# A Spatial Feature-Enhanced Attention Neural Network with High-Order Pooling Representation for Application in Pest and Disease Recognition

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## Abstract

**:**

## 1. Introduction

## 2. Related Work

#### 2.1. Pest and Disease Diagnosis Methods and Datasets

#### 2.2. Visual Attention Mechanism

#### 2.3. Fine-Grained Visual Recognition Modeling

## 3. Methods and Materials

#### 3.1. CropDP-181 Dataset

#### 3.2. Improved CSP-Stage-Based Backbone

#### 3.3. Spatial Feature-Enhanced Attention Module

#### 3.4. Iterative Computation of Matrix Square Root for Fast Training of Global Covariance Pooling

Algorithm 1. The overall calculating steps of the high-order pooling module. |

Calculating processes in high-order pooling module |

Input:F is a feature of the input, k is the number of iterations |

Output:Out is the higher-order feature of the output |

$X=conv\left(F\right)$ where $X\in {\mathbb{R}}^{n\times c},n=w\times h$ |

$\Pi =X\overline{I}{X}^{T}$ where $\overline{I}=\frac{1}{n}\left(I-\frac{1}{n}1\right)$ |

$P=\frac{1}{tr\left(\Pi \right)}\Pi $, and set ${M}_{0}=P$, ${N}_{0}=I$ |

$foritokdo$ ${M}_{i}=\frac{1}{2}{M}_{i-1}\left(3I-{N}_{i-1}{M}_{i-1}\right)$ |

${N}_{i}=\frac{1}{2}\left(3I-{N}_{i-1}{M}_{i-1}\right){N}_{i-1}$ |

$Out=\sqrt{tr\left(\Pi \right)}{M}_{k}$ |

Return Out |

#### 3.5. Data Processing and Parameter Settings

#### 3.5.1. Data Preprocessing

#### 3.5.2. Parameter Settings

^{−3}. The cosine annealing learning rate reduction is started at the 31st cycle, and the minimum learning rate is set to 1 × 10

^{−6}. For each restart, the learning rate is 70% of the initial learning rate of the previous cycle, and the cosine annealing learning rate is set to 1 × 10

^{−6}. Finally, the cosine annealing step is set to 2, the length base cycle of each stage is 10, and the learning rate is restarted at the 41st and 61st cycles.

## 4. Experimental Results

_{1}), and average recognition time (ART).

_{1}. It is the harmonic average of precision and recall to comprehensively characterize the modeling classification performance, of which the minimum and maximum values are 0 and 1. Moreover, the average recognition time (ART) represents how long the trained model needs to handle a single image and recognize massive different samples in the testing stage. Obviously, the smaller the ART value, the better the efficiency modeling performance in recognizing a single image for agriculture practices.

#### 4.1. Contrastive Results

#### 4.2. Ablation Analyses

#### 4.3. Module Effect Discussion

## 5. Conclusions

_{1}with only a 61 ms average recognition time demonstrates the better efficiency and robustness of Fe-Net, which meets the practical demands of different IoT devices and equipment in precision agriculture applications. The proposed approaches in the paper can combine other parameter estimation algorithms [70,71,72,73,74,75] to study the parameter identification problems of linear and nonlinear systems with different disturbances [76,77,78,79,80,81], and can be applied to other fields [82,83,84,85,86] such as signal processing and engineering application systems.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

No. | Annotation Names | Image Sample Numbers | Associated Crops or Plants | Actual Collection | IP102 Dataset | Inaturalist Dataset | AIChallenger Dataset | Additional Info |
---|---|---|---|---|---|---|---|---|

1 | Spodoptera exigua | 214 | Rice, sugar cane, corn, Compositae, cruciferous, etc. | 38 | 65 | 111 | 0 | Pests |

2 | Migratory locust | 122 | Red grass, barnyard grass, climbing grass, sorghum, wheat, etc. | 40 | 25 | 57 | 0 | Pests |

3 | Meadow webworm | 230 | Beet, soybean, sunflower, potato, medicinal materials, etc. | 43 | 73 | 114 | 0 | Pests |

4 | Mythimna separata | 134 | Wheat, rice, millet, corn, cotton, beans, etc. | 44 | 59 | 31 | 0 | Pests |

5 | Nilaparvata lugens | 155 | Rice, etc. | 47 | 88 | 20 | 0 | Pests |

6 | Sogatella furcifera | 152 | Rice, wheat, corn, sorghum, etc. | 50 | 32 | 70 | 0 | Pests |

7 | Cnaphalocrocis medinalis | 154 | Rice, barley, wheat, sugar cane, millet, etc. | 51 | 80 | 23 | 0 | Pests |

8 | Chilo suppressalis | 156 | Rice, etc. | 52 | 45 | 59 | 0 | Pests |

9 | Sitobion miscanthi | 164 | Wheat, barley, oats, naked oats, sugar cane, etc. | 54 | 31 | 79 | 0 | Pests |

10 | Rhopalosiphum padi | 174 | Wheat, barley, oats, etc. | 58 | 91 | 25 | 0 | Pests |

11 | Schizaphis graminum | 280 | Wheat, barley, oats, sorghum, rice, etc. | 93 | 33 | 154 | 0 | Pests |

12 | Leptinotarsadecemlineata | 314 | Potato, tomato, eggplant, chili, tobacco, etc. | 104 | 43 | 167 | 0 | Pests |

13 | Cydiapomonella | 436 | Apples, pears, apricots, etc. | 145 | 112 | 179 | 0 | Pests |

14 | Locusta migratoria manilensis | 867 | Wheat, rice, tobacco, fruit trees, etc. | 189 | 395 | 283 | 0 | Pests |

15 | Grassland caterpillar | 370 | Cyperaceae, Gramineae, Leguminosae, etc. | 123 | 48 | 199 | 0 | Pests |

16 | Sitodiplosis mosellana Géhin | 470 | Wheat, etc. | 156 | 164 | 150 | 0 | Pests |

17 | Plutella xylostella_Linnaeus | 371 | Cabbage, purple cabbage, broccoli, etc. | 123 | 229 | 19 | 0 | Pests |

18 | Trialeurodes vaporariorum | 402 | Cucumber, kidney bean, eggplant, tomato, green pepper, etc. | 134 | 18 | 250 | 0 | Pests |

19 | Bemisia tabaci_Gennadius | 403 | Tomato, cucumber, zucchini, cruciferous vegetables, fruit trees, etc. | 134 | 67 | 202 | 0 | Pests |

20 | Aphis gossypii Glover | 417 | Pomegranate, pepper, hibiscus, cotton, melon, etc. | 139 | 265 | 13 | 0 | Pests |

