Peach firmness is a critical quality attribute, yet conventional destructive measurement methods are unsuitable for batch detection in industrial settings. This study investigated a noncontact method for firmness assessment across multiple peach cultivars based on acoustic vibration technology. Three peach cultivars were mechanically excited via a controlled air jet, and the resulting acoustic vibration responses were captured noninvasively using a laser Doppler vibrometer. The frequency-domain acoustic vibration spectra were used as input for firmness prediction models developed using partial least squares regression (PLSR), support vector regression (SVR), and a one-dimensional convolutional neural network (ISNet-1D) that incorporated Inception and squeeze-and-excitation modules. Comparative analysis demonstrated that the ISNet-1D substantially outperformed the conventional linear and nonlinear methods on an independent test set, achieving superior predictive accuracy, with a coefficient of determination ( $R_P^2)$ of 0.8069, a root mean square error (RMSEP) of 0.9206 N/mm, and a residual prediction deviation ( ${RPD}_P)$ of 2.2879. The good performance of the ISNet-1D can be attributed to the integration of multi-scale convolutional filters with a channel-wise attention mechanism. This integration allows the network to adaptively prioritize discriminative spectral features, thereby enhancing its prediction accuracy. A hierarchical transfer learning strategy was proposed to improve model generalizability, offering a practical and cost-effective means to adapt to diverse cultivars. In summary, the combination of noncontact acoustic vibration and deep learning presents a robust, accurate, and nondestructive methodology for assessing peach firmness, demonstrating considerable potential for cross-cultivar application in industrial sorting and quality control.

Early-stage maize leaf diseases and pests are difficult to detect due to their small size, low contrast, and complex backgrounds. AdaptiveLeaf is a lightweight multi-scale framework designed to improve the detection of such small targets. It integrates an Adaptive Kernel Lightweight Block (AKL-Block) for dynamic multi-scale feature extraction, a Feature Decomposition and Reconstruction (FDR) module to recover fine details such as lesion edges and spore clusters, and a Scale-Aware Gradient Boosting Loss (SAGB-Loss) to increase the training contribution of small targets. Experiments on 10,324 field-collected maize leaf images across eight disease and pest categories show that AdaptiveLeaf achieves a mean mAP@0.5 of 75.0% over three repeated runs and increases small-target AP from 28.1% to 32.8%, using only 2.52 M parameters and 5.3 GFLOPs. The framework balances accuracy and efficiency, making it suitable for real-time field inspection and precision agriculture.