Deep learning for smart agriculture: Concepts, tools, applications, and opportunities

Transcription

1 32 July, 2018 Int J Agric & Bil Eng Open Access at ttps:// Vl. 11 N.4 Deep learning fr smart agriculture: Cncepts, tls, applicatins, and pprtunities Nanyang Zu 1,2, Xu Liu 1,2, Ziqian Liu 1,2, Kai Hu 1,2, Yingkuan Wang 3, Jinglu Tan 4, Min Huang 1, Qibing Zu 1, Xunseng Ji 1, Yngnian Jiang 5, Ya Gu 1,2,4* (1. Key Labratry f Advanced Prcess Cntrl fr Ligt Industry, Ministry f Educatin, Jiangnan University, Wuxi , Cina; 2. Scl f Internet f Tings, Jiangnan University, Wuxi , Cina; 3. Cinese Academy f Agricultural Engineering, Beijing , Cina; 4. Department f Biengineering, University f Missuri, Clumbia, MO 65211, USA; 5. Jiangsu Zngnng IT Tecnlgy C., LTD, Yixing , Cina) Abstract: In recent years, Deep Learning (DL), suc as te algritms f Cnvlutinal Neural Netwrks (CNN), Recurrent Neural Netwrks (RNN) and Generative Adversarial Netwrks (GAN), as been widely studied and applied in varius fields including agriculture. Researcers in te fields f agriculture ften use sftware framewrks witut sufficiently examining te ideas and mecanisms f a tecnique. Tis article prvides a cncise summary f majr DL algritms, including cncepts, limitatins, implementatin, training prcesses, and example cdes, t elp researcers in agriculture t gain a listic picture f majr DL tecniques quickly. Researc n DL applicatins in agriculture is summarized and analyzed, and future pprtunities are discussed in tis paper, wic is expected t elp researcers in agriculture t better understand DL algritms and learn majr DL tecniques quickly, and furter t facilitate data analysis, enance related researc in agriculture, and tus prmte DL applicatins effectively. Keywrds: deep learning, smart agriculture, neural netwrk, cnvlutinal neural netwrks, recurrent neural netwrks, generative adversarial netwrks, artificial intelligence, image prcessing, pattern recgnitin DOI: /j.ijabe Citatin: Zu N Y, Liu X, Liu Z Q, Hu K, Wang Y K, Tan J L, et al. Deep learning fr smart agriculture: Cncepts, tls, applicatins, and pprtunities. Int J Agric & Bil Eng, 2018; 11(4): Intrductin A standard artificial neural netwrk (ANN) mdel cnsists f many neurns (cnnected prcessrs), eac prducing a sequence f real-valued activatins [1]. Wen sensrs perceive envirnment canges, input neurns will be activated and ter neurns will ten get activated trug weigted cnnectins frm previusly active neurns. Depending n te specific prblem and te neurn tplgy, tese beavirs may require lng cains f cmputatinal stages, were eac f te stage transfrms te aggregate activatin f te netwrk. DL is abut w t accurately assign credit acrss many suc stages [2]. Deep learning allws cmputatinal mdels tat are cmpsed f Received date: Accepted date: Bigrapies: Nanyang Zu, Master candidate, researc interests: IT, zunanyang08@163.cm; Xu Liu, Undergraduate, researc interests: IT, francislucien2017@126.cm; Ziqian Liu, Undergraduate, researc interests: IT, jlley1074@gmail.cm; Kai Hu, PD, researc interests: cartgrapy and gegrapic infrmatin engineering, ukai_wlw@ jiangnan.edu.cn; Yingkuan Wang, PD, Prfessr, researc interests: agricultural mecanizatin and infrmatin, wangyk@agri.gv.cn; Jinglu Tan, PD, Prfessr, researc interests: biengineering, tanj@missuri.edu; Min Huang, PD, Prfessr, researc interests: cntrl tery and engineering, uangmzqb@163.cm; Qibing Zu, PD, Prfessr, researc interests: mecanical engineering, zuqib@163.cm; Xunseng Ji, PD, Prfessr, researc interests: precisin instrumentatin and mecanical engineering, jixunseng@163.cm; Yngnian Jiang, CEO f Jiangsu Zngnng IT Tecnlgy C., LTD, jxrjyn@126.cm; *Crrespnding Autr: Ya Gu, PD, Prfessr, researc interest: biengineering. Rm C510, Scl f IT Engineering, Jiangnan University, Wuxi , Cina, Tel: , guy@jiangnan.edu.cn. multiple prcessing layers t represent data wit multiple levels f abstractin. Great imprvements f te metd can be fund in many researc dmains. Te cncept f BP (Back Prpagatin) Neural Netwrk is te basis fr many DL algritms. Wit massive entusiasm puring int te DL field, great imprvements ave been acieved in recent years. DL as drawn a lt f attentin in agriculture. One f its applicatins in agriculture is image recgnitin, wic as cnquered a lt f bstacles tat limit fast develpment in rbtic and mecanized agr-industry and agriculture [3]. Tese imprvements can be seen in many aspects f agriculture, suc as plant disease detectin, weed cntrl, and plant cunting. Researcers in agriculture may nt be experienced prgrammers. Tey ften directly use publicly available sftware framewrks fr deep learning witut carefully examining te learning mecanisms used. An understanding f DL algritms can facilitate data analysis and tus enance researc in agriculture. Altug varius cmmercial sftware framewrks are available, tere is a lack f a systematic summary f majr DL algritms [3], including cncepts, applicatin limitatins, flw carts, and example cdes, wic can elp researcers in agriculture t learn majr DL tecniques quickly and use tem effectively. In rder t prvide a listic picture f DL t researcers in agriculture fields and enance mdern smart agriculture develpment, tis wrk summarizes BP and cmmn DL algritms (Cnvlutinal Neural Netwrks (CNN), Recurrent Neural Netwrks (RNN), and Generative Adversarial Netwrks (GAN)) and teir applicatins in agriculture, wit a fcus n

