shade provided by the mustard canopy influence the nature of weeds composition, and grass species tend to dominate as the mustard plant get bigger is difficult [6] because of their long economic life but they affect the growth of crops or cause yield losses [7]. Weeds in plantation are managed using several methods such as cultural, mechanical, integrated production system using livestock to control the weeds, or chemical (herbicides).…”

Section: Introductionmentioning

confidence: 99%

Weeds Detection in Agricultural Fields using Convolutional Neural Network

Ngo¹,

Hashim²,

Sek³

et al. 2019

IJITEE

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Weeds are very annoying for farmers and also not very good for the crops. Its existence might damage the growth of the crops. Therefore, weed control is very important for farmers. Farmers need to ensure their agricultural fields are free from weeds for at least once a week, whether they need to spray weeds herbicides to their plantation or remove it using tools or manually. The aim of this research is to build an automated weed control robot using the Lego Mindstorm EV3 which connected to a computer. The robot consists of motors, servo motors and a camera which we use to capture the image of the crops and weeds. An automated image classification system has been designed to differentiate between weeds and crops. The robot will spray the weed herbicides directly to the area that have been detected weeds near or at it. For the image classification method, we employ the convolutional neural network algorithm to process the image of the object. Therefore, by the use of technology especially in artificial intelligence, farmers can reduce the amount of workload and workforce they need to monitor their plantation. In addition, this technology also can improve the quality of the crops.

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“…where A ∈ R 6×6 and B ∈ R 6×3 seted as: (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) with reference to (4)(5)(6)(7)(8)(9)(10)(11), the following control signal u(t) ∈ R 3 is noted aṡ (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22) where Λ w ∈ R 3×6 = [Λ w1 Λ w2 ] and Λ w1 and Λ w2 are positive definite gain…”

Section: Hvs Modeling Approachmentioning

confidence: 99%

“…whereφ is an estimation of ϕ to ensure the Hurwitz property ofÃ l (4-24), thisφ estimation will be discussed later, and setting two discrete state values under the constraint (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21), the following operating regions into the radiant space are obtained X 0 = [ −π 2 , ..., π 2 ], X 1 = [φ − π 2 , ...,φ + π 2 ]. Analyzing the continuous system state ζ we have that for any t i ∈ [0, t M ] and, by using the comparison lemma [41], we obtain:…”

Section: Hybrid Control Designmentioning

confidence: 99%

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A Robust Visual Servoing Approach for Robotic Fruit Harvesting

CAMACHO¹

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In this work, we present different eye-in-hand visual servoing control schemes applied to a robotic harvesting task of soft fruits in the presence of parametric uncertainties in the system models. The first scheme combines position-based visual servoing (PBVS) and image-based visual servoing (IBVS) approaches in order to perform respectively an approach phase to the fruit and then a fine tuning of the end-effector to harvest. The second scheme uses a hybrid visual servoing (HVS) approach to fulfill the complete harvesting task, by designing a suitable control law which combines error vectors defined in both the image and operational spaces. For detecting the fruits, an algorithm based on the combination of the OHTA color space and Otsu's threshold method for a fast recognition of mature fruits in complex scenarios. In addition, a more accurate detection method employs a pre-trained deep encoder-decoder algorithm based on a minimized Segnet version for a fast and cheap inference during the task execution. The object localization is accomplished by employing an image triangulation technique, which combines the speeded-up-robust-features (SURF) and the-randomsample-consensus (RANSAC) or the Oriented FAST and Rotated BRIEF and the Brute-Force Matcher (BF-Matcher) algorithms to extract the fruit image feature and match it to its correspondent feature-point into the other view of the stereo camera. However, since these algorithms are computationally expensive for the task requirements, a faster estimation method uses the fruit centroid and a homogeneous transformation for discovering matching points. Finally, a vision-based sliding-mode-control scheme and a switching monitoring function are employed to cope with uncertainties in the calibration parameters of the camera-robot system. In this context, it is possible to guarantee the asymptotic stability and convergence of the image feature error, even if the misalignment angle, around the z-axis, between the camera and end-effector frames is uncertain. 3D computer simulations and preliminary experimental results, obtained with a Mitsubishi robot arm RV-2AJ carrying out a simple strawberry picking task, are included to illustrate the performance and effectiveness of the proposed control scheme.

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Deep Learning with Unsupervised Data Labeling for Weeds Detection on UAV Images

Cited by 83 publications

References 30 publications

Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review

Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review

Weeds Detection in Agricultural Fields using Convolutional Neural Network

A Robust Visual Servoing Approach for Robotic Fruit Harvesting

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