No abstract

BACKGROUND Plant pests mainly refers to insects and mites that harm crops and products. There are a wide variety of plant pests, with wide distribution, fast reproduction and large quantity, which directly causes serious losses to crops. Therefore, pest recognition is very important for crops to grow healthily, and this in turn affects crop yields and quality. At present, it is a great challenge to realize accurate and reliable pest identification. RESULTS In this study, we put forward a diagnostic system based on transfer learning for pest detection and recognition. This method is able to train and test ten types of pests and achieves an accuracy of 93.84%. We compared this transfer learning method with human experts and a traditional neural network model. Experimental results show that the performance of the proposed method is comparable to human experts and the traditional neural network. To verify the general adaptability of this model, we used our model to recognize two types of weeds: Sisymbrium sophia and Procumbent Speedwell, and achieved an accuracy of 98.92%. CONCLUSION The proposed method can provide evidence for the control of pests and weeds and the precise spraying of pesticides. Thus, it provides reliable technical support for precision agriculture. © 2019 Society of Chemical Industry

The motion trajectory of sea cucumbers reflects the behavior of sea cucumbers, and the behavior of sea cucumbers reflects the status of the feeding and individual health, which provides the important information for the culture, status detection and early disease warning. Different from the traditional manual observation and sensor-based automatic detection methods, this paper proposes a detection, location and analysis approach of behavior trajectory based on Faster R-CNN for sea cucumbers under the deep learning framework. The designed detection system consists of a RGB camera to collect the sea cucumbers' images and a corresponding sea cucumber identification software. The experimental results show that the proposed approach can accurately detect and locate sea cucumbers. According to the experimental results, the following conclusions are drawn: (1) Sea cucumbers have an adaptation time for the new environment. When sea cucumbers enter a new environment, the adaptation time is about 30 minutes. Sea cucumbers hardly move within 30 minutes and begin to move after about 30 minutes. (2) Sea cucumbers have the negative phototaxis and prefers to move in the shadows. (3) Sea cucumbers have a tendency to the edge. They like to move along the edge of the aquarium. When the sea cucumber is in the middle of the aquarium, the sea cucumber will look for the edge of the aquarium. (4) Sea cucumbers have unidirectional topotaxis. They move along the same direction with the initial motion direction. The proposed approach will be extended to the detection and behavioral analysis of the other marine organisms in the marine ranching. INDEX TERMS Artificial intelligence (AI), animal behavior, deep learning, object detection, faster R-CNN, marine ranching, sea cucumber.

BACKGROUND: DUS (Distinctness, Uniformity and Stability) testing of new varieties is an important method for peanut germplasm evaluation and identification of varieties. In order to verify the feasibility of variety identification for peanut DUS testing based on image processing, 2000 peanut pod images from 20 varieties were obtained by a scanner. Initially, six DUS testing traits were quantified using a mathematical method based on image processing technology, and then, size, shape, color and texture features (total 31) were also extracted. Next, the Fisher algorithm was used as a feature selection method to select 'good' features from the extracted features to expand the DUS testing traits set. Finally, support vector machine (SVM) and K-means algorithm were respectively used as recognition model and clustering method for variety identification and pedigree clustering.RESULTS: By the Fisher selection method, a number of significant candidate features for DUS testing were selected which can be used in the DUS testing further; using the top half of these features (about 18) ordered by Fisher discrimination ability, the recognition rate of SVM model was found to be more than 90%, which was better than unordered features. In addition, a pedigree clustering tree of 20 peanut varieties was built based on the K-means clustering method, which can be used in deeper studies of the genetic relationship of different varieties. CONCLUSION: This article can provide a novel reference method for future DUS testing, peanut varieties identification and study of peanut pedigree.