2 July, 2018 Zu N Y, et al. Deep learning fr smart agriculture: Cncepts, tls, applicatins, and pprtunities Vl. 11 N.4 33 applicatins publised in te last tree years. Example cdes fr BP, CNN, RNN, and GAN in Pytn are als prvided. 2 Cmmn deep learning algritms CNN, RNN, and GAN are te mst cmmnly used DL algritms. Tere are many ter sub-categry DL algritms, suc as VGGNet [4], CnvNets [5], LSTM [6,7] and DCGAN [8,9]. It is unfeasible t include all f tem in ne summary. Tey can be derived frm te tree cmmn DL algritms directly r indirectly [10]. Understanding te tree cmmn DL algritms is very elpful fr learning te sub-categry DL algritms. Terefre, te sub-categry DL algritms are nt reviewed wit details. Te cncept f backprpagatin (BP) is te basis fr many ANN altug itself des nt mean a deep netwrk. It tus will be intrduced in tis sectin first. 2.1 Feedfrward neural netwrk and backprpagatin (BP) An example structure f feedfrward neural netwrk based n backprpagatin r BP neural netwrk is swn in Figure 1. It is a supervised learning algritm using errr back prpagatin, cmpsed f multiple layers f idden neurns in full cnnectin. Tis means tat a layer f neurns is cnnected t an upper layer f neurns and eac layer as an activatin functin t limit te utput f amplitude f neurns by linear r nnlinear transfrmatin f te input f te afferent neurn. Hidden neurns can learn te salient features f training data frm cntinuus frward prpagatin [11]. Output layer utput: = f ( net ) = f ( w a + b ) k k ki i k i= 1 (4) Cst functin: Fr neural netwrks, te cst functin plays an imprtant rle in ptimizing te mdel parameters (weigts and treslds). A cst functin is a measure f te errr between te predicted utput and te actual utput fr te training samples. Te training prcess gradually reduces te value f te cst functin by te gradient descent. Tere are tw types f cst functin expressins. One is a quadratic lss functin [11], and te ter is a crss-entrpy lss functin [12]. Here, te quadratic lss functin E is used as an example t demnstrate BP and it is expressed as Equatin (5): 1 1 E = d = d f net s s 2 2 ( k k) ( k ( k)) 2 k= 1 2 k= 1 s r dk f wkif vijpj bj bk k= 1 k= 1 j= 1 1 = [ ( ( + ) + )] 2 Backprpagatin is based n te gradient descend f te cst functin and te cain rule f differentiatin [13], wse cnceptual structure is swn in Figure 2. First, tere is a need t differentiate te weigts f te cst functin t get an errr value, ten backprpagate te errr value. During tis prcess, tere is a need t cnstantly adjust and update te weigts and treslds. 2 (5) Figure 1 Example structure f a BP neural netwrk Assuming tat tere is a set f training data {(p 1, d 1 ), (p 2, d 2 ),, (p r, d s )}. [v ij ] r is te weigts between neurn cde and neurn cde r. b dentes te tresld in neurn cde, (d 1, d 2,, d s ) dentes cde utput. net i is te input f te first idden layer. a i is te utput f te first idden layer, and f is te activatin functin f te first idden layer. net k is te input f te utput layer. f is te activatin functin f te utput layer. k is te utput f te utput layer. Te utput f eac neurn cde is determined by te utput f previus neurn. Te majr frmulatins fr te algritm can be listed as Equatins (1)-(4). Input layer t idden layer input: r i = ij j + j j = 1 net v p b (1) Hidden layer utput: r ai = f( neti ) = f( vijpj + bj) (2) j = 1 Output layer input: net w a b (3) k = ki i + k i= 1 Figure 2 Cncept f backprpagatin Assuming tat: Δw ki - weigt adjustment (lcal gradient) frm te utput layer t te idden layer, Δb k - tresld adjustment (lcal gradient) frm te utput layer t te idden layer, Δv ij - weigt adjustment (lcal gradient) frm te idden layer t te input layer,and Δb j - tresld adjustment (lcal gradient) frm te idden layer t te input layer. Te majr frmulatins f backprpagatin algritm can be listed as Equatins (6)-(9). Weigt and tresld adjustment (lcal gradient) frm te idden layer t te utput layer: E Δ wki = η = η ( dk k ) f ( netk ) ai (6) wki E Δ bk = η = η ( dk k ) f ( netk ) (7) bk Weigt and tresld adjustment (lcal gradient) frm te input layer t te idden layer:

3 34 July, 2018 Int J Agric & Bil Eng Open Access at ttps:// Vl. 11 N.4 E Δ v = η = η {[ ( d ) f ( net ) w ] ij s k k k vij k = 1 ki f ( neti )} pj E Δ b η η [ ( d ) f ( net ) w ] f ( net ) s j = = k k k ki i b j k = 1 (9) In te prcess f backprpagatin, te weigts and treslds are adjusted by reversing te value f errr [14]. A flwcart f te BP algritm is swn in Figure 3. (8) wit eac cnvlutinal layer is w 3. Te tresld f cnvlutinal layer is b. Te size f new image feature X after cnvlutin calculatin is H_new W_new, wse cnvlutinal layer cmputatin prcess is swn in Figure 4. Fr eac cnvlutinal layer, te number f cnvlutin kernels determines te number f utput feature maps r te number f inputs in te pling layer. T identify te edge infrmatin f an image, te metd f zer-padding in wic zer is added t te brder f te input vectr is applied and te size f zer-padding is P. It is als vital fr cnvlutin calculatin t set a suitable stride wit size S [20] during te prcess f calculating cnvlutin in te cnvlutinal layer. Te majr frmulatins are listed as Equatins (10)-(12). 3 X _ new= X W = ( Xk Wk) + b (10) k = 1 H + 2 P H _ new= + 1 (11) S W w+ 2 P W _ new= + 1 (12) S Figure 3 Flwcart f te BP prgramming BP neural netwrks ave many merits, including strng nnlinear mapping capabilities and ig degree f self-learning and self-adaptability. Tey are widely applied in many fields suc as speec analysis, image recgnitin, digital watermarking, and cmputer visin. Tey slve many prblems tat cannt be dne by traditinal macine learning algritms. Its classificatin functin is particularly suitable fr applicatins in pattern recgnitin [15] and classificatin [16]. Neverteless, te netwrks used fr bject recgnitin r natural language prcessing cntain tusands f idden units, wic bring expnentially increasing number f parameters. BP neural netwrks may fall int lcal minima in practical applicatins. Tus, w t select typical samples frm training data is an unslved prblem wen massive amunts f cmplex data are available. Te traditinal BP neural netwrks are n lnger sufficiently effective in sme areas. Hence, deep netwrks suc as CNN, RNN, and GAN ave caugt increasing attentin. 2.2 Cnvlutinal neural netwrks (CNN) CNN is a deep learning algritm cmpsed f multiple cnvlutinal layers, pling layers, and fully cnnected layers, wic as resulted in many breaktrugs in speec recgnitin, face recgnitin, natural language prcessing and s n [17]. Te structure cmpsed f te cnvlutinal layers and te pling layers is fr feature extractin and te fully cnnected layers functin as a classifier. BP neural netwrks mainly map features trug te netwrk t specific values, wereas cnvlutinal netwrks first cnvert signals int features and ten map te features t a specific target value [18]. Cnvlutinal Layer: Assuming tat an RGB image is taken as input X and tere are six cnvlutin kernels W [19]. Te image size is an H W 3 tree-dimensinal matrix. Te cnvlutin kernel size assciated Figure 4 Cnvlutinal layer cmputatin prcess Pling Layer: Te functin f te pling layer is t cmpress te input tensr and reduce te size f te input data, wic means canging