Droplet Deposition Distribution Prediction Method for a Six-Rotor Plant Protection UAV Based on Inverse Distance Weighting.

The aim of this work is to establish a method for real-time calculating droplet deposition distribution of a six-rotor plant protection unmanned aerial vehicle (UAV). The numerical simulation of the airflow field was carried out using computational fluid dynamics (CFD). The airflow field distribution was obtained under seven flight speeds, six flight heights, and seven crosswind speeds. The relative error verified the accuracy of the numerical model within 12% between the spatial point wind speed test and the simulated value. The numerical simulation results showed that with the improvement of the UAV flight speed and the crosswind, the relative airflow produces a vortex in the downwash wind field below the UAV and reduces the stability of the downwash wind field. The discrete droplet phase was introduced in the flow field. The ground regions were divided using a small grid of 0.5 m × 0.5 m, and statistical calculations of droplet deposition rates within each grid yielded the distribution of droplets under 294 different parameter combinations. The statistical results show that the relative airflow and crosswind caused droplet convolution, and droplet drift was increased. In the actual operation of the UAV, the flight speed should be well controlled under the condition of low environmental wind to reduce the droplet drift rate and improve the utilization rate of pesticides. Based on the distribution under 294 different parameter combinations, one droplet deposition prediction method was established using inverse distance weighting (IDW). The proposed method lays a foundation for the cumulative calculation of droplet deposition distribution during continuous operation of plant protection UAV. It provides a basis for objectively evaluating the operational quality of plant protection UAVs and optimizing the setting of operation parameters.

A Vegetable Leaf Disease Identification Model Based on Image-Text Cross-Modal Feature Fusion.

In view of the differences in appearance and the complex backgrounds of crop diseases, automatic identification of field diseases is an extremely challenging topic in smart agriculture. To address this challenge, a popular approach is to design a Deep Convolutional Neural Network (DCNN) model that extracts visual disease features in the images and then identifies the diseases based on the extracted features. This approach performs well under simple background conditions, but has low accuracy and poor robustness under complex backgrounds. In this paper, an end-to-end disease identification model composed of a disease-spot region detector and a disease classifier (YOLOv5s + BiCMT) was proposed. Specifically, the YOLOv5s network was used to detect the disease-spot regions so as to provide a regional attention mechanism to facilitate the disease identification task of the classifier. For the classifier, a Bidirectional Cross-Modal Transformer (BiCMT) model combining the image and text modal information was constructed, which utilizes the correlation and complementarity between the features of the two modalities to achieve the fusion and recognition of disease features. Meanwhile, the problem of inconsistent lengths among different modal data sequences was solved. Eventually, the YOLOv5s + BiCMT model achieved the optimal results on a small dataset. Its Accuracy, Precision, Sensitivity, and Specificity reached 99.23, 97.37, 97.54, and 99.54%, respectively. This paper proves that the bidirectional cross-modal feature fusion by combining disease images and texts is an effective method to identify vegetable diseases in field environments.

A Plant Disease Recognition Method Based on Fusion of Images and Graph Structure Text.

The disease image recognition models based on deep learning have achieved relative success under limited and restricted conditions, but such models are generally subjected to the shortcoming of weak robustness. The model accuracy would decrease obviously when recognizing disease images with complex backgrounds under field conditions. Moreover, most of the models based on deep learning only involve characterization learning on visual information in the image form, while the expression of other modal information rather than the image form is often ignored. The present study targeted the main invasive diseases in tomato and cucumber as the research object. Firstly, in response to the problem of weak robustness, a feature decomposition and recombination method was proposed to allow the model to learn image features at different granularities so as to accurately recognize different test images. Secondly, by extracting the disease feature words from the disease text description information composed of continuous vectors and recombining them into the disease graph structure text, the graph convolutional neural network (GCN) was then applied for feature learning. Finally, a vegetable disease recognition model based on the fusion of images and graph structure text was constructed. The results show that the recognition accuracy, precision, sensitivity, and specificity of the proposed model were 97.62, 92.81, 98.54, and 93.57%, respectively. This study improved the model robustness to a certain extent, and provides ideas and references for the research on the fusion method of image information and graph structure information in disease recognition.

Subtropical adaptation of a temperate plant (Brassica oleracea var. italica) utilizes non-vernalization-responsive QTLs.

While many tropical plants have been adapted to temperate cultivation, few temperate plants have been adapted to the tropics. Originating in Western Europe, Brassica oleracea vernalization requires a period of low temperature and BoFLC2 regulates the transition to floral development. In B. oleracea germplasm selected in Taiwan, a non-vernalization pathway involving BoFLC3 rather than BoFLC2 regulates curd induction. In 112 subtropical breeding lines, specific haplotype combinations of BoFLC3 and PAN (involved in floral organ identity and a positional candidate for additional curd induction variation) adapt B. oleracea to high ambient temperature and short daylength. Duplicated genes permitted evolution of alternative pathways for control of flowering in temperate and tropical environments, a principle that might be utilized via natural or engineered approaches in other plants. New insight into regulation of Brassica flowering exemplifies translational agriculture, tapping knowledge of botanical models to improve food security under projected climate change scenarios.