Deep learning for proximal soil sensor development towards smart irrigation

Abstract

Excessive agricultural water consumption threatens safe access of billions of people to potable water. Smart irrigation systems offer more efficient water use in irrigated agriculture. Determining irrigation requirements of various soil texture classes in production fields requires more sophisticated sensing technologies such as deep learning. This study has proposed a proximal sensing system by means of using a color camera towards smart irrigation based on computer vision and deep learning to identify water requirements of three soil texture classes under different illumination conditions. An imaging station was composed to reduce the workload in obtaining training images required for training deep convolutional neural network models. Five deep learning architectures were employed to identify texture-water classes: AlexNet, GoogleNet, ResNet, VGG16, and SqueezeNet. Those models were experimented with and investigated to determine the best models in terms of detection performance and speed. By using cross-validation rules, approximately 12,214 images were studied individually for the purpose of training and testing. The AlexNet model outperformed the other deep learning models with an F1 score of 0.9973 in identifying twelve soil texture-water classes. GoogleNet and ResNet displayed the fastest detection speeds with an average processing time of 16.92 ms. The findings obtained from this study have indicated that deep learning bears a great potential in determining irrigation requirements of production fields under varying conditions