eac n n submatrix f te input image int ne specific element value. Tere are tw cmmn pling metds: maximum pling and mean pling. Maximum pling takes te maximum f te crrespnding n n area as te pled element value and mean pling takes te average value f te crrespnding n n area as te pled element value. Te input vlume fr te pling layer is W H D, te utput vlume fr te pling layer is W_pling H_pling D_pling [20]. Te pling layer cmputatin prcess is swn in Figure 5. Besides, te pling layer requires tw yper parameters: te spatial extent f and te stride size s. Te majr frmulatins are listed as Equatins (13)-(15): W_ pling = ( W f)/ s+ 1 (13) H _ pling = ( H f)/ s+ 1 (14) D_ pling = D (15) A flwcart f te CNN algritm is swn in Figure 6. CNN simulates te beavir f uman brain prcessing f signals and cmbines feature extractin in image prcessing wit BP neural netwrks. CNN as fewer parameters tan deep netwrks because f its lcal perceptin mecanism and parameter saring mecanism wic can reduce parameters. CNN can andle ig-dimensinal arrays, especially fr image classificatin. Many data mdalities are in te frm f multiple arrays. CNN as

4 July, 2018 Zu N Y, et al. Deep learning fr smart agriculture: Cncepts, tls, applicatins, and pprtunities Vl. 11 N.4 35 unique advantages in natural language prcessing [21], face recgnitin [22], and image prcessing because f its special structures sared by lcal weigts. Its layut is clser t te actual bilgical neural netwrk. Neverteless, CNN needs a lt f training samples. = f u x + w + b (16) t t t 1 ( 1) Te predicted utput f recurrent neural netwrk at time t: t = f(v t + b 2 ) (17) Recurrent neural netwrk errr at time t: l = t y t (18) A flwcart fr RNN prgramming is swn in Figure 8. Figure 5 Pling layer cmputatin prcess Figure 7 Recurrent neural netwrk prpagatin prcess Figure 6 Flwcart f CNN prgramming 2.3 Recurrent neural netwrks (RNN) RNN can mine tempral infrmatin and semantic infrmatin and as acieved breaktrugs in time series analysis, speec recgnitin and language mdeling. RNN is a variant f ANN, wic means tat te current input f te netwrk is related t te utput f te previus mment. Te specific manifestatin is tat te netwrk will remember te previus infrmatin and apply it t te current netwrk utput calculatin; in ter wrds, RNN can be treated as BP neural netwrk f wic te utput will be used as te input f te next netwrk [19]. As Figure 7 sws, x t represents te input f training data at te time t, t represents te idden state at te time t, wic is determined by te current input x t and te previus idden layer state t-1. t dentes te utput f idden layer at te time t. l is te errr at te current time t, wic is determined by te t and te true utput f te training data y t. u, w, v are te weigts f recurrent neural netwrk and are sared acrss te recurrent neural netwrks. b 1 and b 2 are te treslds f recurrent neural netwrk and are sared acrss te recurrent neural netwrks [23], f is te activatin functin f te idden layer, wse netwrk structure prpagatin prcess is swn in Figure 7. Te specific frmulatins are listed as Equatins (16)-(18). Hidden state value at time t: Figure 8 Flwcart f RNN prgramming Altug RNN slves te prblems f time series teretically, it is difficult t slve te prblems f lng time series due t te lengt f infrmatin varies in practical applicatins, wic can cause gradients t disappear r t explde. Lng Srt-Term Memry (LSTM) netwrk [24] is an imprvement f te recurrent neural netwrk, wic is mainly designed t slve time series prblems wit lng intervals and lng delays. LSTM depends n te structure f sme drs t selectively affect te state f te mment in te recurrent neural netwrk. LSTM netwrk as been used in speec recgnitin [25], macine translatin [26] and ter fields. 2.4 Generative adversarial netwrks (GAN) Te ingenuity f GAN lies in its design. Tecnically, it is a cmbinatin f existing algritms f BP. GAN is a way tat a set f nise is used t learn te distributin f te real data and t generate new data. Te structure f tis netwrk cnsists f tw mdels, a generatin mdel wic is t capture te distributin f

5 36 July, 2018 Int J Agric & Bil Eng Open Access at ttps:// Vl. 11 N.4 te real data, and a discriminatin mdel wic is similar t a binary classifier [27]. Assuming tat te generatin mdel is a deep neural netwrk wic generates a new vectr called fake data G(z) and te discriminatin mdel is a fully cnnected netwrk wic btains a prbability value D(z) reflecting real data set. x is a real data set [28], y is te utput f te distributin mdel, E d is te lss functin f discriminatin mdel, E g is te lss functin f generatin mdel, E is te lss functin f te entire netwrk, wse netwrk structure is swn in Figure 9 and te majr frmulatins fr te lss functin are listed as Equatins (19)-(24). Discriminatin mdel lss functin E d : E = ((1 y)lg(1 D( G( z))) + ylg D( x)) (19) d Generatin mdel lss functin E g : E = (1 y)lg(1 D( G( z)))(2 D( G( z) 1)) (20) g Ttal netwrk lss functin E: E = (1 y)lg(1 D( G( z)))(2 D( G( z)) 1) (21) After te lss functins are btained, te netwrk is ptimized by us V(D,G),te ptimizatin functin: min max V( D, G) = Ex~ p ( )[lg ( )] data x D x + G D (22) Ez~ p ( )[lg(1 ( ( )))] z z D G z Te expressins abve can be divided int tw functins: discriminatin mdel ptimizatin functin and generatin mdel ptimizatin functin. Discriminatin mdel ptimizatin functin: max V( D, G) = E [lg D( x)] + E [lg(1 D( G( z)))] D x~ pdata ( x) z~ pz ( z) Generatin mdel ptimizatin functin: (23) min (, ) = [lg(1 ( ( )))] (24) V D G E D G z z~ pz ( z) G Accrding t te abve, it can be bserved tat te ptimizatin f te discriminatin mdel and te ptimizatin f te generatin mdel are independent f eac ter. Figure 9 Generative adversarial netwrks structure Te pwer f generative adversarial netwrks (GAN) lies in te ability t autmatically learn te distributin f real sample data. Deep cnvlutinal generative adversarial netwrks (DCGAN) resulting frm te rise f cnvlutinal neural netwrk is widely used in image prcessing suc as image restratin frm split image, dynamic scene generatin [29], image generatin [30], and reslutin enancement [31]. In additin, DCGAN plays an imprtant rle in face detectin and recgnitin [32]. Hwever, it is difficult t train a GAN due t entail syncrnizatin f generatin mdel and discriminatin mdel, and tere is rm fr furter develpment f GAN applicatins. A flwcart fr GAN prgramming is swn in Figure 10. A summary f BP, CNN, RNN, and GAN is swn in Table 1. Cdes f BP, CNN, RNN, and GAN in Pytn are prvided in te supplemental files. Figure 10 Flwcart f GAN prgramming