21 | Myzus persicae | 460 | Vegetables, potatoes, tobacco, stone fruit trees, etc. | 153 | 287 | 20 | 0 | Pests |

22 | Penthaleus major | 492 | Wheat, etc. | 164 | 65 | 263 | 0 | Pests |

23 | Petrobia latens | 493 | Wheat, etc. | 164 | 43 | 286 | 0 | Pests |

24 | Helicoverpa armigera | 513 | Corn, zucchini, pea, wheat, tomato, sunflower, etc. | 171 | 271 | 71 | 0 | Pests |

25 | Spodoptera exigua | 546 | Corn, cotton, sugar beet, sesame, peanut, etc. | 0 | 187 | 359 | 0 | Pests |

26 | Apolygus lucorum | 546 | Cotton, mulberry, jujube, grape, cruciferous vegetables, etc. | 0 | 376 | 170 | 0 | Pests |

27 | Bemisia tabaci | 1255 | Cucumber, tomato, eggplant, zucchini, cotton, watermelon, etc. | 0 | 611 | 644 | 0 | Pests |

28 | Ostrinia furnacalis | 662 | Corn, wheat, etc. | 0 | 347 | 315 | 0 | Pests |

29 | Ostrinia nubilalis | 1316 | Corn, sorghum, hemp, rice, sugar beet, sweet potato, etc. | 0 | 693 | 623 | 0 | Pests |

30 | Tetranychus turkestani | 1234 | Cotton, sorghum, strawberry, beans, corn, potato, etc. | 0 | 710 | 524 | 0 | Pests |

31 | Tetranychus truncates Ehrar | 1477 | Cotton, corn, polygonum, paper mulberry, etc. | 0 | 841 | 636 | 0 | Pests |

32 | Tetranychus dunhuangensis Wang | 1288 | Cotton, corn, vegetables, fruit trees, etc. | 0 | 770 | 518 | 0 | Pests |

33 | Yellow cutworm | 1331 | Wheat, vegetable, grass, etc. | 0 | 793 | 538 | 0 | Pests |

34 | Police-striped ground tiger | 834 | Rape, radish, potato, green Chinese onion, alfalfa, flax, etc. | 0 | 241 | 593 | 0 | Pests |

35 | Eight-character ground tiger | 1237 | Daisies, zinnia, chrysanthemum, etc. | 0 | 686 | 551 | 0 | Pests |

36 | Cotton thrips | 1286 | Zucchini, wax gourd, balsam pear, watermelon, tomato, etc. | 0 | 856 | 430 | 0 | Pests |

37 | Grass blind stinkbug | 824 | Cotton, alfalfa, vegetables, fruit trees, hemp, etc. | 0 | 289 | 535 | 0 | Pests |

38 | Alfalfa blind stinkbug | 866 | Cotton, mulberry, jujube, grape, alfalfa, medicinal plants, etc. | 0 | 428 | 438 | 0 | Pests |

39 | Green stinkbug | 948 | Flowers, artemisia, cruciferous vegetables, etc. | 0 | 348 | 600 | 0 | Pests |

40 | Tomato leaf miner | 965 | Tomato, potato, sweet pepper, ginseng fruit, etc. | 0 | 496 | 469 | 0 | Pests |

41 | Dendrolimus punctatus | 1103 | Masson pine, black pine, slash pine, loblolly pine, etc. | 0 | 371 | 732 | 0 | Pests |

42 | Japanese pine scale | 1176 | Pinus densiflora, pinus tabulaeformis, pinus massoniana, etc. | 0 | 241 | 935 | 0 | Pests |

43 | Anoplophora glabripennis | 1335 | Poplar, willow, wing willow, elm, sugar maple, etc. | 0 | 497 | 838 | 0 | Pests |

44 | American white moth | 2236 | Oak, phoenix tree, poplar, willow, elm, mulberry, pear, etc. | 0 | 1620 | 616 | 0 | Pests |

45 | Hemiberlesia matsumura | 2024 | Masson pine, black pine, slash pine, loblolly pine, etc. | 0 | 1709 | 315 | 0 | Pests |

46 | Red tip borer | 1833 | Masson pine, black pine, slash pine, loblolly pine, etc. | 0 | 1497 | 336 | 0 | Pests |

47 | Dendroctonus armandi | 1824 | Huashan pine, etc. | 0 | 1275 | 549 | 0 | Pests |

48 | Yellow bamboo locust | 1527 | Rigid bamboo, water bamboo, etc. | 1527 | 0 | 0 | 0 | Pests |

49 | Monochamus fortunei | 1197 | Fir, willow, etc. | 1197 | 0 | 0 | 0 | Pests |

50 | Sophora japonica | 1498 | Yang, Huai, Liu, Amorpha fruticosa, elm, maple, etc. | 1498 | 0 | 0 | 0 | Pests |

51 | Ulmus pumila | 2228 | Elm, etc. | 2228 | 0 | 0 | 0 | Pests |

52 | Pine geometrid | 1272 | Pine needles, etc. | 1272 | 0 | 0 | 0 | Pests |

53 | Jujube scale | 1087 | Acer is acacia, jujube, walnut, acacia, plum, pear, apple, etc. | 1087 | 0 | 0 | 0 | Pests |

54 | Coconut beetle | 1109 | Coconut trees, etc. | 1109 | 0 | 0 | 0 | Pests |

55 | Anoplophora longissima | 1149 | Yang, willow, birch, oak, beech, linden, elm, etc. | 1149 | 0 | 0 | 0 | Pests |

56 | Geometrid moth | 1115 | Fruit trees, tea trees, mulberry trees, cotton and pine trees, etc. | 1115 | 0 | 0 | 0 | Pests |

57 | Red brown weevil | 405 | Coconut, oil palm, brown, betel nut, mallow, date, etc. | 405 | 0 | 0 | 0 | Pests |

58 | Dendroctonus valens | 1100 | Larch, fir, pine, white pine, pine, etc. | 1100 | 0 | 0 | 0 | Pests |

59 | Euplophora salicina | 1173 | Oak, Cyclobalanopsis glauca, birch, elm, alder, park and maple, etc. | 1173 | 0 | 0 | 0 | Pests |

60 | Ailanthus altissima | 1227 | Ailanthus altissima, toona ciliata, etc. | 1227 | 0 | 0 | 0 | Pests |

61 | Termite | 1164 | Within each plant | 1164 | 0 | 0 | 0 | Pests |

62 | Pine wood nematode | 390 | Masson pine forest, etc. | 390 | 0 | 0 | 0 | Pests |

63 | Yellow moth | 402 | Jujube, walnut, persimmon, maple, apple, Yang, etc. | 402 | 0 | 0 | 0 | Pests |

