Real-time noninvasive monitoring of crop water information is an important basis for water-saving irrigation and precise management. Nano-electronic technology has the potential to enable smart plant sensors to communicate with electronic devices and promote the automatic and accurate distribution of water, fertilizer, and medicine to improve crop productivity. In this work, we present a new flexible graphene oxide (GO)-based noninvasive crop water sensor with high sensitivity, fast responsibility and good bio-interface compatibility. The humidity monitoring sensitivity of the sensor reached 7945 Ω/% RH, and the response time was 20.3 s. We first present the correlation monitoring of crop physiological characteristics by using flexible wearable sensors and photosynthesis systems, and have studied the response and synergistic effect of net photosynthetic rate and transpiration rate of maize plants under different light environments. Results show that in situ real-time sensing of plant transpiration was realized, and the internal water transportation within plants could be monitored dynamically. The synergistic effect of net photosynthetic rate and transpiration of maize plants can be jointly tested. This study provides a new technical method to carry out quantitative monitoring of crop water in the entire life cycle and build smart irrigation systems. Moreover, it holds great potential in studying individual plant biology and could provide basic support to carry out precise monitoring of crop physiological information.

In 2013, the government officially approved the construction task of developing high-standard farmland, which had been written into the outline of the “12th Five-Year Plan”, the “13th Five-Year Plan” and the “14th Five-Year Plan”, effectively ensuring the sustainable development of farmland with high and stable yield in China. Moreover, with the rapid progress of urbanization and industrialization, the quality and usage of cultivated land have changed greatly, and the relationship between the economic value, social value and ecological value of land has become increasingly prominent. Whether the development of high-standard farmland, especially the high-standard farmland used for grain production, has achieved the goals of increasing farmers’ income, agricultural output and rural development is not clear. Therefore, it is necessary to evaluate the comprehensive benefits of high-standard farmland development in grain production, so as to scientifically measure the results of the development. From the perspective of economic, social and ecological benefits, this paper establishes an entropy weight evaluation index system and a model to evaluate the level and effectiveness of high-standard farmland development from 2013 to 2020 in China. The results show that the high-standard farmland development project has improved the yield of grain and the basic productivity of cultivated land, effectively increased the yields of land in the project area and promoted the protection and improvement of cultivated land quality, which includes soil quality improvement, soil fertility enhancement, pollution control and soil remediation. The project also helped raise the farmers’ income levels and improved farmers’ agricultural knowledge and skills in the project area. The projects are very beneficial for agricultural production, the farmers’ income and rural development. However, there is still a certain gap between the national average level of improvement and the original goal set in the policy. The average grain yield per mu (Note: 1 mu ≈ 0.0667 ha, similarly hereinafter) was expected to be increased by less than 100 kg (the national average was a 40 kg increase), and the degrees of improvement in economic, social, ecological and comprehensive benefits in different project types were also different. In the future, we suggest that the project should be implemented according to local conditions and the features of each region. We should pay attention to the protection of basic farmland quality and further improve grain output to achieve the goal of stabilizing and increasing production.

As the source of data acquisition, sensors provide basic data support for crop planting decision management and play a foundational role in developing smart planting. Accurate, stable, and deployable on-site sensors make intelligent monitoring of various planting scenarios possible. Recent breakthroughs in plant advanced sensors and the rapid development of intelligent manufacturing and artificial intelligence (AI) have driven sensors towards miniaturization, intelligence, and multi-modality. This review outlines the key technologies in developing new advanced sensors, such as micro-nano technology, flexible electronics technology, and micro-electromechanical system technology. The latest technological frontiers and development trends in sensor principles, fabrication processes, and performance parameters in soil and different segmented crop scenarios are systematically expounded. Finally, future opportunities, challenges, and prospects are discussed. We anticipate that introducing advanced technologies like nanotechnology and AI will rapidly and radically revolutionize the accuracy and intelligence of agricultural sensors, leading to new levels of innovation.