6 July, 2018 Zu N Y, et al. Deep learning fr smart agriculture: Cncepts, tls, applicatins, and pprtunities Vl. 11 N.4 37 Type Table 1 Example references Summary f BP, CNN, RNN, and GAN Variants [33] RBF BP Rumelart GRNN CNN RNN LeCun [34] Krizevsky [35] LeNet, AlexNet VggNet Netwrk structure Input layer Output layer Hidden layer Input layer Cnvlutin layer Pling layer Full cnnected layer Miklv [36] Input layer Sundermeyer [37] LSTM Hidden layer Output layer GAN Gdfellw [28] DCGAN 3 Deep learning framewrks Discriminatin mdel Generatin mdel Applicatins Data fitting Pattern recgnitin Classificatin Image prcessing Speec signal Natural Language Prcessing Time series analysis Emtin analysis Natural Language Prcessing Image generatin Vide generatin TensrFlw and Caffe are tw cmmnly-used DL framewrks, wic allw users t use DL witut significant prgramming. Te mst ppular TensrFlw and Caffe framewrks are intrduced belw briefly. 3.1 TensrFlw TensrFlw is an pen surce cmputing framewrk f Ggle tat supprts deep learning algritms, including CNN, RNN, GAN and ter variants, wic can be used n Linux, Windws, and Mac platfrms. TensrFlw as sme advantages including ig flexibility, true prtability, multi-language supprt, ric algritm library, and excellent dcumentatin. TensrFlw prvides a very ric set f deep learning applicatin prgramming interfaces (API) including basic vectr matrix calculatins, ptimizatin algritms, cnvlutinal neural netwrks, recurrent neural netwrks, and visual aids. TensrFlw uses dataflw graps t represent cmputatin, sared state, and te peratins tat mutate state. It maps te ndes f a dataflw grap acrss multiple cmputatinal devices, suc as multi-cre CPUS, general-purpse GPUS, and custm-designed ASICs, wic are knwn as Tensr Prcessing Units (TPUS) [38]. A deep learning mdel, typically a multi-layer neural netwrk, is cmpsed f several cmputatinal layers tat prcess data in a ierarcical fasin. Eac layer takes an input and prduces an utput, ften cmputed as a nn-linear functin r a weigted linear functin f a weigted linear cmbinatin f te input values. It is particularly ppular tat cnvlutinal layers apply a lcal functin r filter t all subsets f te layer s input, suc as prtins f an image [39]. 3.2 Caffe Cnvlutin Arcitecture Fr Feature Extractin (Caffe) is te first deep learning framewrk tat as been widely used in industry as an pen surce. Te Caffe mdel and te crrespnding ptimizing metds are given as texts instead f cdes. Caffe gives te definitin f te mdel, ptimal settings, and pre-training weigts. Caffe andles massive data wit ig speed. Besides, Caffe can be mdular wic easily extended t new tasks. Users can define teir wn mdels using te types f neurn layers prvided by Caffe. Caffe prvides multimedia scientists and practitiners wit a clean and mdifiable framewrk fr state-f-te-art deep learning algritms and a cllectin f reference mdels. Te framewrk is a BSD-licensed C++ library wit Pytn and MATLAB bindings fr training and deplying general-purpse cnvlutinal neural netwrks and ter deep learning mdels efficiently n varied arcitectures [40]. A cmparisn between TensrFlw and Caffe can be fund in Table 2. Table 2 Cmparisn between Tensrflw and Caffe Traits TensrFlw Caffe Supprt language C++, Pytn C++, Pytn, MATLAB Supprt system Linux, Mac OS X, Andrid, IOS Linux, Mac OS X, Windws Data input frmat Data, ImageData Feeding, Data frm a file, Preladed data Supprt mdel CNN, RNN, GAN CNN Lss functin Crss-entrpy lss functin Mean squared errr Cntrative lss, Hingle lss, Sftmax lss 4 Recent applicatins f DL in smart agriculture Recent applicatins f CNN, RNN, and GAN in smart agriculture are summarized in tis sectin. 4.1 CNN applicatins in smart agriculture CNN as strng capability in image prcessing, wic makes it widely used in agriculture researc. Generally speaking, mst applicatins f DL in agriculture can be categrized as plant r crp classificatin, wic is vital fr pest cntrl, rbtic arvesting, yield predictin, disaster mnitring etc. Plant disease detectin is time-cnsuming wen it is dne manually. Frtunately, wit te develpment f artificial intelligence, plant disease detectin can be accmplised trug image prcessing. Plant disease recgnitin mdels are mstly based n leaf image classificatin and pattern recgnitin [41]. A nvel DL framewrk develped by te Berkley Visin and Learning Centre was used t build a plant disease detectin mdel. Te mdel is able t recgnize 13 different types f plant diseases ut f ealty leaves, wit te ability t distinguis plant leaves frm teir surrundings [42]. In anter researc f using DL in detectin f plant diseases, te verall accuracy may reac 95.8% after 100 training iteratins and may be imprved t 96.3% after furter fine-tuning. Te results are actually better tan manual detectin [43]. All tese prved tat DL as very impressive perfrmance in detecting plant diseases. As many cuntries acrss te wrld ave been develping initiatives t build natinal agriculture mnitring netwrk systems, plant classificatin and weed identificatin are particularly imprtant because f te implicatins fr autmating agriculture. Since image recgnitin can be applied t detect plenty f features f plants, CNN as been extensively used t detect weeds r classify plants [44-48]. In 2017, a new apprac tat cmbined CNN and K-means feature learning was prpsed fr weed identificatin and cntrl. Manual design features in weed identificatin may cause unstable identificatin results and weak generalizatin ability in feature extractin. Tus, te applicatin f DL and K-means pre-training resulted in an accuracy f identificatin f 92.89% [44]. One f te pre-trained CNN arcitecture tat is widely used fr plant classificatin is AlexNet. Experimental results based n AlexNet frm te Istanbul Tecnical University in 2017 suggest tat te CNN arcitecture utperfrms macine learning algritms tat are based n and-crafted features fr te discriminatin f penlgical stages [47]. In anter study, self-rganizing Knen maps (SOMs) were used fr ptical images segmentatin and subsequent restratin f missing data in a time-series f satellite imagery. Supervised classificatin wit CNN was perfrmed. Tis metd added a pst-prcessing step tat included several filtering algritms based n te available

