Traditional pest image detection and recognition methods have low accuracy and weak model generalization capabilities. In natural field conditions, pest images face challenges such as inter-species similarity, varying scales, posture changes, illumination effects, and crop occlusion. Currently, pest identification relies heavily on manual inspection, which is time-consuming, labor-intensive, and susceptible to error. Accurate and timely detection of pests is crucial for the effective control of agricultural production. Insect pests are a significant threat to the yield and quality of crops. It also achieves great performance on public datasets, such as IP102, with a great reduction in the number of parameters. Our proposal achieves 91.9% mAP0.5 on the Teddy Cup pest dataset, which outperforms the Yolov5s model by nearly 8% in mAP0.5. Comparative experiments prove the effectiveness of our approach. By applying a ConvNext module based on the vision transformer (ViT), the proposed method achieves effective feature extraction while keeping a lightweight network. Specifically, we tackle the problem of feature extraction in small sample learning with the proposed CAC3 module, which is built in a stacking residual structure based on the standard BottleNeck module. In this paper, we explore and study the improvement methods of convolutional neural network (CNN) models on the Teddy Cup pest dataset and further propose a lightweight and effective agricultural pest detection method for small target pests, named Yolo-Pest, for the pest detection task in agriculture. An accurate and timely pest detection algorithm is essential for effective pest control however, the existing approach suffers from a sharp performance drop when it comes to the pest detection task due to the lack of learning samples and models for small pest detection. /* Identify VID mode and step selection.Insect pests have always been one of the main hazards affecting crop yield and quality in traditional agriculture. struct pmbus_driver_info *info, u32 reg, int page, mp2971_identify_vid(struct i2c_client *client, struct mp2971_data *data, if (info->phases > data->max_phase_rail1) When rail 1’s phase count is configured as 0, rail * Rail 1 can be set to 8 phases, while rail 2 can only be set to 4 * to different phase count applications for rail 1 and rail 2. * The device provides a total of 8 PWM pins, and can be configured ret = i2c_smbus_write_byte_data(client, PMBUS_PAGE, 2) static int mp2971_identify_multiphase(struct i2c_client *client, Int phase, int -365,6 493,63 mp2975_set_phase_rail2(struct pmbus_driver_info *info, int num_phases) Static int mp2975_read_word_data(struct i2c_client *client, int page, ret = mp2971_vid2direct(data, page, ret) ret = mp2971_linear2direct(data, page, ret) ret = mp2975_read_word_helper(client, page, phase, reg, ret = mp2975_read_word_helper(client, page, phase, struct mp2971_data *data = to_mp2971_data(info) const struct pmbus_driver_info *info = pmbus_get_driver_info(client) mp2971_read_word_data(struct i2c_client *client, int page, mp2971_vid2direct(struct mp2971_data *data, int page, int val) /* LINEAR16 does not support negative voltages */ mp2971_linear2direct(struct mp2971_data *data, int page, int val) Static int mp2975_read_byte_data(struct i2c_client *client, int page, int -95,6 121,40 mp2975_read_word_helper(struct i2c_client *client, int page, int phase, u8 reg, #define to_mp2975_data(x) container_of(x, struct mp2975_data, info) #define to_mp2971_data(x) container_of(x, struct mp2971_data, info) static const struct i2c_device_id mp2975_id PMBUS_HAVE_IOUT | PMBUS_HAVE_STATUS_IOUT | \ #define MP2975_RAIL2_FUNC (PMBUS_HAVE_VOUT | PMBUS_HAVE_STATUS_VOUT | \ PMBUS_HAVE_STATUS_IOUT | PMBUS_HAVE_POUT) (PMBUS_HAVE_VOUT | PMBUS_HAVE_STATUS_VOUT | PMBUS_HAVE_IOUT | \ To: linux-kernel, linux, linux-hwmon, jdelvare, openbmc, joel Pmbus: Add mp2971/mp2973 support in mp2975 LKML Archive on help / color / mirror / Atom feed * pmbus: Add mp2971/mp2973 support in mp2975 10:57 Wang Xiaohua 0 siblings, 0 replies 6 messages in thread
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