64 | Icerya purchasi maskell | 1020 | Boxwood, citrus, tung, holly, pomegranate, papaya, etc. | 1020 | 0 | 0 | 0 | Pests |

65 | Adelphocoris lineolatus | 1107 | Masson pine, fir, spruce, corns, cedar, larch, etc. | 1107 | 0 | 0 | 0 | Pests |

66 | Tomicus piniperda | 200 | Huashan pine, alpine pine, Yunnan pine, etc. | 200 | 0 | 0 | 0 | Pests |

67 | Rice leaf caterpillar | 201 | Rice, sorghum, corn, sugar cane, etc. | 0 | 91 | 110 | 0 | Pests |

68 | Paddy stem maggot | 128 | Rice, etc. | 0 | 72 | 56 | 0 | Pests |

69 | Asiatic rice borer | 814 | Rice, etc. | 0 | 560 | 254 | 0 | Pests |

70 | Yellow rice borer | 1138 | Rice, etc. | 0 | 636 | 502 | 0 | Pests |

71 | Rice gall midge | 1003 | Rice, lishihe, etc. | 0 | 813 | 190 | 0 | Pests |

72 | Rice stemfly | 124 | Rice, oil grass, etc. | 0 | 80 | 44 | 0 | Pests |

73 | Ampelophaga | 110 | Grapes | 0 | 105 | 5 | 0 | Pests |

74 | Earwig Furficulidae | 158 | Rice, grasses, alismataceae, commelina, etc. | 0 | 74 | 84 | 0 | Pests |

75 | Rice leafhopper | 223 | Rice, etc. | 0 | 64 | 159 | 0 | Pests |

76 | Rice shell pest | 763 | Rice, sesame, pumpkin, cotton, etc. | 0 | 530 | 233 | 0 | Pests |

77 | Black cutworm | 282 | Corn, cotton, tobacco, etc. | 0 | 239 | 43 | 0 | Pests |

78 | Tipulidae | 328 | Cotton, corn, sorghum, tobacco, etc. | 0 | 146 | 182 | 0 | Pests |

79 | Yellow cutworm | 150 | Crops, grasses and turfgrasses | 0 | 106 | 44 | 0 | Pests |

80 | Red spider | 282 | Solanaceae, Cucurbitaceae, Leguminosae, Liliaceae, etc. | 0 | 121 | 161 | 0 | Pests |

81 | Peach borer | 1003 | Chestnut, corn, sunflower, peach, plum, hawthorn, etc. | 0 | 401 | 602 | 0 | Pests |

82 | Curculionidae | 144 | Wheat, barley, oats, rice, corn, sugar cane, grass, etc. | 0 | 119 | 25 | 0 | Pests |

83 | Rhopalosiphum padi | 394 | Plum, peach, plum, etc. | 0 | 243 | 151 | 0 | Pests |

84 | Wheat blossom midge | 986 | Wheat | 0 | 424 | 562 | 0 | Pests |

85 | Pentfaleusmajor | 576 | Wheat, barley, peas, broad beans, rape, Chinese milk vetch, etc. | 0 | 308 | 268 | 0 | Pests |

86 | Aphidoidea | 142 | Wheat, barley, peas, alfalfa, weeds, etc. | 0 | 109 | 33 | 0 | Pests |

87 | Spodoptera frugiperda | 282 | Wheat, barley, rye, oat, sunflower, dandelion, green bristlegrass, etc. | 0 | 142 | 140 | 0 | Pests |

88 | Spodoptera litura Fabricius | 227 | Wheat | 0 | 139 | 88 | 0 | Pests |

89 | Mamestra brassicae Linnaeus | 169 | Wheat, oats, barley, etc. | 0 | 23 | 146 | 0 | Pests |

90 | Herminiinae | 2730 | Wheat, rice, etc. | 0 | 20 | 2710 | 0 | Pests |

91 | Cabbage army worm | 237 | Cabbage, cabbage, radish, spinach, carrot, etc. | 0 | 78 | 159 | 0 | Pests |

92 | Beet spot flies | 116 | Beet, cabbage, rape, cabbage, etc. | 0 | 64 | 52 | 0 | Pests |

93 | Psyllidae | 925 | Pear, peach, etc. | 0 | 552 | 373 | 0 | Pests |

94 | Alfalfa weevil | 172 | Clover, etc. | 0 | 37 | 135 | 0 | Pests |

95 | Acrida cinerea | 273 | Pea, soybean, sunflower, hemp, beet, cotton, tobacco, potato | 0 | 252 | 21 | 0 | Pests |

96 | Legume blister beetle | 130 | Legume | 0 | 21 | 109 | 0 | Pests |

97 | Therioaphis maculata buckton | 244 | Leguminosae forage | 0 | 81 | 163 | 0 | Pests |

98 | Odontothrips loti | 153 | Alfalfa | 0 | 100 | 53 | 0 | Pests |

99 | Thrips | 320 | Eggplant, cucumber, kidney bean, pepper, watermelon, etc. | 0 | 195 | 125 | 0 | Pests |

100 | Alfalfa seed chalcid | 491 | Leguminosae forage seed | 0 | 208 | 283 | 0 | Pests |

101 | Pieris canidia | 1003 | Cauliflower | 0 | 839 | 164 | 0 | Pests |

102 | Slug caterpillar moth | 190 | Bamboo and rice | 0 | 99 | 91 | 0 | Pests |

103 | Grape phylloxera | 284 | Grape | 0 | 165 | 119 | 0 | Pests |

104 | Colomerus vitis | 176 | Grape | 0 | 16 | 160 | 0 | Pests |

105 | Oides decempunctata | 1003 | Grapes, wild grapes, blackberries, etc. | 0 | 938 | 65 | 0 | Pests |

106 | paranthrene regalis butler | 260 | Grape | 0 | 190 | 70 | 0 | Pests |

107 | Eumenid poher wasp | 330 | Rice, corn, sorghum and wheat, etc. | 0 | 16 | 314 | 0 | Pests |

108 | Coccinellidae | 444 | Wheat, citrus, zanthoxylum bungeanum, citrus, etc. | 0 | 23 | 421 | 0 | Pests |

109 | Phyllocoptes oleiverus ashmead | 177 | Citrus | 0 | 109 | 68 | 0 | Pests |

110 | Crioceridae | 177 | Rice, centurion, euonymus japonicus, etc. | 0 | 70 | 107 | 0 | Pests |

111 | Ceroplastes rubens | 450 | Laurel, gardenia, osmanthus, rose, etc. | 0 | 450 | 0 | 0 | Pests |

112 | Parlatoria zizyphus lucus | 117 | Citrus plants, dates, coconuts, oil palm, laurel. | 0 | 97 | 20 | 0 | Pests |