7 38 July, 2018 Int J Agric & Bil Eng Open Access at ttps:// Vl. 11 N.4 infrmatin and gespatial analysis. An accuracy f 85% was acieved fr classificatin f majr crps (weat, maize, sunflwer, sybean, and sugar beet) [42]. Hwever, tere are callenges tat ave slwed dwn te applicatin f CNN fr plant classificatin. Fr example, eac pixel f te space brne SAR (syntetic aperture radar) imagery is caracterized by backscatter pase and intensity in multiple plarizatins. Bt data surces ave multitempral nature and different spatial reslutins [47]. Infrmatin fusin is tus imprtant in te future t make DL mre applicable in tis area. Fruit cunting is imprtant fr yield predictin and rbtic arvesting. Te traditinal manual cunting r mbile camera cunting cannt prvide satisfactry results and are time-cnsuming. Because f canges in cclusin and illuminatin, pre-prcessing suc images is callenging. Regular DL metds ave difficulty in slving tese prblems. A blb detectin metd as prven t be useful [48], wic was prpsed t accmpany a fully cnvlutinal netwrk (FCN). Te first step f te metd is t cllect uman-generated labels frm a set f fruit images. Ten, a blb detectin FCN was trained t perfrm image segmentatin. After tat, a cunt cnvlutinal netwrk was trained t take te segmented image and utput an intermediate estimate f te fruit cunt. Te final step f te wrk was t train a linear regressin equatin t map intermediate fruit cunt estimates t final cunts using uman-generated labels as te grund trut. Tis apprac using DL wit blb detectin imprved nt nly te accuracy but als te efficiency f cunting. Land classificatin usually invlves classificatin f large areas f land and is imprtant t suc purpses as land use and land cver (LULC), disaster risk assessment, agriculture, and fd security [49]. Fr classificatin and area estimatin in te remte sensing and ter agriculture imagery, DL tecniques as been applied. Te general idea f tis DL apprac is t fuse r integrate data acquired by multiple etergeneus surces by using macine learning tecniques and emerging big data and ge-infrmatin tecnlgies t prvide data prcessing and visualizatin capabilities. Te metdlgy can be divided int fur steps: nise filtratin and data clustering, classifying land cver, map pst-prcessing wit filtering, and gespatial analysis. In additin t satellites, unmanned aerial veicles (UAV) are nw widely used t investigate varius resurces [50] based n deep CNN (DCNN) and transfer learning (DTCLE). A feature extractin metd based n DCNN was used t extract cultivated land infrmatin by intrducing a transfer learning mecanism. Finally, cultivated land infrmatin extractin results were cmpleted by te DTCLE and e-cgnitin fr cultivated land infrmatin extractin (ECLE). Te verall precisin f DCTLE and ECLE were bt arund 90%, but in terms f integrity and cntinuity, DTCLE utperfrmed ECLE. Tis instance is an extensin f DL tat can be applied in agriculture. Te utilizatin f UAVs permits acquisitin f ig-quality images. Item detectin is a fast-grwing dmain in DL. In agriculture fields, bstacle detectin is als imprtant fr farmers, especially wen igly autnmus macines ave been increasingly used. In rder t perate tese macines safely witut supervisin, tey must perfrm autmatic real-time risk detectin wit ig reliability [51]. An image classificatin metd wit te AlexNet and DCNN were utilized t enance perfrmances. Te accuracy reaced 99.9% in rw crps and 90.8% in grass mwing, wic is muc better tan traditinal etds [52]. Infrmatin frm satellite is very precius and imprtant fr making sustainable land use planning fr minimizing CO 2 emissin, maximizing ecnmic returns, and minimizing land degradatin. Te callenge wit using infrmatin is t interpret te images cllected. Translating satellite images by using cnvlutinal neural netwrks (CNN) and genetic algritms as becme a useful strategy fr decisin making, especially fr precisin agriculture and agrindustry. Te data can be used t classify plant types in a land area using CNN. Land types and ter data can be added t te grid frm. A grid frm mdel was evaluated t assess bjectives and a genetic algritm was used prduce an ptimal slutin [53]. Flwer grading was als dne by using a similar cncept [54]. CNN can als be used in weater frecasting [55], wic is key t agriculture. Crp yield predictin befre arvest is crucial t farmers, cnsumers, and te gvernment in teir effrts t design strategies fr selling, purcasing, market interventin, and fd srtage relief. CNN as als been used t predict yield in agriculture [56]. It can be used fr studying nt nly crps but als animals. Fr example, CNN as been extensively used t classify animal beavirs [57,58]. 4.2 RNN applicatins in smart agriculture RNN is very useful t prcess time series data and as been used in many agricultural areas, suc as land cver classificatin, pentype recgnitin, crp yield estimatin, leaf area index estimatin, weater predictin, sil misture estimatin, animal researc, and event date estimatin. Land cver classificatin (LCC) is cnsidered as a vital and callenging task in agriculture, and te key pint is t recgnize wat class a typical piece f land is in. In te past, a lt f applicatins are based n mn-tempral bservatins and ignring time-series effects in sme prblems. Fr instance, vegetatin canges its spatial appearance peridically, wic can cnfuse te mn-tempral appraces. Meanwile, mn-tempral appraces migt be influenced by sme biases, like weater. Terefre, deep sequence mdels ave been applied, and a widely-used variant RNN mdel is te LSTM. In an experiment led by Rußwurm et al. [59], te LSTM netwrk utperfrmed all mn-tempral mdels (CNN and SVM) as well as te standard RNN. Ienc et al. [60] cmbined te LSTM units wit ter macine learning mdels (SVM, RF) and cmpared tem wit mn-tempral appraces. Tey cncluded tat SVM wit LSTM units wrked te best. In additin, RNN mdels are used nt nly in recgnitin f land cver classes but als detectin f te canges f land. Lyu et al. [61] establised a netwrk called REFEREE (learning a transferable cange rule frm RNN fr cange detectin), wic cnsisted f tw grups f picture input (same regin, different timestamp), several LSTM units, and a grapical utput swing regins experiencing canges. By applying RNN mdels, REFEREE culd learn a stable and ratinal cange rule, in bt binary cange cases and multi-class cange cases. Plant pentyping as becme a t tpic, yet a callenging ne, because f te increasing need f precisin agriculture. Briefly, plant pentyping means t recgnize te kind f a plant trug its appearance r traits. Mst macine learning appraces ave relied n individual static bservatins, wic cause incrrect recgnitin f similar plants in typical perids. A new deep learning structure fr plant pentype recgnitin was created by Namin et al. [62], in wic CNN was cmbined wit LSTM units. Accrding t teir structure, CNN was t extract

