Broilers produce abnormal sounds such as cough and snore when they suffer from respiratory diseases. The aim of this research work was to develop a method for broiler abnormal sound detection. The sounds were recorded in a broiler house for one week (24/7). There were 20 thousand white feather broilers reared on the floor in a building. Results showed that the developed recognition algorithm, using wavelet transform Mel frequency cepstrum coefficients (WMFCCs), correlation distance Fisher criterion (CDF), and hidden Markov model (HMM), provided an average accuracy, precision, recall, and F1 of 93.8%, 94.4%, 94.1%, and 94.2%, respectively, for broiler sound samples. The results indicate that sound analysis can be used in broiler respiratory assessment in a commercial broiler farm.

Due to highly cluttered backgrounds and limited visual features, transmission line detection (TLD) in complex and natural outdoor environments is a difficult task. Motivated by image parsing, a TLD algorithm that utilizes a decomposition structure and contextual information is proposed. Considering that cables are generally associated with the presence of pylons, correlations between pylons and cables, as well as correlations among different cable segments, are defined as the contextual information, which is described using a hierarchical model with the structure of an and-or graph. First, pylons in images are detected via an active basis model, which is an appearance model that uses sparse representation. Next, according to the number of pylons, the bottom-up inference in the hierarchical model selects different procedures and corresponding operations to detect cables. Multiple experiments are performed on aerial images captured in practical powerline inspection tasks. The experimental results illustrate that our TLD algorithm can effectively manage images captured under various landforms at different visual distances and from different viewpoints. For highly cluttered environments in particular, our algorithm has obvious advantages in terms of detection performance compared with existing approaches.

The emergence of deep learning-based methods for harvesting and yield estimates, including object detection or image segmentation-based methods, has notably improved performance but has also resulted in large annotation workloads. Considering the difficulty of such annotation, a method for locating fruit is developed in this study using only center-point labeling information. To address point labeling, the weighted Hausdorff distance is chosen as the loss function of the corresponding network, while deep layer aggregation (DLA) is used to contend with the variability in the visible area of the fruit. The performance of our method in terms of both detection and position is not inferior to the method based on Mask-RCNN. Experiments on a public apple dataset are provided to further demonstrate the performance of the proposed method. Specifically, no more than two targets had positioning deviations exceeding five pixels within the field of view.