Abstract
Crop-type identification is one of the most significant applications of agricultural remote sensing, and it is important for yield estimation prediction and field management. At present, crop identification using datasets from unmanned aerial vehicle (UAV) and satellite platforms have achieved state-of-the-art performances. However, accurate monitoring of small plants, such as the coffee flower, cannot be achieved using datasets from these platforms. With the development of time-lapse image acquisition technology based on ground-based remote sensing, a large number of small-scale plantation datasets with high spatial-temporal resolution are being generated, which can provide great opportunities for small target monitoring of a specific region. The main contribution of this paper is to combine the binarization algorithm based on OTSU and the convolutional neural network (CNN) model to improve coffee flower identification accuracy using the time-lapse images (i.e., digital images). A certain number of positive and negative samples are selected from the original digital images for the network model training. Then, the pretrained network model is initialized using the VGGNet and trained using the constructed training datasets. Based on the well-trained CNN model, the coffee flower is initially extracted, and its boundary information can be further optimized by using the extracted coffee flower result of the binarization algorithm. Based on the digital images with different depression angles and illumination conditions, the performance of the proposed method is investigated by comparison of the performances of support vector machine (SVM) and CNN model. Hence, the experimental results show that the proposed method has the ability to improve coffee flower classification accuracy. The results of the image with a 52.5° angle of depression under soft lighting conditions are the highest, and the corresponding Dice (F1) and intersection over union (IoU) have reached 0.80 and 0.67, respectively.
Highlights
Coffee is one of the top three major beverages worldwide and has important economic value
The coffee flower images with different depression angles and illumination conditions are identified by SPMG, convolutional neural network (CNN), and Bin+CNN models, and the corresponding coffee flower identification results are compared to validate advantages of the proposed method
From the specific identification results (Figure 9(a), Figure S2, and Table 2), it can be seen that the recall rate of the CNN model reached 0.93, which is the highest compared to the other methods
Summary
Coffee is one of the top three major beverages worldwide and has important economic value. Coffee flower monitoring is of paramount importance in flowering regulation, irrigation, yield prediction, and other crop management tasks [1, 2]; the accurate identification of coffee flowers is the key to better managing these tasks. Nowadays, based on various data platforms, a number of datasets have been generated and developed for crop-type identification [3,4,5,6,7,8]. For different identification fields, the datasets can be divided into two main categories: datasets that are based on manual observation [9, 10] and datasets that are based on satellite platform observation [11,12,13]. As the demand for observation grows, there are several disadvantages for plant identification in small-scale plantations using these types of datasets, which are explained as follows
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