8 July, 2018 Zu N Y, et al. Deep learning fr smart agriculture: Cncepts, tls, applicatins, and pprtunities Vl. 11 N.4 39 features and its utput was fed int an LSTM unit in rder t build a sequence mdel. Experiment results indicated tat te sequence mdel imprved accuracy significantly, cmpared wit a previus pure CNN mdel, frm 76.8% t 93%. Beynd CNN, RNN as als been used fr crp yield estimatin, wic uses time series data t reduce biasing. Min et al. [63] trained tw RNN-based classifiers, an LSTM netwrk and a Gated Recurrent Unit (GRU) netwrk, and applied tem in te task f mapping winter vegetatin quality cverage, alng wit mn-tempral mdels. Teir results indicated tat te GRU mdel utperfrmed all ter mdels, wit an accuracy f 99.05% fr a 5-fld crss validatin dataset. Te leaf area index (LAI) is a key attribute f many agricultural mdels. Accurate LAI and its dynamics are widely used fr estimatins f envirnment, vegetatin status and carbn cycle etc. Traditinal LAI estimatin metds fall int tw categries, empirical metds and pysical metds. Te LAI results may suffer frm spatial r tempral discntinuities, wic limit teir applicatins in climate simulatin and weaken teir rbustness. T slve te prblem, an RNN-based mdel named NARX (Nnlinear Autregressive mdel prcess wit exgenus input) was applied. Te inputs f te NARX mdel were previus predictin values and te current and previus values f an exgenus input signal. Te inputs were fed t an RNN and te predictin Y were a part f inputs in te next time step. Tis mdel tk nt nly independent inputs int cnsideratin but als te utput f te mdel in te past, making it mre pwerful and. Cai et al. [64] establised a mdel based n te NARX mdel called NARXNN t estimate time-series LAI. Tey trained te mdel n several datasets and made indirect and direct validatin, bt f wic suggested tat NARXNN is a prmising tl fr time-series LAI estimatin. In applicatin, Cen et al. [65] applied a NARX mdel t predict te LAI f rubber. RNN is useful fr time series and tus as been used in weater predictin. In Biswas et al. [66], tree mdels were cmpared fr weater predictin: an RNN-based mdel named NARXnet, a case-based reasning mdel (CBR), and a segmented CBR mdel. Te input f NARXnet was te weater attributes f a days befre te target day and b predictins and targets. Tis structure means tat NARXnet culd nt nly learn frm istrical data but als frm te previus predictins. Te NARXnet gt an accuracy f 93.95%, utperfrming te ter tw mdels significantly. In additin, Zaytar et al. [67] establised a new LSTM mdel t predict 24 and 72 urs weater attributes f a city: temperature, umidity, and wind speed. Cmpared wit nrmal RNNs, teir structure cnsisted f an input layer, tw stacked LSTM layers cnnected wit a dense layer, an activatin layer and a repeat layer. Tey used urly attributes data f 15 years t train te mdel and gt cmpetitive results cmpared wit ter traditinal metds. Tese suggest tat deep RNN-based metds are a cmpetitive alternative fr weater frecasting. Sil misture (SM) is a vital ydrlgical attribute fr precisin agriculture, meterlgy, and climate cange. Hwever, SM in farmlands is a functin f many factrs and can vary extremely wit time and space, causing difficulty in precise estimatin. Neural netwrks are applied t tis task naturally because tey can estimate cmplex functins and time-series input. Lu et al. [68] tested a simplified NARX mdel wse input was nly te current features and te predictin it ad given in te last time step. Tey cmpared te predictins wit te sil misture data given by Japan Aerspace Explratin Agency (JAXA), te Land Surface Parameter Mdel (LPRM), and te Glbal Land Data Assimilatin System (GLDAS). Te direct validatin indicated tat teir mdel remained stable and cmpetitive in bt frzen and unfrzen seasns. In additin, Tzeng et al. [69] used a typical NARX mdel t estimate te dynamics f sil misture. Tey used a NARX mdel (DLNN as tey called) t predict sil misture n an urly basis and cmpared te predictins wit grund measurements. Te experiments swed tat te mdel was a prmising tl fr te task. RNN as als been used in studying animals in bt te macrscpical and te micrcsmic scales. Wit te develpment f deep learning, RNN-based mdels ave prven t be very cmpetitive. In Sut Africa, te mvement f elepant erds urt te endangered species f vegetatin. Palangpur et al. [70] trained an RNN mdel cmbined wit te particle swarm ptimizatin (PSO) algritm t predict te lcatins f elepant erds. Te results indicated tat te RNN mdel culd prvide predictins wit a lw level f errrs. In anter researc, Demmers et al. [71] applied a first-rder RNN fr estimatin f pig grwt. Te result swed tat te first-rder RNN wrked well t predict pig grwt. RNN can als be used fr event date estimatin [72] and many ter purpses. As we ave seen, sequence mdels ave been increasingly applied in agriculture, even tug te main deep learning metds used in agricultural tasks are still te CNN-based mdels. If we lk trug te applicatins f sequence mdels, it is nt ard t cnclude tat were sequence mdels can play an imprtant rle are usually tasks tat invlve a lng time perid r require stability in te lng term. Te main mecanism f imprvements resulting frm sequence mdels is t vercme bias tat ccurs at typical time pints r lcatins. 