Abstract:Helicoverpa armigera (Hübner) regularly requires quick deployment of control practices due to its potential of causing yield loss of processing tomatoes. The monitoring of H. armigera adults using stick traps lured with sex pheromone was correlated to egg densities, larvae, and percentage of damaged fruits, aiming to subsidize control decisions only using the number of males caught in the traps since surveying pest stages or damaged fruits is laborious and costly. Surveys were carried out on 11 tomato fields (… Show more
“…For example, the cotton bollworm Helicoverpa armigera Hübner (Lepidoptera: Noctuidae) is a highly polyphagous moth that can feed on a wide range of major crops like cotton, tomato, maize, chickpea, alfalfa and tobacco. It has been reported to cause at least 25-31.5% losses on tomato [5][6]. Without effective control measures, damage by Η. armigera and other moth pests on cotton can be as high as 67% [7].…”
This study describes the development of an image-based insect trap diverging from the plug-in camera insect trap paradigm. In short, a) it does not require manual annotation of images to learn how to count targeted pests and, b) it self-disposes the captured insects, and therefore is suitable for long-term deployment. The device consists of an imaging sensor integrated with Raspberry Pi microcontroller units with embedded deep learning algorithms that count agricultural pests inside a pheromone-based funnel trap. The device also receives commands from the server which configures its operation while an embedded servomotor can automatically rotate the detached bottom of the bucket to dispose of hydrated insects as they begin to pile up. Therefore, it completely overcomes a major limitation of camera-based insect traps: the inevitable overlap and occlusion caused by the decay and layering of insects during long-term operation, thus extending the autonomous operational capability. We study cases that are underrepresented in literature such as counting in situations of congestion and significant debris using crowd counting algorithms encountered in human surveillance. Finally, we perform comparative analysis of the results from different deep-learning approaches (YOLO7/8, crowd counting, deep learning regression) and we open-source the code and a large database of Lepidopteran plant pests.
“…For example, the cotton bollworm Helicoverpa armigera Hübner (Lepidoptera: Noctuidae) is a highly polyphagous moth that can feed on a wide range of major crops like cotton, tomato, maize, chickpea, alfalfa and tobacco. It has been reported to cause at least 25-31.5% losses on tomato [5][6]. Without effective control measures, damage by Η. armigera and other moth pests on cotton can be as high as 67% [7].…”
This study describes the development of an image-based insect trap diverging from the plug-in camera insect trap paradigm. In short, a) it does not require manual annotation of images to learn how to count targeted pests and, b) it self-disposes the captured insects, and therefore is suitable for long-term deployment. The device consists of an imaging sensor integrated with Raspberry Pi microcontroller units with embedded deep learning algorithms that count agricultural pests inside a pheromone-based funnel trap. The device also receives commands from the server which configures its operation while an embedded servomotor can automatically rotate the detached bottom of the bucket to dispose of hydrated insects as they begin to pile up. Therefore, it completely overcomes a major limitation of camera-based insect traps: the inevitable overlap and occlusion caused by the decay and layering of insects during long-term operation, thus extending the autonomous operational capability. We study cases that are underrepresented in literature such as counting in situations of congestion and significant debris using crowd counting algorithms encountered in human surveillance. Finally, we perform comparative analysis of the results from different deep-learning approaches (YOLO7/8, crowd counting, deep learning regression) and we open-source the code and a large database of Lepidopteran plant pests.
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