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Classification Method of Significant Rice Pests Based onDeep LearningBog'liq Classification Method of Significant Rice Pests Ba Физика-курси-1-қисм.-Кодиров-О, ТДИУ, HSyflfAupzrtVxmeX2kSjtbqHjgjAGFqyWgWY1gY, Введение в физику ускорителей заряженных частиц by Иссинский И Б, O\'lchash usullari va vositalari. MamajonovA.A. Sattorov M.O., mustaqil ish 12212121212, Futbol maydonlari va o`lchamlari, TEST argos — копия, «ТЕХНОЛОГИК ЖАРАЁНЛАРНИ АВТОМАТЛАШТИРИШ ВА МОДЕЛЛАШТИРИШ», sinfdan-tashqari-tarbiyaviy-ishlarni-tashkil-qilishning-ilmiy-va-nazariy-asoslari
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Figure 1. Images showing some of the poorer samples in the IP102 dataset.
Overall, the number of rice pest samples in IP_RicePests and IP102 remained largely
consistent (as shown in Table 1).
Table 1. Comparison between the number of IP102 and IP_RicePests.
Categories
IP102
IP_RicePests
Asiatic rice borer
1073
1000
Brown plant hopper
834
800
Grain spreader thrips
173
174
Paddy stem maggot
241
250
Rice gall midge
506
520
Rice leaf caterpillar
487
500
Rice leaf hopper
404
411
Rice leaf roller
1115
1110
Rice shell pest
409
401
Rice stemfly
369
370
Rice water weevil
856
860
Small brown plant hopper
553
556
White backed plant hopper
893
792
Yellow rice borer
504
504
Total Quantity
8417
8248
The IP_RicePests dataset included 8248 images covering 14 rice pest species (some
images are shown in Figure 2). To obtain more reliable test results on IP_RicePests, there
should be enough samples for each category in the test set. Therefore, we divided the
dataset into training, validation, and test sets at an approximate ratio of 6:2:2. Specifically,
IP_RicePests used for the classification task was divided into 4950 images for training,
1649 images for validation and 1649 images for testing.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
Figure 1.
Images showing some of the poorer samples in the IP102 dataset.
Overall, the number of rice pest samples in IP_RicePests and IP102 remained largely
consistent (as shown in Table
1
).
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