Investigating the interplay between biotic stress from downy mildew and abiotic stress from nitrogen deficiency is crucial for improving crop management measures and enhancing cucumber production. A greenhouse experiment was conducted using two pathogen treatments—non-infected (B1) and infected with Pseudoperonospora cubensis (B0)—across three nitrogen levels: deficiency (N1: 50%), optimal (N2: 100%), and excess (N3: 150%). The resulting six treatment combinations (B1N1, B1N2/Control, B1N3, B0N1, B0N2, and B0N3) revealed that downy mildew infection and nitrogen application rates significantly influenced key physiological and biochemical parameters (p<0.05). These included sucrose, soluble sugar, hydrogen peroxide (H₂O₂), catalase (CAT), superoxide dismutase (SOD), polyphenol oxidase (PPO), and malondialdehyde (MDA), as well as the fresh and dry weights of the leaves, stems, and roots. Among all groups, the combination of infection and nitrogen deficiency (B0N1) had the most significant impact on biomass accumulation and hormone metabolism. Compared to the B1N2 control, B0N1 led to substantial reductions in sucrose (52.83%), soluble sugar (68.67%), leaf fresh weight (56.67%), leaf dry weight (55.51%), stem fresh weight (52.82%), stem dry weight (57.28%), root fresh weight (32.46%), and root dry weight (54.07%). This study clarifies the interactive physiological responses of cucumbers to combined biotic and abiotic stress. It is of great significance for facilitating the control of downy mildew and the improvement of cucumber yield in sustainable agriculture.

Parathion-methyl, a widely used pesticide in agricultural production, poses significant risks to human immune health when present in excessive amounts in water and soil. Sensitive and rapid detection of parathion-methyl in environmental and agricultural samples is thus critical. Conventional methods like gas chromatography (GC) and liquid chromatography-mass spectrometry (LC-MS) rely on large-scale instrumentation, offering high sensitivity but suffering from limitations including complex sample pretreatment, cumbersome operations, and high costs. These drawbacks underscore the need for efficient, cost-effective, and user-friendly analytical approaches.Colorimetric sensors, enabling naked-eye detection of analytes in biological and environmental samples, have gained traction due to their low cost, simplicity, and rapid response. In such systems, the catalytic activity of artificial enzymes (nanozymes) is pivotal for inducing colorimetric reactions. Nanozymes, which mimic natural enzymes with enhanced stability and tunable catalytic properties, have emerged as promising alternatives. A key challenge remains enhancing peroxidase-mimetic activity while devising innovative detection strategies to enable visual pesticide residue monitoring in agricultural products.Cobalt oxide (Co₃O₄), a nanozyme exhibiting both catalase-like and peroxidase-like activities, benefits from a higher reduction potential of Co(III)/Co(II) (1.92 eV) compared to other metal ions. However, its catalytic efficiency often diminishes due to agglomeration during synthesis, caused by magnetic interactions and van der Waals forces. To address this, graphene aerogels (3D GAs) were integrated as scaffolds. Their robust mechanical strength, electrical conductivity, and three-dimensional (3D) interconnected architecture provide a stable matrix for nanozyme loading and facilitate rapid catalytic reactions.This study introduces a 3D GA/Co₃O₄ nanowire-based visual sensor for parathion-methyl detection. The composite was synthesized via a one-pot hydrothermal method, yielding enhanced peroxidase-mimetic activity toward 3,3',5,5'-tetramethylbenzidine (TMB) substrate compared to pristine Co₃O₄. By leveraging the peroxidase-like activity of 3D GA/Co₃O₄ and the inhibitory effect of organophosphorus pesticides on acetylcholinesterase (AChE), a colorimetric sensor was developed. The system demonstrated a linear response to parathion-methyl within 30–1000 nmol/L, with a detection limit of 10 nmol/L. This work pioneers the use of graphene aerogels as sensitizers to boost peroxidase-mimetic activity and offers a scalable platform for visual pesticide monitoring.