113 | Aleurocanthus spiniferus | 192 | Citrus, oil tea, pear, persimmon, grape, etc. | 0 | 33 | 159 | 0 | Pests |

114 | Tetradacus c bactrocera minax | 194 | Mandarin orange and pomelo | 0 | 116 | 78 | 0 | Pests |

115 | Bactrocera tsuneonis | 635 | Citrus | 0 | 257 | 378 | 0 | Pests |

116 | Phyllocnistis citrella stainton | 219 | Citrus, willow, kumquat, etc. | 0 | 85 | 134 | 0 | Pests |

117 | Aphis citricola vander goot | 311 | Apple, amomum villosum, begonia, etc. | 0 | 253 | 58 | 0 | Pests |

118 | Atractomorpha sinensis Bolivar | 259 | Canna, celosia, chrysanthemum, hibiscus, poaceae, etc. | 0 | 236 | 23 | 0 | Pests |

119 | Sternochetus frigidus Fabricius | 154 | Mango | 0 | 107 | 47 | 0 | Pests |

120 | Mango flat beak leafhopper | 1003 | Mango | 0 | 244 | 759 | 0 | Pests |

121 | Flea beetle | 618 | Glycyrrhrizae radix, willow seedlings, etc. | 0 | 64 | 554 | 0 | Pests |

122 | Brevipoalpus lewisi mcgregor | 556 | Parthenocissus tricuspidata, magnolia officinalis, lilac, etc. | 0 | 390 | 166 | 0 | Pests |

123 | Polyphagotars onemus latus | 4385 | Melon, eggplant, pepper, etc. | 0 | 1118 | 3267 | 0 | Pests |

124 | Cicadella viridis | 120 | Poplar, willow, ash, apple, peach, pear, etc. | 0 | 82 | 38 | 0 | Pests |

125 | Rhytidodera bowrinii white | 210 | Mango, cashew nuts, face, etc. | 0 | 53 | 157 | 0 | Pests |

126 | Aphis citricola Vander Goot | 110 | Apple, sand fruit, begonia, etc. | 0 | 84 | 26 | 0 | Pests |

127 | Deporaus marginatus Pascoe | 296 | Mango, cashew nut and almond | 0 | 149 | 147 | 0 | Pests |

128 | Adristyrannus | 267 | Citrus, apple, grape, loquat, mango, pear, peach, etc. | 0 | 230 | 37 | 0 | Pests |

129 | Salurnis marginella Guerr | 285 | Coffee, tea, camellia oleifera, citrus, etc. | 0 | 272 | 13 | 0 | Pests |

130 | Dacus dorsalis | 201 | oranges, tangerines, etc. | 0 | 174 | 27 | 0 | Pests |

131 | Dasineura sp | 1247 | lychee, etc. | 0 | 555 | 692 | 0 | Pests |

132 | Trialeurodes vaporariorum | 1045 | Cucumber, kidney bean, eggplant, tomato, green pepper, etc. | 0 | 623 | 422 | 0 | Pests |

133 | Eriophyoidea | 361 | Citrus, apple, grape, loquat, mango, pear, peach, etc. | 0 | 0 | 361 | 0 | Pests |

134 | Mane gall mite | 854 | Chinese wolfberry | 0 | 0 | 854 | 0 | Pests |

135 | Mulberry powdery mildew | 260 | White mulberry | 0 | 0 | 0 | 260 | Diseases |

136 | Tobacco anthracnose | 229 | tobacco | 0 | 0 | 0 | 229 | Diseases |

137 | Apple_Scab general | 321 | Apple | 80 | 0 | 0 | 241 | Diseases |

138 | Apple_Scab serious | 232 | Apple | 58 | 0 | 0 | 174 | Diseases |

139 | Apple Frogeye Spot | 650 | Apple | 162 | 0 | 0 | 488 | Diseases |

140 | Cedar Apple Rust general | 277 | Apple | 69 | 0 | 0 | 208 | Diseases |

141 | Medlar powdery mildew | 170 | Medlar | 42 | 0 | 0 | 128 | Diseases |

142 | Medlar anthracnose | 170 | Medlar | 42 | 0 | 0 | 128 | Diseases |

143 | Grape powdery mildew | 290 | Grape | 72 | 0 | 0 | 218 | Diseases |

144 | Tehon and Daniels serious | 254 | Corn | 63 | 0 | 0 | 191 | Diseases |

145 | Rice bakanae | 736 | Corn | 184 | 0 | 0 | 552 | Diseases |

146 | Puccinia polysora serious | 541 | Corn | 135 | 0 | 0 | 406 | Diseases |

147 | Puccinia polysra | 316 | Corn | 79 | 0 | 0 | 237 | Diseases |

148 | Curvularia leaf spot fungus serious | 758 | Corn | 189 | 0 | 0 | 569 | Diseases |

149 | Maize dwarf mosaic virus | 1241 | Corn | 310 | 0 | 0 | 931 | Diseases |

150 | Grape Black Rot Fungus general | 580 | Grape | 145 | 0 | 0 | 435 | Diseases |

151 | Grape Black Rot Fungus serious | 704 | Grape | 176 | 0 | 0 | 528 | Diseases |

152 | Grape Black Measles Fungus general | 769 | Grape | 192 | 0 | 0 | 577 | Diseases |

153 | Grape Black Measles Fungus serious | 637 | Grape | 159 | 0 | 0 | 478 | Diseases |

154 | Grape Leaf Blight Fungus serious | 960 | Grape | 240 | 0 | 0 | 720 | Diseases |

155 | Liberobacter asiaticum | 1796 | Orange | 699 | 0 | 0 | 1097 | Diseases |

156 | Citrus Greening June serious | 1748 | Orange | 687 | 0 | 0 | 1061 | Diseases |

157 | Grape brown spot | 1305 | Grape | 326 | 0 | 0 | 979 | Diseases |

158 | Peach_Bacterial Spot serious | 1173 | Peach | 293 | 0 | 0 | 880 | Diseases |

159 | Peach scab | 695 | Peach | 327 | 0 | 0 | 368 | Diseases |

160 | Pepper scab | 512 | Pepper | 81 | 0 | 0 | 431 | Diseases |

161 | Pear scab | 519 | Pear | 232 | 0 | 0 | 287 | Diseases |

162 | Potato_Early Blight Fungus serious | 692 | Potato | 109 | 0 | 0 | 583 | Diseases |

163 | Phyllostcca pirina Sacc | 452 | Potato | 240 | 0 | 0 | 212 | Diseases |

164 | Potato_Late Blight Fungus serious | 623 | Potato | 113 | 0 | 0 | 510 | Diseases |

165 | Strawberry_Scorch general | 601 | Strawberry | 219 | 0 | 0 | 382 | Diseases |

