Al-BashishDBraikMBani-AhmadS (2011) Detection and classification of leaf diseases using K-means-based segmentation and neural networks base classification. Information Technology Journal10(2): 267–275. DOI: 10.3923/itj.2011.267.275.
2.
AlirezaPWonSLEtxeberriaED, et al. (2015) An evaluation of a vision-based sensor performance in Huanglongbing disease identification. Biosystems Engineering130(1): 13–22. DOI: 10.1016/j.biosystemseng.2014.11.013.
3.
ApoloAOEMartínezGJEgeaG, et al. (2020) Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV. European Journal of Agronomy115(1): 126030. DOI: 10.1016/j.eja.2020.126030.
4.
ArgüesoDPiconAIrustaU, et al. (2020) Few-shot learning approach for plant disease classification using images taken in the field. Computers and Electronics in Agriculture175(1); 105542. DOI: 10.1016/j.compag.2020.105542.
5.
AsadzadehSMAzadehAZiaeifarA (2011) A neuro-fuzzy-regression algorithm for improved prediction of manufacturing lead time with machine breakdowns. Concurrent Engineering19(4): 269–281. DOI: 10.1177/106329 3X11424512.
6.
BehmannJMahleinAKRumpfT, et al. (2015) A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precision Agriculture16(3): 239–260. DOI: 10.1007/s11119-014-9372-7.
7.
CamargoASmithJS (2009) Image pattern classification for the identification of disease-causing agents in plants. Computers and Electronics in Agriculture66(2): 121–125. DOI: 10.1016/j.compag.2009.01.003.
8.
CamponezMRibeiroPMarcondesD, et al. (2012) Infrared spectroscopy: A potential tool in huanglongbing and citrus variegated chlorosis diagnosis. Talanta91(1): 1–6. DOI: 10.1016/j.talanta.2012.01.008.
9.
Cevallos-CevallosJMGarcía-TorresREtxeberriaE, et al. (2010) GC-MS analysis of headspace and liquid extracts for metabolomic differentiation of citrus huanglongbing and zinc deficiency in leaves of ‘Valencia’ sweet orange from commercial groves. Phytochemical Analysis22(3): 236–246. DOI: 10.1002/pca.1271.
10.
DinakaranSJeba-ThangaiahPR (2013) Role of attribute selection in classification algorithms. International Journal of Engineering Research4(6): 67–71.
11.
GarciaESandovalMCarrilloJA, et al. (2015) Identification with probabilistic neural networks of deficiencies of iron and manganese by using digital images from bean leaves (Phaseolus vulgaris L.). Agrociencia49(4): 395–409.
12.
GlorotXBengioY (2010) Understanding the difficulty of training deep feed forward neural networks. In: Proceedings of the 13th international conference on artificial intelligence and statistics, Sardinia Italy, 13–15 May 2010, JMLR 9(1): 249–256.
13.
GoodfellowIBengioYCourvilleA (2016) Deep feedforward networks. In: GoodfellowI.BengioY.CourvilleA (eds) Deep Learning: Adaptive Computation and Machine Learning, 1st edn. USA: MIT-Press, 2016, pp.167–170.
14.
JanarthanSThuseethanSRajasegararS, et al. (2020) Deep metric learning based citrus disease classification with sparse data. IEEE Access8(1): 162588–162600. DOI: 10.1109/ACCESS.2020.3021487.
15.
KarthikeyanAKarthikeyanAVenkatesh RajaK (2020) Machine learning in optimization of multi-hole drilling using a hybrid combinatorial IGSA algorithm. Concurrent Engineering28(2): 130–141. DOI: 10.1177/1063293X209 08318.
16.
LatifGAlghazoJMaheswarR, et al. (2020) Deep learning-based intelligence cognitive vision drone for automatic plant diseases identification and spraying. Journal of Intelligent & Fuzzy Systems39(6): 8103–8114. DOI: 10.323 3/JIFS-189132.
MohantySPHughesDPSalathéM (2016) Using deep learning for image-based plant disease detection. Frontiers in Plant Science7(1): 1–10. DOI: 10.3389/fpls.2016.01419.
