Deep learning application in diverse fields with plant weed detection as a case study

Abstract

Machine learning applications have gained popularity over the years as more advanced algorithms like the deep learning (DL) algorithm are being employed in signal identification, classification and detection of cracks or faults in structures. The DL algorithm has broader applications compared to other machine learning systems and it is a creative algorithm capable of processing data, creating pattern, interpreting information due to its high level of accuracy in pattern recognition under stochastic conditions. This research gives an exposition of DL in diverse areas of operations with a focus on plant weed detection which is inspired by the need to treat a specific class of weed with a particular herbicide. A Convolutional Neural Network (CNN) model was trained through transfer learning on a pre-trained ResNet50 model and the performance was evaluated using a random forest (RF) classifier, the trained model was deployed on a raspberry pi for prediction of the test data. Training accuracies of 99% and 93% were obtained for the CNN and RF classifier respectively. Some recommendations have been proffered to improve inference time such as the use of better embedded systems such as the Nvidia Jetson TX2, synchronizing DL hardware accelerators with appropriate optimization techniques. A prospect of this work would be to incorporate an embedded system, deployed with DL algorithms, on an unmanned aerial vehicle or ground vehicle. Overall, it is revealed from this study that DL is highly efficient in every sector and can improve the accuracy on automatic detection of systems in especially in this era of Industry 4.0.