166 | Strawberry_Scorch serious | 673 | Strawberry | 97 | 0 | 0 | 576 | Diseases |

167 | Tomato powdery mildew general | 630 | Tomato | 365 | 0 | 0 | 265 | Diseases |

168 | Tomato powdery mildew serious | 487 | Tomato | 83 | 0 | 0 | 404 | Diseases |

169 | Strawberry leaf blight | 939 | Strawberry | 287 | 0 | 0 | 652 | Diseases |

170 | Tomato_Early Blight Fungus serious | 617 | Tomato | 112 | 0 | 0 | 505 | Diseases |

171 | Tomato_Late Blight Water Mold general | 611 | Tomato | 302 | 0 | 0 | 309 | Diseases |

172 | Tomato_Late Blight Water Mold serious | 830 | Tomato | 163 | 0 | 0 | 667 | Diseases |

173 | Tomato_Leaf Mold Fungus general | 807 | Tomato | 371 | 0 | 0 | 436 | Diseases |

174 | Tomato_Leaf Mold Fungus serious | 471 | Tomato | 87 | 0 | 0 | 384 | Diseases |

175 | Tomato_Septoria Leaf Spot Fungus general | 549 | Tomato | 281 | 0 | 0 | 268 | Diseases |

176 | Tomato_Septoria Leaf Spot Fungus serious | 1132 | Tomato | 210 | 0 | 0 | 922 | Diseases |

177 | Tomato Mite Damage general | 930 | Tomato | 319 | 0 | 0 | 611 | Diseases |

178 | Tomato Mite Damage serious | 929 | Tomato | 480 | 0 | 0 | 449 | Diseases |

179 | Tomato YLCV Virus general | 1212 | Tomato | 616 | 0 | 0 | 596 | Diseases |

180 | Tomato YLCV Virus serious | 2350 | Tomato | 524 | 0 | 0 | 1826 | Diseases |

