• Korean journal of applied entomology
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• v.48 no.2
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• pp.123-158
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• 2009

An attempt was made to stimulate future research by providing exemplary information, which would integrate published knowledge to solve specific pest problem caused by resistance. This review was directed to find a way for delaying resistance development with consideration of chemical(s) nature, of mixture, rotation, or mosaics, and of insecticide(s) compatible with the biological agents in integrated pest management (IPM). The application frequency, related to the resistance development, was influenced by insecticide activity from potentiation, residual period, and the vulnerability to resistance development of chemical, with secondary pest. Chemical affected feeding, locomotion, flight, mating, and predator avoidance. Insecticides with negative cross-resistance by the difference of target sites and mode of action would be adapted to mixture, rotation and mosaic. Mixtures for delaying resistance depend on each component killing very high percentage of the insects, considering allele dominance, cross-resistance, and immigration and fitness disadvantage. Potential disadvantages associated with mixtures include disruption of biological control, resistance in secondary pests, selecting very resistant population, and extending cross-resistance range. The rotation would use insecticides in high and low doses, or with different metabolic mechanisms. Mosaic apply insecticides to the different sectors of a grid for highly mobile insects, spray unrelated insecticides to sedentary aphids in different areas, or mix plots of insecticide-treated and untreated rows. On the evolution of pest resistance, selectivity and resistance of parasitoids and predator decreased the number of generations in which pesticide treatment is required and they could be complementary to refuges from pesticides To enhance the viability of parasitoids, the terms on the insecticides selectivity and factors affecting to the selectivity in field were examined. For establishment of resistant parasitoid, migration, survivorship, refuge, alternative pesticides were considered. To use parasitoids under the pressure of pesticides, resistant or tolerant parasitoids were tested, collected, and/or selected. A parasitoid parasitized more successfully in the susceptible host than the resistant. Factors affecting to selective toxicity of predator are mixing mineral oil, application method, insecticide contaminated prey, trait of individual insecticide, sub-lethal doses, and the developmental stage of predators. To improve the predator/prey ratio in field, application time, method, and formulation of pesticide, reducing dose rate, using mulches and weeds, multicropping and managing of surroundings are suggested. Plant resistance, predator activity, selective insect growth regulator, and alternative prey positively contributed to the increase of the ratio. Using selective insecticides or insecticide resistant predator controlled its phytophagous prey mites, kept them below an economic level, increased yield, and reduced the spray number and fruits damaged.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1757-1766
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• 2021

The green coverage ratio is the ratio of the land area to green coverage area, and it is used as a practical urban greening index. The green coverage ratio is calculated based on the land cover map, but low spatial resolution and inconsistent production cycle of land cover map make it difficult to calculate the correct green coverage area and analyze the precise green coverage. Therefore, this study proposes a new method to calculate green coverage area using aerial images and deep neural networks. Green coverage ratio can be quickly calculated using manned aerial images acquired by local governments, but precise analysis is difficult because components of image such as acquisition date, resolution, and sensors cannot be selected and modified. This limitation can be supplemented by using an unmanned aerial vehicle that can mount various sensors and acquire high-resolution images due to low-altitude flight. In this study, we proposed a method to calculate green coverage ratio from manned or unmanned aerial images, and experimentally verified the proposed method. Aerial images enable precise analysis by high resolution and relatively constant cycles, and deep learning can automatically detect green coverage area in aerial images. Local governments acquire manned aerial images for various purposes every year and we can utilize them to calculate green coverage ratio quickly. However, acquired manned aerial images may be difficult to accurately analyze because details such as acquisition date, resolution, and sensors cannot be selected. These limitations can be supplemented by using unmanned aerial vehicles that can mount various sensors and acquire high-resolution images due to low-altitude flight. Accordingly, the green coverage ratio was calculated from the two aerial images, and as a result, it could be calculated with high accuracy from all green types. However, the green coverage ratio calculated from manned aerial images had limitations in complex environments. The unmanned aerial images used to compensate for this were able to calculate a high accuracy of green coverage ratio even in complex environments, and more precise green area detection was possible through additional band images. In the future, it is expected that the rust rate can be calculated effectively by using the newly acquired unmanned aerial imagery supplementary to the existing manned aerial imagery.