Method for the automatic recognition of cropland headland images based on deep learning

For self-driving agricultural vehicles,the sensing of the headland environment based on image recognition is an important technological aspect.Cropland headland environments are complex and diverse.Traditional image feature extraction methods have many limitations.This study proposed a method of automatic cropland headland image recognition based on deep learning.Based on the characteristics of cropland headland environments and practical application needs,a dataset was constructed containing six categories of annotated cropland headland images and an augmented headland image training set was used to train the compact network MobileNetV2.Under the same experimental conditions,the model prediction accuracy for the first ranked category in all the results(Top-1 accuracy)of the MobileNetV2 network on the validation set was 98.5%.Compared with classic ResNetV2-50,Inception-V3,and backend-compressed Inception-V3,MobileNetV2 has a high accuracy,high recognition speed,and a small memory footprint.To further test the performance of the model,250 images were used for each of the six categories of headland images as the test set for the experiments.The average of the harmonic mean of precision and recall(F1-score)of the MobileNetV2 network for all the categories of headland images reached 97%.The MobileNetV2 network exhibits good robustness and stability.The results of this study indicate that onboard computers on self-driving agricultural vehicles are able to employ the MobileNetV2 network for headland image recognition to meet the application requirements of headland environment sensing.

Did the modern Yellow River form at the Mid-Pleistocene transition?

The incision of the Sanmen Gorge marks the birth of the modern Yellow River,but its timing varies from the late Miocene-early Pliocene to the late Pleistocene(~0.15 Ma),and the suggested forcing mechanisms vary from the uplift of the Tibetan Plateau to global climate change.Here,we report sedimentologic,geochronologic,and provenance data from a drill core near the Sanmen Gorge,the last gorge along the main course of the Yellow River.Our results indicate that typical river channel deposits,with detritus from the Ordos Block in the upstream regions,started to accumulate in the Sanmen Gorge at~1.25 Ma.When integrated with river terrace evidence from the upstream and downstream regions,the results provide robust evidence that the final integration of the modern Yellow River occurred at~1.25 Ma,consistent with the beginning of the Mid-Pleistocene transition(MPT).We propose that the accelerated lowering of eustatic sea level during the MPT may play as important a role as tectonism in driving the birth and evolution of the modern Yellow River.

Convolutional neural network-based automatic image recognition for agricultural machinery

An internet of things-based subsoiling operation monitoring system for agricultural machinery is able to identify the type and operating state of a certain machinery by collecting and recognizing its images;however,it does not meet regulatory requirements due to a large image data volume,heavy workload by artificial selective examination,and low efficiency.In this study,a dataset containing machinery images of over 100 machines was established,which including subsoilers,rotary cultivators,reversible plows,subsoiling and soil-preparation machines,seeders,and non-machinery images.The images were annotated in tensorflow,a deep learning platform from Google.Then,a convolutional neural network(CNN)was designed for targeting actual regulatory demands and image characteristics,which was optimized by reducing overfitting and improving training efficiency.Model training results showed that the recognition rate of this machinery recognition network to the demonstration dataset reached 98.5%.In comparison,the recognition rates of LeNet and AlexNet under the same conditions were 81%and 98.8%,respectively.In terms of model recognition efficiency,it took AlexNet 60 h to complete training and 0.3 s to recognize 1 image,whereas the proposed machinery recognition network took only half that time to complete training and 0.1 s to recognize 1 image.To further verify the practicability of this model,6 types of images,with 200 images in each type,were randomly selected and used for testing;results indicated that the average recognition recall rate of various types of machinery images was 98.8%.In addition,the model was robust to illumination,environmental changes,and small-area occlusion,and thus was competent for intelligent image recognition of subsoiling operation monitoring systems.