The early identification of rotten potatoes is one of the most important challenges in a storage facility because of the inconspicuous symptoms of rot, the high density of storage, and environmental factors (such as temperature, humidity, and ambient gases). An electronic nose system based on an ensemble convolutional neural network (ECNN, a powerful feature extraction method) was developed to detect potatoes with different degrees of rot. Three types of potatoes were detected: normal samples, slightly rotten samples, and totally rotten samples. A feature discretization method was proposed to optimize the impact of ambient gases on electronic nose signals by eliminating redundant information from the features. The ECNN based on original features presented good results for the prediction of rotten potatoes in both laboratory and storage environments, and the accuracy of the prediction results was 94.70% and 90.76%, respectively. Moreover, the application of the feature discretization method significantly improved the prediction results, and the accuracy of prediction results improved by 1.59% and 3.73%, respectively. Above all, the electronic nose system performed well in the identification of three types of potatoes by using the ECNN, and the proposed feature discretization method was helpful in reducing the interference of ambient gases.

Anthocyanins are important secondary metabolites in fruits, and anthocyanin accumulation in the flesh of peach exhibits a spatial pattern, but the relevant mechanism is still unknown. In this study, the yellow-fleshed peach, cv. ‘Jinxiu’, with anthocyanin accumulation in the mesocarp around the stone was used as the experimental material. Red flesh (RF) and yellow flesh (YF) were sampled separately for flavonoid metabolite (mainly anthocyanins), plant hormone, and transcriptome analyses. The results showed that the red coloration in the mesocarp was due to the accumulation of cyanidin-3-O-glucoside, with an up-regulation of anthocyanin biosynthetic genes (F3H, F3′H, DFR, and ANS), transportation gene GST, and regulatory genes (MYB10.1 and bHLH3). Eleven ERFs, nine WRKYs, and eight NACs were also defined as the candidate regulators of anthocyanin biosynthesis in peach via RNA-seq. Auxin, cytokinin, abscisic acid (ABA), salicylic acid (SA), and 1-aminocyclopropane-1-carboxylic acid (ACC, ethylene precursor) were enriched in the peach flesh, with auxin, cytokinin, ACC, and SA being highly accumulated in the RF, but ABA was mainly distributed in the YF. The activators and repressors in the auxin and cytokinin signaling transduction pathways were mostly up-regulated and down-regulated, respectively. Our results provide new insights into the regulation of spatial accumulation pattern of anthocyanins in peach flesh.

Charcoal-based fertilizer could be used extensively and is environmentally friendly. An experiment was designed to investigate the effects of different charcoal-based fertilizer application methods on soil microbiology and grape quality in a vineyard to guide the cultivation of ‘Shine-Muscat’. A control treatment without fertilization and six other treatments were set up. Four treatments applied carbon-based fertilizer as a base fertilizer with or without potassium fulvic acid, a complex microbial agent, or Bacillus subtilis, and two treatments were only applied with two applications of carbon-based fertilizer or compound fertilizer during the expansion period. The results showed that the bacterial phyla were mainly Proteobacteria and Bacteroidetes. Ascomycota, Basidiomycota, and Mortierellomycota dominated the fungal community. At the genus level, the composition of fungi, compared to bacteria, varied significantly, while the dominant flora differed among fertilization practices. Application of charcoal-based fertilizer enriched beneficial microorganisms, while chemical fertilizers enriched pathogenic microorganisms. The addition of microbial fungicides and biostimulants for a period reduced the size of the microbial network, lowered positive correlations, and enhanced resistance to adverse conditions and diseases and there was no significant correlation between agronomic traits and microbial network topology. A combination of soil microbial and grape agronomic traits suggests that a charcoal-based fertilizer base, with microbial fungicides applied, is the optimal fertilization regimen for grape.

To address the challenges of low automatic leveling efficiency and insufficient control precision for small tracked operation chassis navigating uneven terrain in hilly and mountainous areas, this study proposes a leveling control system that integrates a dual-layer threshold strategy with a BP neural network algorithm. The system is developed based on a four-point lifting leveling mechanism. Building upon this foundation, the conventional single-threshold angle error compensation control strategy was optimized to meet the specific leveling demands of chassis operating in such complex environments. A co-simulation platform was established using Matlab/Simulink-AMEsim for subsequent simulation and comparative analysis. Simulation results demonstrate that the proposed method achieves a 15.6% improvement in leveling response speed and a 21.3% enhancement in leveling accuracy compared to the classical single-threshold PID control algorithm. Static test results reveal a smooth leveling process devoid of significant overshoot or hysteresis, with the leveling error consistently maintained within 0.5°. Field tests further indicate that at a travel speed of 3 km/h under a 50 kg load, the platform stabilization time is reduced by an average of 1.3 s, while the leveling angle error remains within 0.5°. The proposed system not only improves leveling response speed and precision but also effectively enhances the overall leveling efficiency of the tracked chassis system.