4.3 GAN applicatins in smart agriculture GAN is a new kind f neural netwrk but as been cnsidered a very useful metd in many fields, especially in image prcessing. GAN as ften been used t enric datasets. It as nt been applied t agriculture widely. Ledig et al. [73] used GAN t slve feature lss caused by dwn sampling. If a picture is cmpressed, sme features can be lst r becme inaccurate and tere is a need t recver pt-realistic textures frm it. T d tat, tey intrduced a perceptual lss functin made up f an adversarial lss and a cntent lss. Cmpared wit widely-used pixel-wise MSE lss, te cntent lss functin tey used was mtivated by perceptual similarity. After being trained wit 350 K images, teir mdel culd recver igly cmpressed images and utperfrmed sme state-f-te-art mdels at tat time. Tis wrk is s fundamental tat it can be used in almst every prject cntaining image prcessing, particularly in agriculture fields were many applicatins are based n remte sensing images. In anter study, Bart et al. [74] attempted t vercme te gap between large quantity f data deep learning mdels require and te srtage f manually anntated datasets. Tey used a GAN-based mdel, called unsupervised cycle generative adversarial netwrk, t ptimize te realism f syntetic agricultural images. In teir wrk, syntetic, 50 empirically anntated, and 225 unlabeled empirical images were used t train teir mdel and tey yptesized tat te similarity between syntetic images and empirical images can be imprved qualitatively t imprve te translatin f features. Te results swed tat te syntetic images were translated well n lcal features suc as clr, illuminatin scattering and texture wile glbal feature translatin

9 40 July, 2018 Int J Agric & Bil Eng Open Access at ttps:// Vl. 11 N.4 was nt s gd. 4.4 A Meta-Analysis f DL applicatins in smart agriculture Many pure DL researc articles are publised in te ACM prceedings. In cntrast, applicatins f DL in agriculture are mre ften publised in researc jurnals. Amng te publised papers, te well-knwn databases f Science Citatin Index (SCI) and Scial Science Citatin Index (SSCI) cllect te mst representative and qualified papers. Tus, a meta-data analysis was perfrmed using te bibligrapic datasets, wic cntain te metadata infrmatin, like jurnals, autrs, publicatin year, citatins, and institutins. Te metadata analysis, als called as biblimetric analysis, belngs t te field f scientmetrics. Te analysis as te advantages f data-driven caracteristics, and being bjective, cmplete and repeatable [75]. Te analysis invlved te fllwing steps: cllecting datasets frm te bibligrapic databases wit explicit searc strategies; analysis f yearly utput, tpics, and related disciplines; and displaying f an verall picture f DL-agriculture researc. Te searc strategy is as fllws: (TS=(deep learning) AND SU=agriculture) AND language: (Englis) AND type: (Article) Time span: Index: SCI-EXPANDED, SSCI were, TS stands fr tpic, SU stands fr researc area. Frty-seven recrds were btained by applying te searc term in te cre cllectin f Web f Science database. SU=agriculture was used t cnfine te researc areas in te field f agriculture. Te analysis results were displayed as fllws. Publicatin Yearly Output Te publicatin cunts, t sme degree, reveal te researc intensity in a field. Te yearly utput f publicatins is swn in Figure 11. Als, te Ttal Lcal Citatin Scres (TLCS) and Ttal Glbal Citatin Scres (TGCS) are swn in te figure. Te primary vertical axis is set fr te Recs and TLCS, and te secndary vertical axis is set fr TGCS. TLCS stands fr te field recgnitin and TGCS stands fr te recgnitin witut field cnstraint. Tese values are cmputed wit te biblimetric sftware, HistCite [76]. Figure 11 Publicatin yearly utput (Recs stands fr article recrds, TLCS stands fr te Ttal Lcal Citatin Scres, TGCS stands fr te Ttal Glbal Citatin Scres) Frm te figure, it can be bserved tat te verall publicatin trend is upward frm te Recs cunts. Hwever, frm te number canges f te TLCS, ne can tell tat mst f te TLCS are zers, meaning tat DL-agriculture applicatin articles are nt citing eac ter. Tis suggests tat applicatins f DL in agriculture fields are igly divergent and scattered in very different researc purpses. It als implies tat mre callenges and cances remain in te researc directin. A ntable pint in te TGCS is tat in 2002, te paper Canging systems fr supprting farmers' decisins: prblems, paradigms, and prspects [77] drew wide attentins frm all disciplines. Tis paper may be regarded as a pineering article advcating macine learning fr agriculture applicatins. As tere are citatin windws, te papers need time t accrue citatins. Te papers in 2016 are impressive fr aving acquired 44 glbal citatins (TGCS=44). DL-agriculture papers in 2016 discussed tpics including plant identificatins [78], and pest detectins [79], etc. T ave a clse lk at te researc tpics, te fllwing c-wrd analysis was cnducted, wic was based n te keywrd c-ccurrence relatins. Te illustratin was generated by te biblimetric sftware, CiteSpace [80], as swn in Figure 12. Figure 12 C-wrd visualizatin fr te researc articles In Figure 12, te nde size is prprtinal t te keywrd frequencies. Te different clusters are generated and tagged wit plygns. Te tpics fr eac cluster were generated wit Latent Diriclet Allcatin (LDA) [81], as swn by te red-clred wrds. Te first and te secnd cluster (cluster #0 and cluster #1) fcus n te tpic f develpment plicy. Te tird and furt cluster (cluster #2 and cluster #3) fcus n recgnitin, using CNN and structure similarity tery, respectively. Te revealed tpics prvide a landscape f te dmain expert knwledge at a micr level. Hw te subject f agriculture in a macr-level is affected by te researc fever is still nt clear. Jurnals can reveal te macr status f researc trends t sme level. Relevant jurnals were investigated and te results are listed in Table 3. Table 3 Tp ten ig-impact jurnals tat publis DL-agriculture papers Index Jurnal Recs TLCS TGCS 1 Agricultural Systems Bilgy and Fertility f Sils Vadse Zne Jurnal Agricultural Ecnmics Cmputers and Electrnics in Agriculture Agricultural Sciences in Cina Bisystems Engineering Canadian Jurnal f Sil Science Agriculture and Human Values Crp & Pasture Science Te jurnals are listed by te descending rder f TGCS index, iger ranking meaning iger all-discipline impacts. Te number prvides a direct and simple indicatr fr te jurnal impacts n te DL-agriculture researc. Hwever, te distributin n w te researc cmbines different disciplines is still nt clear.