Multi-machine collaboration of agricultural machinery is one of the international frontier and hot research in the field of agricultural equipment. However, the current domestic multi-machine collaborative operation of agricultural machinery is limited to the research of task goal planning and collaborative path optimization in a single production link. In order to achieve the purpose of zero inventory of agricultural materials and precise and efficient production operations, a new technology of agricultural machinery multi-machine collaboration with multi-dimension and full chain was proposed, which takes into account the whole process of agricultural production, as well as agricultural machinery system and external supply chain, storage and transportation chain collaboration. The problems of data collaboration, process collaboration and organization collaboration were analyzed. And the realization conditions of new multi-machine cooperative technology were analyzed. Meanwhile, the zero inventory mode and precise operation mode of agricultural materials under the background of multi-machine cooperation of intelligent agricultural machinery were studied. Then, a precise and efficient agricultural production mode based on data-process-organization collaboration was constructed. The results showed that the multi-machine cooperative technology mode of multi-dimensional and full-chain agricultural machinery could greatly improve the efficiency of agricultural machinery, operation quality, land utilization rate and reduce production cost.

Timely detection and control of airborne disease is important to improve productivity. This study proposed a novel approach that utilizes micro polarization image features and a backpropagation neural network (BPNN) to classify and identify airborne disease spores in a greenhouse setting. Firstly, disease spores were collected in the greenhouse, and their surface morphological parameters were analyzed. Subsequently, the micropolarization imaging system for disease spores was established, and the micropolarization images of airborne disease spores from greenhouse crops were collected. Then the micropolarization images of airborne disease spores were processed, and the image features of polarization degree and polarization angle of disease spores were extracted. Finally, a disease spore classification model based on the BPNN was ultimately developed. The results showed that the texture position of the surface of the three disease spores was inconsistent, and the texture also showed an irregular shape. Texture information was present on the longitudinal and transverse axes, with the longitudinal axis exhibiting more uneven texture information. The polarization-degree images of the three disease spores exhibit variations in their representation within the entirety of the beam information. The disease spore polarization angle image exhibited the maximum levels of contrast and entropy when the Gabor filter’s direction was set to π/15. The recognition accuracy of cucumber downy mildew spores, tomato gray mildew spores, and cucumber powdery mildew spores were 75.00%, 83.33%, and 96.67%, respectively. The average recognition accuracy of disease spores was 86.67% based on BPNN and micropolarization image features. This study can provide a novel method for the detection of plant disease spores in the greenhouse.

This study addresses the challenge posed by the small spore size of tomato gray mold, which hinders its identification and enumeration by conventional techniques. This work presents a novel approach for quantifying spore counts of tomato gray mold using diffraction imaging technology and image processing techniques. To construct a device for acquiring diffraction images of tomato gray mold spores, initially, the hyperspectral data pertaining to the gray mold spores of tomatoes was obtained. The characteristic wavelength of the light source of the diffraction image acquisition device was obtained by smoothing, principal component analysis, and comprehensive coefficient weight calculation. Then, the key parameters of the system were simulated, and the diffraction image acquisition device was built. Finally, tomato gray mold spores were counted based on angular spectrum reconstruction and image processing. The findings indicated that the combined contribution rate of the initial and secondary principal components of the original spectral data obtained from tomato gray mold spore samples amounted to 92.271%. The visible range of 435 nm, 475 nm, and 720 nm can be selected as the light source for tomato gray mold’s spore diffraction imaging system. CMOS image sensor was installed 45 mm below the micropore with a diameter of 100 μm, and the diffraction image obtained by simulation has a clear diffraction fingerprint. The diffraction imaging system can collect diffraction images of disease spores, and the collected diffraction images have clear diffraction fingerprints. The experimental error range was 5.13%-8.57%, and the average error was 6.42%. The error was within a 95% consistency. Therefore, this study can provide a research basis for the classification and recognition of greenhouse disease spores.