An intelligent system for crop identification and classification from UAV images using conjugated dense convolutional neural network

Crop identification and classification is an important aspect for modern agricultural sector. With development of unmanned aerial vehicle (UAV) systems, crop identification from RGB images is experiencing a paradigm shift from conventional image processing techniques to deep learning strategies because of successful breakthrough in convolutional neural networks (CNNs). UAV images are quite trustworthy to identify different crops due to its higher spatial resolution. For precision agriculture crop identification is the primal criteria. Identifying a specific type of crop in a land is essential for performing proper farming and that also helps to estimate the net yield production of a particular crop. Previous works are limited to identify a single crop from the RGB images captured by UAVs and have not explored the chance of multi-crop classification by implementing deep learning techniques. Multi crop identification tool is highly needed as designing separate tool for each type of crop is a cumbersome job, but if a tool can successfully differentiate multiple crops then that will be helpful for the agro experts. In contrast with the previous existing techniques, this article elucidates a new conjugated dense CNN (CD-CNN) architecture with a new activation function named SL-ReLU for intelligent classification of multiple crops from RGB images captured by UAV. CD-CNN integrates data fusion and feature map extraction in conjunction with classification process. Initially a dense block architecture is proposed with a new activation function, called SL-ReLU, associated with the convolution operation to mitigate the chance of unbounded convolved output and gradient explosion. Dense block architecture concatenates all the previous layer features for determining the new features. This reduces the chance of losing important features due to deepening of the CNN module. Later, two dense blocks are conjugated with the help of a conversion block for obtaining better performance. Unlike traditional CNN, CD-CNN omits the use of fully connected layer and that reduces the chance of feature loss due to random weight initialization. The proposed CD-CNN achieves a strong distinguishing capability from several classes of crops. Raw UAV images of five different crops are captured from different parts of India and then small candidate crop regions are extracted from the raw images with the help of Arc GIS 10.3.1 software and then the candidate regions are fed to CD-CNN for proper training purpose. Experimental results show that the proposed module can achieve an accuracy of 96.2% for the concerned data. Further, superiority of the proposed network is established after comparing with other machine learning techniques viz. RF-200 and SVM, and standard CNN architectures viz. AlexNet, VGG-16, VGG-19 and ResNet-50.