A real-time plant discrimination system utilising discrete reflectance spectroscopy

Symonds, P.; Paap, Arie; Alameh, Kamal; Rowe, J. E.; Miller, Carl C.

doi:10.1016/j.compag.2015.07.011

Cited by 20 publications

(11 citation statements)

References 9 publications

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“…Shape features for object identification typically require invariance, which indicates that the values of the shape features will not be changed after the object is translated, rotated, rescaled, or repositioned [13]. Therefore, four kinds of features were determined in the study, including area, shape index, length/width, and length.…”

Section: Spectral Shape and Texture Feature Extraction Of Plant Objmentioning

confidence: 99%

“…Up to date, there are many studies on identification of weeds from crops using the sensitive spectral bands with encouraging results. However, the identification accuracy is low in cases when the spectral difference between the crop and the weed is not obvious, or the reflection of leaves is affected by factors of water content, plant disease, and growth stage [9][10][11][12][13]. Therefore, to more effectively discriminate weeds from crops, the combination of multiple features, such as the combination of shape and textural, shape and spectral, and spectral and textural features, should be considered.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Corn and Weed Species by the Combination of Spectral, Shape and Textural Features

et al. 2017

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Accurate detection of weeds in farmland can help reduce pesticide use and protect the agricultural environment. To develop intelligent equipment for weed detection, this study used an imaging spectrometer system, which supports micro-scale plant feature analysis by acquiring high-resolution hyper spectral images of corn and a number of weed species in the laboratory. For the analysis, the object-oriented classification system with segmentation and decision tree algorithms was utilized on the hyper spectral images to extract shape and texture features of eight species of plant leaves, and then, the spectral identification characteristics of different species were determined through sensitive waveband selection and using vegetation indices calculated from the sensitive band data of the images. On the basis of the comparison and analysis of the combined characteristics of spectra, shape, and texture, it was determined that the spectral characteristics of the ratio vegetation index of R677/R710 and the normalized difference vegetation index, shape features of shape index, area, and length, as well as the texture feature of the entropy index could be used to build a discrimination model for corn and weed species. Results of the model evaluation showed that the Global Accuracy and the Kappa coefficient of the model were both over 95%. In addition, spectral and shape features can be regarded as the preferred characteristics to develop a device of weed identification from the view of accessibility to crop/weeds discriminant features, according to different roles of various features in classifying plants. Therefore, the results of this study provide valuable information for the portable device development of intelligent weed detection.

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Section: Spectral Shape and Texture Feature Extraction Of Plant Objmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Corn and Weed Species by the Combination of Spectral, Shape and Textural Features

et al. 2017

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“…Field images were captured by an integrated weed sensing system with the combination of multispectral and spatial sensors at a commercial farm in Cunderdin, Western Australia, shown in Figure 5. This hardware system, which is housed at the Electron Science Research Institute (ESRI), Edith Cowan University, Australia, consists of two components (i) a Xilinx Zynq ZC702 development board with a VITA 2000 camera sensor and (ii) a Plan Discrimination Unit (PDU) [15] based on spectral reflectance measurements. We collected a "fieldtrip_can_weeds" dataset (published online) under different weather conditions (cloudy, windy, and sunny) and illumination variations (morning and afternoon light).…”

Section: Field Data Collectionmentioning

confidence: 99%

“…However, the performance of computer vision algorithms is greatly dependent on the selection of an appropriate set of features [9]. Particularly, the key characteristics of vegetation (crops and weeds), which comprise biological morphology [10][11][12], spectral features [13][14][15], spatial contexts [16][17][18] and visual textures [19][20][21] can be extracted by applying different characterization methods. Each of these characteristics has its own advantages, and depends on the complexity of the generated datasets for plant species.…”

Section: Introductionmentioning

confidence: 99%

Performances of the LBP Based Algorithm over CNN Models for Detecting Crops and Weeds with Similar Morphologies

Ahderom

Alameh

2020

Sensors

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Weed invasions pose a threat to agricultural productivity. Weed recognition and detection play an important role in controlling weeds. The challenging problem of weed detection is how to discriminate between crops and weeds with a similar morphology under natural field conditions such as occlusion, varying lighting conditions, and different growth stages. In this paper, we evaluate a novel algorithm, filtered Local Binary Patterns with contour masks and coefficient k (k-FLBPCM), for discriminating between morphologically similar crops and weeds, which shows significant advantages, in both model size and accuracy, over state-of-the-art deep convolutional neural network (CNN) models such as VGG-16, VGG-19, ResNet-50 and InceptionV3. The experimental results on the “bccr-segset” dataset in the laboratory testbed setting show that the accuracy of CNN models with fine-tuned hyper-parameters is slightly higher than the k-FLBPCM method, while the accuracy of the k-FLBPCM algorithm is higher than the CNN models (except for VGG-16) for the more realistic “fieldtrip_can_weeds” dataset collected from real-world agricultural fields. However, the CNN models require a large amount of labelled samples for the training process. We conducted another experiment based on training with crop images at mature stages and testing at early stages. The k-FLBPCM method outperformed the state-of-the-art CNN models in recognizing small leaf shapes at early growth stages, with error rates an order of magnitude lower than CNN models for canola–radish (crop–weed) discrimination using a subset extracted from the “bccr-segset” dataset, and for the “mixed-plants” dataset. Moreover, the real-time weed–plant discrimination time attained with the k-FLBPCM algorithm is approximately 0.223 ms per image for the laboratory dataset and 0.346 ms per image for the field dataset, and this is an order of magnitude faster than that of CNN models.

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“…For instance, a number of studies assume a controlled greenhouse condition [9]. Use of different hardware setups and fundamentally different algorithms for monitoring different crop stresses is also another problem in the reported work [10], [11]. For the solution to be optimal, it should be easily adaptable in different situations with minimal changes.…”

Section: Issn: 2319 -1058mentioning

confidence: 99%

Suitable Image Features for Drought Stress Dection In Beans Using Raspberry Pi Imaging System

2017

IJIET

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-Crop stresses are often detected late and this leads to massive crop yield loss. There exists a need for development of a real time system for monitoring crop growth progress with a view of early detection of diseases and other forms of crop stress. In this work, a simple raspberry pi imaging system is developed and used to capture images of beans subjected to drought stress. The images are used to extract features for classification of the stress. The features are then used to train classifiers for drought stress detection. Detection accuracy of between 90 to 100% has been achieved.

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A real-time plant discrimination system utilising discrete reflectance spectroscopy

Cited by 20 publications

References 9 publications

Detection of Corn and Weed Species by the Combination of Spectral, Shape and Textural Features

Detection of Corn and Weed Species by the Combination of Spectral, Shape and Textural Features

Performances of the LBP Based Algorithm over CNN Models for Detecting Crops and Weeds with Similar Morphologies

Suitable Image Features for Drought Stress Dection In Beans Using Raspberry Pi Imaging System

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