181 | Tomato Tomv | 599 | Tomato | 301 | 0 | 0 | 298 | Diseases |

TOTAL | 123,987 | 33,160 | 33,801 | 33,370 | 23,656 |

## References

- Manavalan, R. Automatic identification of diseases in grains crops through computational approaches: A review. Comput. Electron. Agric.
**2020**, 178, 105802. [Google Scholar] [CrossRef] - Kong, J.; Wang, H.; Wang, X.; Jin, X.; Fang, X.; Lin, S. Multi-stream hybrid architecture based on cross-level fusion strategy for fine-grained crop species recognition in precision agriculture. Comput. Electron. Agric.
**2021**, 185, 106134. [Google Scholar] [CrossRef] - Zheng, Y.-Y.; Kong, J.-L.; Jin, X.-B.; Wang, X.-Y.; Su, T.-L.; Zuo, M. Crop Deep: The crop vision dataset for deep-learning-based classification and detection in precision agriculture. Sensors
**2019**, 19, 1058. [Google Scholar] [CrossRef][Green Version] - Marcu, I.M.; Suciu, G.; Balaceanu, C.M.; Banaru, A. IOT based system for smart agriculture. In Proceedings of the 11th International Conference on Electronics, Computers and Artificial Intelligence, Pitesti, Romania, 27–29 June 2019; pp. 1–4. [Google Scholar]
- Jin, X.-B.; Zheng, W.-Z.; Kong, J.-L.; Wang, X.-Y.; Bai, Y.-T.; Su, T.-L.; Lin, S. Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization. Energies
**2021**, 14, 1596. [Google Scholar] [CrossRef] - Ding, F.; Chen, T. Combined parameter and output estimation of dual-rate systems using an auxiliary model. Automatica
**2004**, 40, 1739–1748. [Google Scholar] [CrossRef] - Ding, F.; Chen, T. Parameter estimation of dual-rate stochastic systems by using an output error method. IEEE Trans. Autom. Control
**2005**, 50, 1436–1441. [Google Scholar] [CrossRef] - Ding, F.; Shi, Y.; Chen, T. Auxiliary model-based least-squares identification methods for Hammerstein output-error systems. Syst. Control Lett.
**2007**, 56, 373–380. [Google Scholar] [CrossRef] - Xu, L. Separable multi-innovation Newton iterative modeling algorithm for multi-frequency signals based on the sliding measurement window. Circuits Syst. Signal Process.
**2022**, 41, 805–830. [Google Scholar] [CrossRef] - Xu, L. Separable Newton recursive estimation method through system responses based on dynamically discrete measurements with increasing data length. Int. J. Control Autom. Syst.
**2022**, 20, 432–443. [Google Scholar] [CrossRef] - Zhou, Y.H.; Ding, F. Modeling nonlinear processes using the radial basis function-based state-dependent autoregressive models. IEEE Signal Process. Lett.
**2020**, 27, 1600–1604. [Google Scholar] [CrossRef] - Zhou, Y.H.; Zhang, X. Partially-coupled nonlinear parameter optimization algorithm for a class of multivariate hybrid models. Appl. Math. Comput.
**2022**, 414, 126663. [Google Scholar] [CrossRef] - Zhou, Y.H.; Zhang, X. Hierarchical estimation approach for RBF-AR models with regression weights based on the increasing data length. IEEE Trans. Circuits Syst. II Express Briefs
**2021**, 68, 3597–3601. [Google Scholar] [CrossRef] - Zhang, X. Optimal adaptive filtering algorithm by using the fractional-order derivative. IEEE Signal Process. Lett.
**2022**, 29, 399–403. [Google Scholar] [CrossRef] - Ding, J.; Liu, X.P.; Liu, G. Hierarchical least squares identification for linear SISO systems with dual-rate sampled-data. IEEE Trans. Autom. Control
**2011**, 56, 2677–2683. [Google Scholar] [CrossRef] - Ding, F.; Liu, Y.J.; Bao, B. Gradient based and least squares based iterative estimation algorithms for multi-input multi-output systems. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng.
**2012**, 226, 43–55. [Google Scholar] [CrossRef] - Xu, L.; Chen, F.Y.; Hayat, T. Hierarchical recursive signal modeling for multi-frequency signals based on discrete measured data. Int. J. Adapt. Control Signal Process.
**2021**, 35, 676–693. [Google Scholar] [CrossRef] - Kumar, S.A.; Ilango, P. The impact of wireless sensor network in the field of precision agriculture: A review. Wirel. Pers. Commun.
**2018**, 98, 685–698. [Google Scholar] [CrossRef] - Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; et al. Imagenet large scale visual recognition challenge. Int. J. Comput. Vis.
**2015**, 115, 211–252. [Google Scholar] [CrossRef][Green Version] - Zhuang, P.; Wang, Y.L.; Yu, Q. Learning Attentive pairwise interaction for fine-grained classification. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, New York, NY, USA, 7–12 February 2020; Association for the Advancement of Artificial Intelligence: Menlo Park, CA, USA, 2020; Volume 34, pp. 13130–13137. [Google Scholar]
- Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv
**2014**, arXiv:1409.1556. [Google Scholar] - Jie, H.; Li, S.; Sun, G. Squeeze-and-excitation networks. In Proceedings of the 2018 Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018; pp. 7132–7141. [Google Scholar]
- Gao, H.; Zhuang, L.; Laurens, V.D.; Weinberger, K.Q. Densely connected convolutional networks. In Proceedings of the 2017 Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 4700–4708. [Google Scholar]
- Tan, M.X.; Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA, 9–15 June 2019; pp. 6105–6114. [Google Scholar]
- Wang, D.; Deng, L.M.; Ni, J.G.; Zhu, H.; Han, Z. Recognition Pest by Image-Based Transfer Learning. J. Sci. Food Agric.
**2019**, 99, 4524–4531. [Google Scholar] - Rupali, S.K.; Vibha, V.; Alwin, A. Component-based face recognition under transfer learning for forensic Applications. Inf. Sci.
**2019**, 476, 176–191. [Google Scholar] - Liao, W.X.; He, P.; Hao, J.; Wang, X.-Y.; Yang, R.-L.; An, D.; Cui, L.-G. Automatic identification of breast ultrasound image based on supervised block-based region segmentation algorithm and features combination migration deep learning model. IEEE J. Biomed. Health Inform.
**2020**, 24, 984–993. [Google Scholar] [CrossRef] - Anagnostis, A.; Asiminari, G.; Papageorgiou, E.; Bochtis, D. A convolutional neural networks based method for anthracnose infected walnut tree leaves identification. Appl. Sci.
**2020**, 10, 469. [Google Scholar] [CrossRef][Green Version] - Anagnostis, A.; Tagarakis, A.C.; Asiminari, G.; Papageorgiou, E.; Kateris, D.; Moshou, D.; Bochtis, D. A deep learning approach for anthracnose infected trees classification in walnut. Comput. Electron. Agric.
**2021**, 182, 105998. [Google Scholar] [CrossRef] - He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 770–778. [Google Scholar]
- Ge, W.F.; Lin, X.G.; Yu, Y.Z. Weakly supervised complementary parts models for fine-grained image classification from the bottom up. In Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15–20 June 2019; pp. 3034–3043. [Google Scholar]
- Zheng, Y.-Y.; Kong, J.-L.; Jin, X.-B.; Wang, X.-Y.; Su, T.-L.; Wang, J.-L. Probability fusion decision framework of multiple deep neural networks for fine-grained visual classification. IEEE Access
**2019**, 7, 122740–122757. [Google Scholar] [CrossRef] - Zhen, T.; Kong, J.L.; Yan, L. Hybrid deep-learning framework based on gaussian fusion of multiple spatiotemporal networks for walking gait phase recognition. Complexity
**2020**, 2020, 8672431. [Google Scholar] [CrossRef] - Jin, X.-B.; Zheng, W.-Z.; Kong, J.-L.; Wang, X.-Y.; Zuo, M.; Zhang, Q.-C.; Lin, S. Deep-Learning Temporal Predictor via Bidirectional Self-Attentive Encoder–Decoder Framework for IOT-Based Environmental Sensing in Intelligent Greenhouse. Agriculture
**2021**, 11, 802. [Google Scholar] [CrossRef] - Wang, C.Y.; Liao, H.Y.M.; Wu, Y.H.; Chen, P.-Y.; Hsieh, J.-W.; Yeh, I.-H. CSPNet: A New Backbone that can Enhance Learning Capability of CNN. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA, 14–19 June 2020; pp. 390–391. [Google Scholar]
- Mohanty, S.P.; David, P.H.; Marcel, S. Using deep learning for image-based plant disease detection. Front. Plant Sci.
**2016**, 7, 1419–1426. [Google Scholar] [CrossRef][Green Version] - Alex, K.; Ilya, S.; Geoffrey, E.H. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Processing Syst.
**2012**, 25, 1097–1105. [Google Scholar] - Ferentinos, K.P. Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric.
**2018**, 145, 311–318. [Google Scholar] [CrossRef] - Wu, X.; Zhan, C.; Lai, Y.-K.; Cheng, M.-M.; Yang, J. Ip102: A large-scale benchmark dataset for insect pest recognition. In Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15–20 June 2019; pp. 8787–8796. [Google Scholar]
- Ding, F. Two-stage least squares based iterative estimation algorithm for CARARMA system modelling. Appl. Math. Model.
**2013**, 37, 4798–4808. [Google Scholar] [CrossRef] - Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; Rabinovich, A. Going deeper with convolutions. In Proceedings of the 2015 Computer Vision and Pattern Recognition IEEE, Boston, MA, USA, 7–12 June 2015; pp. 1–9. [Google Scholar]
- Liu, Y.; Ding, F.; Shi, Y. An efficient hierarchical identification method for general dual-rate sampled-data systems. Automatica
**2014**, 50, 962–970. [Google Scholar] [CrossRef] - Picon, A.; Alvarez-Gila, A.; Seitz, M.; Ortiz-Barredo, A.; Echazarra, J.; Johannes, A. Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Comput. Electron. Agric.
**2019**, 161, 280–290. [Google Scholar] [CrossRef] - Lee, Y.; Park, J. Centermask: Real-time anchor-free instance segmentation. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13–19 June 2020; pp. 13906–13915. [Google Scholar]
- Qin, Z.Q.; Zhang, P.Y.; Wu, F.; Li, X. Fcanet: Frequency channel attention networks. In Proceedings of the 2020 IEEE/CVF International Conference on Computer Vision, Seattle, WA, USA, 13–19 June 2020; pp. 783–792. [Google Scholar]
