Using Deep Learning Methods to Evaluate the Quality of Cereal Sowing

One of the methods of assessing the performance of seed drills may be to compare the performance with the crop population growth. Pixels of crop emergence zone appear to have similar characteristics concerning image parameter variations between soil and crop. The use of deep learning methods based on convolution neural networks to map regions of interest in the image seems appropriate. In this regard, a total of 2720 images of early-growth cereals were obtained from a field. 212 images with different backgrounds were selected and annotated to feed and train a neural network model. Raw images were defined as inputs and maps of manually marked growth points as network outputs. In order to calculate the network cost, the predicted output of the network was compared with the pre-marked pixel map. Prediction errors were then back-propagated and the network parameters updated. Examination of the initial network output showed that the trained network had responded incorrectly to plant tips, weeds and plant remains as plant growth points. To overcome these errors and improve network performance, a penalty function was defined for the mistaken predicted points. The network was trained with three penalty rates and evaluated with nine Soft Max thresholds. According to the network output, images were arranged in terms of plant density. In order to evaluate the model in different ranges, images from each particular range were selected at random. These images were fed to the model and their outputs compared with the truth. For the ranges where approximately 94% of the total field images existed, the average harmonic accuracy of the precision index and the recall index was estimated to be over 80%, indicating good model performance. The results showed that the model can provide acceptable feedback on sowing performance and improve farm management and efficiency in the next steps.