10 July, 2018 Zu N Y, et al. Deep learning fr smart agriculture: Cncepts, tls, applicatins, and pprtunities Vl. 11 N.4 41 Te fllwing verlay visualizatin based n dual-mapping was tus perfrmed, as swn in Figure 13. Te dual-mapping cnsists f tw mappings based n datasets f large-scale jurnals frm all disciplines. Te wle science mappings cnsist f te citing jurnal cluster n te left side and te cited jurnal cluster n te rigt. Te citing cluster cntain all-discipline jurnals and te cited cluster cntain all-discipline jurnals [82]. Eac pint in te figure stands fr a jurnal and te large-scale netwrk clustering metd generated tese clusters, called Blndel clusters [83]. Tus, te citing trajectries ver different disciplines can be visually depicted n tis base map. One can tell tat plant science, cemistry, and ecnmics are als te citatin surces besides te cmputer science discipline. Tis is in line wit intuitive understanding. Mre researc energy may ave te ptential t link te abve mentined disciplines in te near future. Figure 13 Overlay visualizatin f DL-agriculture researc n te wle science dual-mapping 5 Discussin Mst f te recent advances in agriculture fields made by researcers are clsely cnnected t prductin and every ter part f agriculture fr te purpses f imprving prductivity f crps, reducing and preparing fr te plant diseases, bsting mecanized and autmated mdern agriculture and agr-industry. DL is usually used fr image recgnitin r data classificatin, wic can be summarized t include fur steps: data cllectin and data preprcessing, neural netwrk training, mdel testing, and final result analysis. Fr te first step, te cmbinatin f DL wit ter advanced tecnlgies, suc as unmanned aerial veicles (UAVs), radar, and Internet f Tings, can prvide ig quality datasets f images and ter frms. Tese data greatly enance applicatins f DL in agriculture and imprve te accuracy f resulting tls [84]. Fr te secnd step, new training algritms and metds can enance te accuracy, especially fr tse applicatins tat require ig precisin. K-means feature learning, blb detectin, FCNN, AlexNet etc. play an increasingly imprtant rle. Mdel testing wit new data is always an imprtant tird step. In te final step, te results are interpreted and analyzed. Eiter frm istrical researc statistics r te recent researc results, it can be swn tat DL can be well applied in agriculture t slve varius prblems tat ave cncerned farmers and scientists fr a lng time. Wit te elp f nvel tecniques and new teries, tese new appraces utperfrm te previus metds in many ways. Take image recgnitin fr instance, previusly, sclars may cllect images and use DL t d classificatin by analyzing pixels in RGB images [85]. Te result f tis metd can be satisfactry under certain ptimal cnditins. Because f illuminatin canges, displacement f targets by winds,

11 42 July, 2018 Int J Agric & Bil Eng Open Access at ttps:// Vl. 11 N.4 camera jitter, zm canges, unexpected canges in camera parameters; incnsistent recgnitin and classificatin ccur [41]. Terefre, clr analysis can be unreliable since it usually relies n te clr distributin in an image and tere are many cnditins were te tempral cnsistency f tis feature is vilated [36]. Fr plant clr applicatin, metds based n vein and pattern recgnitin ave been prpsed t reduce te uncertainty fr analysis f clr in an image [86], because vein is als a rbust and significant feature f te plants and usually vein mrplgical patterns are a gd leaf fingerprint. Te crrelatin between vein caracteristics and sme prperties f te leaf seldm cange wen and after pre-prcessing te images. Because tere are many applicatins in agriculture nt equipped wit recent new tecniques, tere is still a lt f rm fr expanding DL in agriculture researc. Altug sme f te results gained accuracy arund r iger tan 95%, rbustness and reliability are still callenges. Te prmise f te applicatin f DL in agriculture can be freseen. Mrever, it is very pssible tat future develpment f DL in agriculture will be based n nt nly a single tery r metd but a cmbinatin f multiple metds. Te metadata analysis sws tat applicatins f DL in agriculture are upward frm te Recs cunts. Hwever, tey are igly divergent and scattered in very different researc purpses. It implies tat mre callenges and pprtunities remain in te area and mre effrts are needed. 6 Cnclusins In tis summary, te cncepts, tls, limitatins, algritms f DL are summarized. Applicatins f DL in agriculture are reviewed. It can be bserved tat DL as been widely used in different areas f agriculture, suc as plant disease detectin, plant classificatin and weed identificatin, fruit cunting, land classificatin, bstacle detectin, image translatin, weater frecasting, yield predictin, and animal beavir classificatin. Wile DL researc is gaining mmentum in general, researc f DL fr agriculture is very divergent accrding t te metadata analysis. Tere are many pprtunities fr DL applicatins in smart agriculture, suc as: (1) Agriculture infrmatin prcessing. Mnitring te status f plants and animals is vital t agriculture prductin. Sme status variables f plants and animals cannt be measured directly. Under tis case, DP can be used t determine unmeasured infrmatin frm measured ne because different variables f plant status may ave sme dependent relatinsips. In agriculture prductin, plants r animals interact wit envirnmental factrs. It is difficult t build a pure mecanism-based-mdel structure t describe te relatinsip between plants (r animals) and envirnmental factrs. As a data-driven-mdel structure, DP can be directly used t build te relatinsip f plant (r animal) factrs, envirnmental factrs and plant (r animal) grwt status. Tis will facilitate agriculture infrmatin prcessing. (2) Agriculture prductin system ptimal cntrl. Cntrl strategies in agriculture prductin system ften rely n farmer experience r experts knwledge, wic d nt cnsider plant (animal) pysilgical status r real time demand. Tis unavidably makes te strategies nt ptimal. An advantage f DP is t mdel cmplex systems witut eavily relying n te knwledge f mecanisms. By using DP t mdel agriculture prductin systems, ptimal cntrl strategy develpment tus becmes pssible, wic makes use real time measurement f plant (animal) pysilgical status and istrical data. (3) Smart agriculture macinery equipments. Agriculture prductin invlves numerus kinds f tasks. Tese tasks are ften labr cnsuming and te wrking envirnment is very callenging. Using DP t mimic uman beavir and driven agriculture macinery equipments as prspective future in many areas f agriculture, suc as seeding, management, arvesting, and pst-arvest prcessing. Fr example, a rbt tat can be used t arvest apples is very useful. It must be intelligent enug t psitin apples and pick apples wit a ig efficiency. Under natural cnditin, backgrund ligt, tree brances and leaves ave strng interference t cmputer visin signal. Te rutes f rbt arms reac targets suld als be ptimized. Fr all tese issues, DP tecniques may be very useful. (4) Agricultural ecnmic system management. Agriculture yield itself is nt enug fr agriculture. Tere are many mre factrs suld be cnsidered suc as te prices and te quality f agriculture prducts. It is very meaningful t predict agriculture prduct prices. Hwever, prices are related t many variables. Under tis case, DP can be used t mdel price canges wit different variables. Tere are cmplex relatinsips between agriculture prduct quality and nutritin, uman ealt, and ecnmy. DP can be used t mdel te cmplex relatinsip and enance agricultural ecnmic system management. Acknwledgements Tis prject is partially supprted by Natinal Natural Science Fundatin f Cina (N ), Fundamental Researc Funds fr te Central Universities f Cina (N: JUSRP51730A), te Mdern Agriculture Funds f Jiangsu Prvince (N. BE ), te Mdern Agriculture Funds f Jiangsu Prvince (Vegetable) (N. SXGC[2017]210), te New Agricultural Engineering f Jiangsu Prvince (N. SXGC[2016]106), te 111 Prject (B1208), and te Researc Funds fr New Faculty f Jiangnan University. [References] [1] Scalkff R J. Artificial Neural Netwrks. Vl. 1. New Yrk: McGraw-Hill, [2] Scmiduber J. Deep learning in neural netwrks: An verview. Neural Netwrks, 2015; 61: [3] Kamilaris A, Prenafeta-Bldú F X. Deep learning in agriculture: A survey. 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