- Zhang, T.; Chang, D.; Ma, Z.; Guo, J. Progressive co-attention network for fine-grained visual classification. In Proceedings of the 2021 International Conference on Visual Communications and Image Processing, Munich, Germany, 5–8 December 2021; pp. 1–5. [Google Scholar]
- Wang, Q.; Wu, B.; Zhu, P.; Li, P.; Zuo, W.; Hu, Q. Supplementary material for “ECA-Net: Efficient channel attention for deep convolutional neural networks”. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Seattle, WA, USA, 13–19 June 2020; pp. 11531–11539. [Google Scholar]
- Kong, S.; Fowlkes, C. Low-rank bilinear pooling for fine-grained classification. In Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition IEEE Computer Society, Honolulu, HI, USA, 21–26 July 2017; pp. 365–374. [Google Scholar]
- Li, P.H.; Xie, J.T.; Wang, Q.L.; Zuo, W. Is Second-order information helpful for large-scale visual recognition? In Proceedings of the 2017 IEEE International Conference on Computer Vision, IEEE, Venice, Italy, 22–29 October 2017; pp. 2070–2078. [Google Scholar]
- Du, R.; Chang, D.; Bhunia, A.K.; Xie, J.; Ma, Z.; Song, Y.-Z.; Guo, J. Fine-grained visual classification via progressive multi-granularity training of jigsaw Patches. In Proceedings of the 2020 European Conference on Computer Vision, online, 23–28 August 2020; pp. 153–168. [Google Scholar]
- Ji, R.; Wen, L.; Zhang, L.; Du, D.; Wu, Y.; Zhao, C.; Liu, X.; Huang, F. Attention convolutional binary neural tree for fine-grained visual categorization. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13–19 June 2020; pp. 10468–10477. [Google Scholar]
- Lin, T.Y.; Aruni, R.; Subhransu, M. Bilinear Cnn models for fine-grained visual recognition. In Proceedings of the 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 7–13 December 2015; pp. 1449–1457. [Google Scholar]
- Zhang, Q.L.; Yang, Y.B. Sa-Net: Shuffle attention for deep convolutional neural networks. In Proceedings of the ICASSP 2021 IEEE International Conference on Acoustics, Speech and Signal Processing, Toronto, ON, Canada, 6–11 June 2021; pp. 2235–2239. [Google Scholar]
- Han, K.; Wang, Y.H.; Tian, Q.; Guo, J.; Xu, C.; Xu, C. Ghostnet: More Features from cheap operations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 14–19 June 2020; pp. 1580–1589. [Google Scholar]
- Zhang, X.Y.; Zhou, X.Y.; Lin, M.X.; Sun, J. Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018; pp. 6848–6856. [Google Scholar]
- Filip, R.; Giorgos, T.; Ondrej, C. Fine-tuning CNN image retrieval with no human annotation. IEEE Trans. Pattern Anal. Mach. Intell.
**2017**, 41, 1655–1668. [Google Scholar] - Xie, S.; Girshick, R.; Dollar, P.; Tu, Z.; He, K. Aggregated residual transformations for deep neural networks. In Proceedings of the 2017 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 1492–1500. [Google Scholar]
- Ding, Y.; Ma, Z.; Wen, S.; Xie, J.; Chang, D.; Si, Z.; Wu, M.; Ling, H. AP-CNN: Weakly supervised attention pyramid convolutional neural network for fi-ne-grained visual classification. IEEE Trans. Image Process.
**2021**, 30, 2826–2836. [Google Scholar] [CrossRef] - Woo, S.Y.; Park, J.; Lee, J.Y.; Kweon, I.S. Cbam: Convolutional Block Attention Module. In Proceedings of the 2018 European Conference on Computer Vision, Munich, Germany, 8–14 September 2018; pp. 3–19. [Google Scholar]
- Wang, Y. Novel data filtering based parameter identification for multiple-input multiple-output systems using the auxiliary model. Automatica
**2016**, 71, 308–313. [Google Scholar] [CrossRef] - Li, P.; Xie, J.; Wang, Q.; Gao, Z. Towards faster training of global covariance pooling networks by iterative matrix square root normalization. In Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018; pp. 947–955. [Google Scholar]
- Kong, J.L.; Yang, C.C.; Wang, J.L.; Wang, X.; Zuo, M.; Jin, X.; Lin, S. Deep-stacking network approach by multisource data mining for hazardous risk identification in IoT-based intelligent food management systems. Comput. Intell. Neurosci.
**2021**, 2021, 1194565. [Google Scholar] [CrossRef] - Cai, W.; Wei, Z. PiiGAN: Generative adversarial networks for pluralistic image inpainting. IEEE Access
**2020**, 8, 48451–48463. [Google Scholar] [CrossRef] - Cai, W.W.; Wei, Z.G. Remote sensing image classification based on a cross-attention mechanism and graph convolution. IEEE Geosci. Remote Sens. Lett.
**2022**, 19, 1–5. [Google Scholar] [CrossRef] - Guo, N.; Gu, K.; Qiao, J.F. Active vision for deep visual learning: A unified pooling framework. IEEE Trans. Ind. Inform.
**2021**, 10, 1109. [Google Scholar] [CrossRef] - Jin, X.B.; Gong, W.T.; Kong, J.L.; Bai, Y.T.; Su, T.L. PFVAE: A planar flow-based variational auto-encoder prediction model for time series data. Mathematics
**2022**, 10, 610. [Google Scholar] [CrossRef] - Jin, X.B.; Gong, W.T.; Kong, J.L.; Bai, Y.T.; Su, T.L. A variational Bayesian deep network with data self-screening layer for massive time-series data forecasting. Entropy
**2022**, 24, 355. [Google Scholar] [CrossRef] - Jin, X.B.; Zhang, J.S.; Kong, J.L.; Su, T.L.; Bai, Y.T. A reversible automatic selection normalization (RASN) deep network for predicting in the smart agriculture system. Agronomy
**2022**, 12, 591. [Google Scholar] [CrossRef] - Shi, Z.; Bai, Y.; Jin, X.; Wang, X.; Su, T.; Kong, J. Deep Prediction Model Based on Dual Decomposition with Entropy and Frequency Statistics for Nonstationary Time Series. Entropy
**2022**, 24, 360. [Google Scholar] [CrossRef] - Xu, L.; Zhu, Q.M. Decomposition strategy-based hierarchical least mean square algorithm for control systems from the impulse responses. Int. J. Syst. Sci.
**2021**, 52, 1806–1821. [Google Scholar] [CrossRef] - Zhang, X.; Xu, L.; Hayat, T. Combined state and parameter estimation for a bilinear state space system with moving average noise. J. Frankl. Inst.
**2018**, 355, 3079–3103. [Google Scholar] [CrossRef] - Pan, J.; Jiang, X.; Ding, W. A filtering based multi-innovation extended stochastic gradient algorithm for multivariable control systems. Int. J. Control Autom. Syst.
**2017**, 15, 1189–1197. [Google Scholar] [CrossRef] - Pan, J.; Ma, H.; Liu, Q.Y. Recursive coupled projection algorithms for multivariable output-error-like systems with coloured noises. IET Signal Process.
**2020**, 14, 455–466. [Google Scholar] [CrossRef] - Ding, F.; Liu, G.; Liu, X.P. Partially coupled stochastic gradient identification methods for non-uniformly sampled systems. IEEE Trans. Autom. Control
**2010**, 55, 1976–1981. [Google Scholar] [CrossRef] - Ding, F.; Shi, Y.; Chen, T. Performance analysis of estimation algorithms of non-stationary ARMA processes. IEEE Trans. Signal Process.
**2006**, 54, 1041–1053. [Google Scholar] [CrossRef] - Zhang, X. Adaptive parameter estimation for a general dynamical system with unknown states. Int. J. Robust Nonlinear Control
**2020**, 30, 1351–1372. [Google Scholar] [CrossRef] - Pan, J.; Li, W.; Zhang, H.P. Control algorithms of magnetic suspension systems based on the improved double exponential reaching law of sliding mode control. Int. J. Control Autom. Syst.
**2018**, 16, 2878–2887. [Google Scholar] [CrossRef] - Ma, H.; Pan, J.; Ding, W. Partially-coupled least squares based iterative parameter estimation for multi-variable output-error-like autoregressive moving average systems. IET Control Theory Appl.
**2019**, 13, 3040–3051. [Google Scholar] [CrossRef] - Ding, F.; Liu, X.P.; Yang, H.Z. Parameter identification and intersample output estimation for dual-rate systems. IEEE Trans. Syst. Man. Cybern. Part A Syst. Hum.
**2008**, 38, 966–975. [Google Scholar] [CrossRef] - Xu, L.; Yang, E.F. Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems. Int. J. Robust Nonlinear Control
**2021**, 31, 148–165. [Google Scholar] [CrossRef] - Zhao, Z.Y.; Zhou, Y.Q.; Wang, X.Y.; Wang, Z.; Bai, Y. Water quality evolution mechanism modeling and health risk assessment based on stochastic hybrid dynamic systems. Expert Syst. Appl.
**2022**, 193, 116404. [Google Scholar] [CrossRef] - Chen, Q.; Zhao, Z.; Wang, X.; Xiong, K.; Shi, C. Microbiological predictive modeling and risk analysis based on the one-step kinetic integrated Wiener process. Innovat. Food Sci. Emerg. Technol.
**2022**, 75, 102912. [Google Scholar] [CrossRef] - Ding, F.; Liu, X.P.; Liu, G. Multiinnovation least squares identification for linear and pseudo-linear regression models. IEEE Trans. Syst. Man Cybern. Part B Cybern.
**2010**, 40, 767–778. [Google Scholar] [CrossRef] - Yao, P.; Wei, Y.; Zhao, Z. Null-space-based modulated reference trajectory generator for multi-robots formation in obstacle environment. ISA Trans.
**2022**, 7, 1–18. [Google Scholar] [CrossRef] - Zhang, X. Hierarchical parameter and state estimation for bilinear systems. Int. J. Syst. Sci.
**2020**, 51, 275–290. [Google Scholar] [CrossRef] - Wang, H.; Fan, H.; Pan, J. Complex dynamics of a four-dimensional circuit system. Int. J. Bifurc. Chaos
**2021**, 31, 2150208. [Google Scholar] [CrossRef]

