Powering and communicating with wearable devices on bio-interfaces is challenging due to strict weight, size, and resource constraints. This study presents a sunflower-like plant-wearable sensing device that harnesses solar energy, achieving complete energy self-sustainability for long-term monitoring of plant sap flow, a crucial indicator of plant health. It features foldable solar panels along with all essential flexible electronic components, resulting in a compact system that is lightweight enough for small plants. To tackle the low-energy density of solar power, we developed an ultralow-energy light communication mechanism inspired by fireflies. Together with unmanned aerial vehicles and deep learning algorithms, this approach enables efficient data retrieval from multiple devices across large agricultural fields. With its simple deployment, it shows great potential as a low-cost plant phenotyping tool. We believe our energy and communication solution for wearable devices can be extended to similar resource-limited and challenging scenarios, leading to exciting applications.

Poppy is a special medicinal plant. Its cultivation requires legal approval and strict supervision. Unauthorized cultivation of opium poppy is forbidden. Low-altitude inspection of poppy illegal cultivation through unmanned aerial vehicle is featured with the advantages of time-saving and high efficiency. However, a large amount of inspection image data collected need to be manually screened and analyzed. This process not only consumes a lot of manpower and material resources, but is also subjected to omissions and errors. In response to such a problem, this paper proposed an inspection method by adding a larger-scale detection box on the basis of the original YOLOv3 algorithm to improve the accuracy of small target detection. Specifically, ResNeXt group convolution was utilized to reduce the number of model parameters, and an ASPP module was added before the small-scale detection box to improve the model’s ability to extract local features and obtain contextual information. The test results on a self-created dataset showed that: the mAP (mean average precision) indicator of the Global Multiscale-YOLOv3 model was 0.44% higher than that of the YOLOv3 (MobileNet) algorithm; the total number of parameters of the proposed model was only 13.75% of that of the original YOLOv3 model and 35.04% of that of the lightweight network YOLOv3 (MobileNet). Overall, the Global Multiscale-YOLOv3 model had a reduced number of parameters and increased recognition accuracy. It provides technical support for the rapid and accurate image processing in low-altitude remote sensing poppy inspection.

Papaver somniferum (opium poppy) is not only a source of raw material for the production of medical narcotic analgesics but also the major raw material for certain psychotropic drugs. Therefore, it is stipulated by law that the cultivation of Papaver somniferum must be authorized by the government under stringent supervision. In certain areas, unauthorized and illicit Papaver somniferum cultivation on private-owned lands occurs from time to time. These illegal Papaver somniferum cultivation sites are dispersedly-distributed and highly-concealed, therefore becoming a tough problem for government supervision. The low-altitude inspection of Papaver somniferum cultivation by unmanned aerial vehicles has the advantages of high efficiency and time saving, but the large amount of image data collected needs to be manually screened, which not only consumes a lot of manpower and material resources but also easily causes omissions. In response to the above problems, this paper proposed a two-stage (target detection and image classification) method for the detection of Papaver somniferum cultivation sites. In the first stage, the YOLOv5s algorithm was used to detect Papaver somniferum images for the purpose of identifying all the suspicious Papaver somniferum images from the original data. In the second stage, the DenseNet121 network was used to classify the detection results from the first stage, so as to exclude the targets other than Papaver somniferum and retain the images containing Papaver somniferum only. For the first stage, YOLOv5s achieved the best overall performance among mainstream target detection models, with a Precision of 97.7%, Recall of 94.9%, and mAP of 97.4%. For the second stage, DenseNet121 with pre-training achieved the best overall performance, with a classification accuracy of 97.33% and a Precision of 95.81%. The experimental comparison results between the one-stage method and the two-stage method suggest that the Recall of the two methods remained the same, but the two-stage method reduced the number of falsely detected images by 73.88%, which greatly reduces the workload for subsequent manual screening of remote sensing Papaver somniferum images. The achievement of this paper provides an effective technical means to solve the problem in the supervision of illicit Papaver somniferum cultivation.

This study designs a spectrum data collection device and system based on the Internet of Things technology, aiming to solve the tedious process of chlorophyll collection and provide a more convenient and accurate method for predicting chlorophyll content. The device has the advantages of integrated design, portability, ease of operation, low power consumption, low cost, and low maintenance requirements, making it suitable for outdoor spectrum data collection and analysis in fields such as agriculture, environment, and geology. The core processor of the device uses the ESP8266-12F microcontroller to collect spectrum data by communicating with the spectrum sensor. The spectrum sensor used is the AS7341 model, but its limited number of spectral acquisition channels and low resolution may limit the exploration and analysis of spectral data. To verify the performance of the device and system, this experiment collected spectral data of Hami melon leaf samples and combined it with a chlorophyll meter for related measurements and analysis. In the experiment, twelve regression algorithms were tested, including linear regression, decision tree, and support vector regression. The results showed that in the original spectral data, the ETR method had the best prediction effect at a wavelength of 515 nm. In the training set, RMSE_c was 0.3429, and R_c² was 0.9905. In the prediction set, RMSE_p was 1.5670, and R_p² was 0.8035. In addition, eight preprocessing methods were used to denoise the original data, but the improvement in prediction accuracy was not significant. To further improve the accuracy of data analysis, principal component analysis and isolation forest algorithm were used to detect and remove outliers in the spectral data. After removing the outliers, the RFR model performed best in predicting all wavelength combinations of denoised spectral data using PBOR. In the training set, RMSE_c was 0.8721, and R_c² was 0.9429. In the prediction set, RMSE_p was 1.1810, and R_p² was 0.8683.

Sap flow serves as the primary carrier for water, nutrients, and signaling molecules, playing a crucial role in fruit development by delivering these essential constituents to the fruit. While the efflux of sap from fruit to other organs (termed reverse sap flow) has been observed in plants, its underlying mechanisms remain unclear due to a lack of effective methodologies for comprehensive studies. Here, we pioneered the integration of real-time sap flow measurements from novel plant-wearable sensors with synchronized environmental monitoring, establishing a multimodal data framework to systematically decode the endogenous causes and exogenous triggers of reverse sap flow in watermelon plants. Our experimental results reveal that plant water supply–consumption imbalance is the core endogenous cause of reverse sap flow, which is induced by two external triggers in the natural environment: rapid light intensity surges and soil drought. Furthermore, a long-term drought stress experiment illustrates that reverse sap flow from the fruit enhances the drought resistance of plants by adjusting water redistribution within the whole plant. This study challenges the unitary view of fruit solely as a “sink” in the traditional source–sink theory, further refines the understanding of the source–sink paradigm, and provides a novel mechanism and insight for plant drought tolerance strategies.