@article {868,
	title = {Automatic non-destructive video estimation of maturation levels in Fuji apple (Malus Malus pumila) fruit in orchard based on colour (Vis) and spectral (NIR) data},
	journal = {Biosystems Engineering},
	volume = {195},
	year = {2020},
	pages = {136{\textendash}151},
	abstract = {Non-destructive estimates information on the desired properties of fruit without damaging them. The objective of this work is to present an algorithm for the automatic and non-destructive estimation of four maturity stages (unripe, half-ripe, ripe, or overripe) of Fuji apples (Malus Malus pumila) using both colour and spectral data from fruit. In order to extract spectral and colour data to train a proposed system, 170 samples of Fuji apples were collected. Colour and spectral features were extracted using a CR-400 Chroma Meter colorimeter and a custom set up. The second component  of  colour space and near infrared (NIR) spectrum data in wavelength ranges of 535{\textendash}560 nm, 835{\textendash}855 nm, and 950{\textendash}975 nm, were used to train the proposed algorithm. A hybrid artificial neural network-simulated annealing algorithm (ANN-SA) was used for classification purposes. A total of 1000 iterations were conducted to evaluate the reliability of the classification process. Results demonstrated that after training the correction classification rate (CCR, accuracy) was, at the best state, 100\% (test set) using both colour and spectral data. The CCR of the four different classifiers were 93.27\%, 99.62\%, 98.55\%, and 99.59\%, for colour features, spectral data wavelength ranges of 535{\textendash}560 nm, 835{\textendash}855 nm, and 950{\textendash}975 nm, respectively, over the test set. These results suggest that the proposed method is capable of the non-destructive estimation of different maturity stages of Fuji apple with a remarkable accuracy, in particular within the 535{\textendash}560 nm wavelength range.},
	doi = {https://doi.org/10.1016/j.biosystemseng.2020.04.015},
	url = {https://www.sciencedirect.com/science/article/pii/S1537511020301148},
	author = {Pourdarbani, Razieh and Sabzi, Sajad and Kalantari, Davood and Karimzadeh, Rouhollah and Ilbeygi, Elham and J I Arribas}
}
@article {867,
	title = {An automatic visible-range video weed detection, segmentation and classification prototype in potato field},
	journal = {Heliyon},
	volume = {6},
	year = {2020},
	pages = {e03685},
	abstract = {Weeds might be defined as destructive plants that grow and compete with agricultural crops in order to achieve water and nutrients. Uniform spray of herbicides is nowadays a common cause in crops poisoning, environment pollution and high cost of herbicide consumption. Site-specific spraying is a possible solution for the problems that occur with uniform spray in fields. For this reason, a machine vision prototype is proposed in this study based on video processing and meta-heuristic classifiers for online identification and classification of Marfona potato plant (Solanum tuberosum) and 4299 samples from five weed plant varieties: Malva neglecta (mallow), Portulaca oleracea (purslane), Chenopodium album L (lamb{\textquoteright}s quarters), Secale cereale L (rye) and Xanthium strumarium (coklebur). In order to properly train the machine vision system, various videos taken from two Marfona potato fields within a surface of six hectares are used. After extraction of texture features based on the gray level co-occurrence matrix (GLCM), color features, spectral descriptors of texture, moment invariants and shape features, six effective discriminant features were selected: the standard deviation of saturation (S) component in HSV color space, difference of first and seventh moment invariants, mean value of hue component (H) in HSI color space, area to length ratio, average blue-difference chrominance (Cb) component in YCbCr color space and standard deviation of in-phase (I) component in YIQ color space. Classification results show a high accuracy of 98\% correct classification rate (CCR) over the test set, being able to properly identify potato plant from previously mentioned five different weed varieties. Finally, the machine vision prototype was tested in field under real conditions and was able to properly detect, segment and classify weed from potato plant at a speed of up to 0.15 m/s.},
	doi = {https://doi.org/10.1016/j.heliyon.2020.e03685},
	url = {https://www.sciencedirect.com/science/article/pii/S2405844020305302},
	author = {Sabzi, Sajad and Yousef Abbaspour-Gilandeh and J I Arribas}
}