19.
NeelavathiCJagatheesanSM (2015) Performance evaluation for different classification techniques of spam mail using Weka. International Journal Scientific Research and Development3(6): 511–514.
20.
OmraniEKhoshnevisanBShamshirbandS, et al. (2014) Potential of radial basis function-based support vector regression for apple disease detection. Measurement55(1): 512–519. DOI: 10.1016/j.measurement.2014.05.033.
21.
OsakoYYamaneHLinSY, et al. (2020) Cultivar discrimination of litchi fruit images using deep learning. Scientia Horticulturae269(1): 1–7. DOI: 10.1016/j.scienta.2020.109360
22.
OtsuN (1979) A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics9(1): 62–66. DOI: 10.1109/TSMC.1979.4310076.
23.
PydipatiRBurksTFLeeWS (2006) Identification of citrus disease using color texture features and discriminant analysis. Computers and Electronics in Agriculture52(2): 49–59. DOI: 10.1016/j.compag.2006.01.004.
24.
RameshSVydekiD (2020) Recognition and classification of paddy leaf diseases using optimized deep neural network with Jaya algorithm. Information Processing in Agriculture7(2): 249–260. DOI: 10.1016/j.inpa.2019.09.002.
25.
SahooPKSoltaniSWongAKC (1988) A survey of thresholding techniques. Computer Vision, Graphics, and Image Processing41(2): 233–260. DOI: 10.1016/07 34-189X(88)90022-9.
26.
Salazar-RequeIFHuamánSGKemperG, et al. (2019) An algorithm for plant disease visual symptom detection in digital images based on superpixels. International Journal on Advanced Science, Engineering and Information Technology9(1): 194–203. DOI: 10.18517/ijaseit.9.1.5322.
27.
SankaranSMishraAEhsaniR, et al. (2010) A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture72(1): 1–13. DOI: 10.1016/j.compag.2010.02.007.
28.
SethyPKBarpandaNKRathAK, et al. (2020) Deep feature-based rice leaf disease identification using support vector machine. Computers and Electronics in Agriculture175(1): 105527. DOI: 10.1016/j.compag.2020.105527
29.
SethyPKNegiBBhoiN (2017) Detection of healthy and defected diseased leaf of rice crop using K-means clustering technique. International Journal of Computer Applications157(1): 24–27. DOI: 10.5120/ijca2017912601.
30.
ShiYHuangWLuoJ, et al. (2017) Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis. Computers and Electronics in Agriculture141(1): 171–180. DOI: 10.1016/j.compag.2017.07.019.
31.
SindhujaSRezaEEtxeberriaE (2010) Mid-infrared spectroscopy for detection of Huanglongbing (greening) in citrus leaves. Talanta83(3): 574–581. DOI: 10.1016/j. talanta.2010.10.008.
32.
SinghAGanapathysubramanianBSinghAK, et al. (2016) Machine learning for high-throughput stress phenotyping in plants. Trends in Plant Science21(2): 110–124. DOI: 10.1016/j.tplants.2015.10.015.
33.
SkurichinaMDuinR (2002) Bagging, boosting and the random subspace method for linear classifiers. Pattern Analysis & Applications5(2): 121–135. DOI: 10.1007/s100 440200011.
34.
StepanskiyLGKwonY (2010) Modeling of robot availability for the network-controllable production system. Concurrent Engineering18(4): 303–310. DOI: 10.1177/10 63293X10389800.
35.
SuD (1999) Design automation with the aids of multiple artificial intelligence techniques. Concurrent Engineering7(1): 23–30. DOI: 10.1177/1063293X9900700103.
36.
WittenHFrankEHallMA, et al. (2016) Algorithms: The basic methods. In: WittenHFrankEHallMA, et al. (eds) Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn.London: Morgan Kaufmann, pp.85–145. DOI: 10.1016/C2009-0-19715-5
37.
ZhangMMengQ (2011) Automatic citrus canker detection from leaf images captured in field. Pattern Recognition Letters32(15): 2036–2046. DOI: 10.1016/j.patrec. 2011.08.003.