Figure 2. Example images from the IP_RicePests datasets, each image corresponds to a different species of rice pest: (a

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Bog'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

Figure 2.
Example images from the IP_RicePests datasets, each image corresponds to a different
species of rice pest: (a) Asiatic rice border; (b) Brown plant hopper; (c) Grain spreader thrips;
(d) Paddy stem maggot; (e) Rice gall midge; (f) Rice leaf caterpillar; (g) Rice leaf hopper; (h) Rice
leaf roller; (i) Rice shell pest; (j) Rice stemfly; (k) Rice water weevil; (l) Small brown plant hopper;
(m) White backed plant hopper; (n) Yellow rice borer.
As can be seen from Figure
2
, the IP_RicePests dataset includes 14 types of pests, in-
cluding Asiatic rice border, brown plant hopper, grain spreader thrips, paddy stem maggot,
rice gall midge, rice leaf caterpillar, rice leaf hopper, rice leaf roller, rice shell pest, rice stem-

Agronomy 2022, 12, 2096
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fly, rice water weevil, small brown plant hopper, white backed plant hopper and yellow
rice borer. The largest category contained 1110 samples (rice leaf roller) and the smallest
category had only 174 samples (grain spreader thrips). Figure
3
shows the distribution of
the number of samples for each pest category in the IP_RicePests dataset, which shows
that the dataset exhibits a natural long-tailed distribution, and clearly demonstrating that
IP_RicePests has a high imbalance rate (IR) [
39
] in most categories. This is mainly due to the
complexity of the rice field environment and other reasons that make it difficult to collect
samples of individual rice pests, such as grain spreader thrips, paddy stem maggot, rice
stemfly and other pests, and the number of samples therefore tends to be low. Unbalanced
data can lead to biases in the classification model learning in classes with relatively more
training samples, and therefore, unbalanced data distribution should not be ignored.
Agronomy 2022, 12, x FOR PEER REVIEW
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(k)
(l)
(m)
(n)

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