**Figure 1.**Fine-grained recognition illustration of crop pests and diseases in complex agricultural practices.

**Figure 7.**Characteristic thermograms of different methods: (

**a**) Spodoptera frugiperda, (

**b**) Coccinellidae, (

**c**) Medlar anthracnose, and (

**d**) Pepper scab.

**Figure 9.**Convolutional visualization of different attention methods in the last layer of third CSP-stage.

Method | Backbone | Top-1 Acc (%) | Top-5 Acc (%) | F_{1} | ART (ms) |
---|---|---|---|---|---|

VGG-16 [19] | 74.62 | 88.87 | 0.794 | 39 | |

ResNet-50 [30] | 76.91 | 90.04 | 0.808 | 34 | |

ResNeXt-50 [57] | 77.47 | 90.11 | 0.810 | 33 | |

CSPResNeXt-50 [35] | 77.86 | 90.18 | 0.816 | 31 | |

DenseNet-121 [23] | 76.84 | 90.02 | 0.808 | 36 | |

CSPNet-v2-50 [35] | 80.44 | 91.47 | 0.841 | 39 | |

VGG-19 [19] | 76.16 | 89.65 | 0.801 | 59 | |

ResNet-101 [30] | 79.19 | 90.53 | 0.834 | 48 | |

ResNeXt-101 [57] | 79.81 | 90.76 | 0.838 | 46 | |

CSPResNeXt-101 [35] | 80.12 | 91.17 | 0.841 | 43 | |

DenseNet-201 [23] | 78.57 | 90.51 | 0.831 | 54 | |

CSPNet-v2-101 [35] | 82.05 | 92.77 | 0.857 | 55 | |

B-CNN [40] | VGG-19 [19] | 80.38 | 91.57 | 0.844 | 69 |

iSQ-RTCOV(32k) [58] | ResNet-101 [30] | 83.11 | 93.95 | 0.871 | 61 |

PMG [50] | ResNet-50 | 82.84 | 93.64 | 0.859 | 72 |

API-Net [20] | ResNet-50 | 82.67 | 93.87 | 0.861 | 84 |

Proposed Fe-Net | CSPNet-v2(50) | 84.59 | 94.41 | 0.877 | 57 |

Proposed Fe-Net | CSPNet-v2(101) | 85.29 | 95.07 | 0.887 | 61 |

Method | Top-1 Acc (%) |
---|---|

CSPResNeXt-50 | 77.86 |

CSPResNeXt-50 + channel shuffle | 78.39 (+0.53) |

CSPResNeXt-50 + FEA | 79.81 (+1.95) |

CSPResNeXt-50 + ISQRT-COV | 82.11 (+4.25) |

CSPResNeXt-50 + channel shuffle + FEA + ISQRT-COV (Fe-Net) | 84.59 (+6.73) |

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**MDPI and ACS Style**

Kong, J.; Wang, H.; Yang, C.; Jin, X.; Zuo, M.; Zhang, X.
A Spatial Feature-Enhanced Attention Neural Network with High-Order Pooling Representation for Application in Pest and Disease Recognition. *Agriculture* **2022**, *12*, 500.
https://doi.org/10.3390/agriculture12040500

**AMA Style**

Kong J, Wang H, Yang C, Jin X, Zuo M, Zhang X.
A Spatial Feature-Enhanced Attention Neural Network with High-Order Pooling Representation for Application in Pest and Disease Recognition. *Agriculture*. 2022; 12(4):500.
https://doi.org/10.3390/agriculture12040500

**Chicago/Turabian Style**

Kong, Jianlei, Hongxing Wang, Chengcai Yang, Xuebo Jin, Min Zuo, and Xin Zhang.
2022. "A Spatial Feature-Enhanced Attention Neural Network with High-Order Pooling Representation for Application in Pest and Disease Recognition" *Agriculture* 12, no. 4: 500.
https://doi.org/10.3390/agriculture12040500