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Abstract. The seeding process is an important part of agricultural production. The miss-seeding in the process will cause the reduction of corn. The system of monitoring of precise seeding of maize based on images can monitor the seeding process in real time, detect miss-seeding and count the number of seeds.

During the operation of precise seeding, a high-speed camera installed under the seeding device collected the images of the process of seeds falling in real time and transferred the collected images to the embedded detection system based on a STM32 microcontroller. The system used the algorithm of Artificial Neural Network to analyze the images and detect whether the seeds are correctly sowed. Convolutional neural network was used as the input layer to reduce the data and extract useful features to form the feature map. Moreover, a fully connected network was used in the middle layer to combine and summarize the features from the former layer. SoftMax classifier was used as the output layer to give the prediction results, i.e., whether seeds were correctly sowed. The training of the model of the neural network was completed in a high-performance workstation. The mature network was transplanted to the embedded control system of the microcontroller for real-time detection of seeds.

The test indicated that the whole system run smoothly and accurately during monitoring. It is convenient to install and can be widely used in corn precision seeders and intelligent agricultural production.