As the habitat areas of Tibetan antelopes usually exhibit poaching and unpredictable risks, combining target recognition and tracking with intelligent Unmanned Aerial Vehicle (UAV) technology is necessary to obtain the real-time location of injured Tibetan antelopes to better protect and rescue them. (1) Background: The most common way to track an object is to detect each frame of it, and it is not necessary to run the object tracker and classifier at the same rate, because the speed for them to change class is slower than objects move. Especially in the edge reasoning scene, UAV real-time monitoring requires to seek a balance between the frame rate, latency, and accuracy. (2) Methods: A backtracking tracker is proposed to recognize Tibetan antelopes which generates motion vectors through stored optical flow, achieving faster target detection. The lightweight You Only Look Once X (YOLOX) is selected as the baseline model to reduce the dependence on hardware configuration and calculation cost while ensuring detection accuracy. Region-of-Interest (ROI)-to-centroid tracking technology is employed to reduce the processing cost of motion interpolation, and the overall processing frame rate is smoothed by pre-calculating the motions of different objects recognized. The On-Line Object Tracking (OLOT) system with adaptive search area selection is adopted to dynamically adjust the frame rate to reduce energy waste. (3) Results: using YOLOX to trace back in the native Darkenet can reduce latency by 3.75 times, and the latency is only 2.82 ms after about 10 frame hops, with the accuracy being higher than YOLOv3. Compared with traditional algorithms, the proposed algorithm can reduce the tracking latency of UAVs by 50%. By running and comparing in the onboard computer, although the proposed tracker is inferior to KCF in FPS, it is significantly higher than other trackers and is obviously superior to KCF in accuracy. (4) Conclusion: A UAV equipped with the proposed tracker effectively reduces reasoning latency in monitoring Tibetan antelopes, achieving high recognition accuracy. Therefore, it is expected to help better protection of Tibetan antelopes.

Nowadays, metal oxide semiconductor gas sensors have diverse applications ranging from human health to smart agriculture with the development of Internet of Things (IoT) technologies. However, high operating temperatures and an unsatisfactory detection capability (high sensitivity, fast response/recovery speed, etc.) hinder their integration into the IoT. Herein, a ternary heterostructure was prepared by decorating WO₃ nanoplates with Au and SnO₂ nanoparticles through a facial photochemical deposition method. This was employed as a sensing material for 3-hydroxy-2-butanone (3H-2B), a biomarker of Listeria monocytogenes. These Au/SnO₂–WO₃ nanoplate-based sensors exhibited an excellent response (R_a/R_g = 662) to 25 ppm 3H-2B, which was 24 times higher than that of pure WO₃ nanoplates at 140 °C. Moreover, the 3H-2B sensor showed an ultrafast response and recovery speed to 25 ppm 3H-2B as well as high selectivity. These excellent sensing performances could be attributed to the rich Au/SnO₂–WO₃ active interfaces and the excellent transport of carriers in nanoplates. Furthermore, a wireless portable gas sensor equipped with the Au/SnO₂–WO₃ nanoplates was assembled, which was tested using 3H-2B with known concentrations to study the possibilities of real-time gas monitoring in food quality and safety.

Purple membranes (PM) of the bacteria Halobacterium salinarum are a unique natural membrane where bacteriorhodopsin (BR) can convert photon energy and pump protons. Elucidating the electronic properties of biomembranes is critical for revealing biological mechanisms and developing new devices. We report here the electric properties of PMs studied by using multi-functional electric force microscopy (EFM) at the nanoscale. The topography, surface potential, and dielectric capacity of PMs were imaged and quantitatively measured in parallel. Two orientations of PMs were identified by EFM because of its high resolution in differentiating electrical characteristics. The extracellular (EC) sides were more negative than the cytoplasmic (CP) side by 8 mV. The direction of potential difference may facilitate movement of protons across the membrane and thus play important roles in proton pumping. Unlike the side-dependent surface potentials observed in PM, the EFM capacitive response was independent of the side and was measured to be at a dC/dz value of ~5.25 nF/m. Furthermore, by modification of PM with de novo peptides based on peptide-protein interaction, directional oriented PM assembly on silicon substrate was obtained for technical devices. This work develops a new method for studying membrane nanoelectronics and exploring the bioelectric application at the nanoscale.