Accurate and timely monitoring of pests is an effective way to minimize the negative effects of pests in agriculture. Since deep learning-based methods have achieved good performance in object detection, they have been successfully applied for pest detection and monitoring. However, the current pest detection methods fail to balance the relationship between computational cost and model accuracy. Therefore, this paper proposes a lightweight, locality-aware faster R-CNN (LLA-RCNN) method for effective pest detection and real-time monitoring. The proposed model uses MobileNetV3 to replace the original backbone, reduce the computational complexity, and compress the size of the model to speed up pest detection. The coordinate attention (CA) blocks are utilized to enhance the locality information for highlighting the objects under complex backgrounds. Furthermore, the generalized intersection over union (GIoU) loss function and region of interest align (RoI Align) technology are used to improve pest detection accuracy. The experimental results on different types of datasets validate that the proposed model not only significantly reduces the number of parameters and floating-point operations (FLOPs), but also achieves better performance than some popular pest detection methods. This demonstrates strong generalization capabilities and provides a feasible method for pest detection on resource-constrained devices.

Rice has an important position in China as well as in the world. With the wide application of rice hybridization technology, the problem of mixing between individual varieties has become more and more prominent, so the variety identification of rice is important for the agricultural production, the phenotype collection, and the scientific breeding. Traditional identification methods are highly subjective and time-consuming. To address this issue, we propose a novel locality preserved selective projection learning (LPSPL) method for non-destructive rice variety identification based on leaf hyperspectral characteristics. The proposed LPSPL method can select the most discriminative spectral features from the leaf hyperspectral characteristics of rice, which is helpful to distinguish different rice varieties. In the experiments, support vector machine (SVM) is adopted to conduct the rice variety identification based on the selected spectral features. The experimental results show that the proposed method here achieves higher identification rates, 96% for the early rice and 98% for the late rice, respectively, which are superior to some state-of-the-art methods.

Plants NADP-malic enzymes (NADP-MEs) act as a class of oxidative decarboxylase to mediate malic acid metabolism in organisms. Despite NADP-MEs have been demonstrated to play pivotal roles in regulating diverse biological processes, the role of NADP-MEs involving in plant growth and development remains rarely known. Here, we characterized the function of rice cytosolic OsNADP-ME2 in regulating plant height. The results showed that RNAi silencing and knock-out of OsNADP-ME2 in rice results in a dwarf plant structure, associating with significant expression inhibition of genes involving in phytohormone Gibberellin (GA) biosynthesis and signaling transduction, but with up-regulation for the expression of GA signaling suppressor SLR1. The accumulation of major bioactive GA1, GA4 and GA7 are evidently altered in RNAi lines, and exogenous GA treatment compromises the dwarf phenotype of OsNADP-ME2 RNAi lines. RNAi silencing of OsNADP-ME2 also causes the reduction of NADP-ME activity associating with decreased production of pyruvate. Thus, our data revealed a novel function of plant NADP-MEs in modulation of rice plant height through regulating bioactive GAs accumulation and GA signaling, and provided a valuable gene resource for rice plant architecture improvement.