Jul 2026· International Conference on Machine Vision, Automatic Identification and Detection· Vol 14261, pp. 142611O - 142611O-5· 0 citations· 10 references
Engineering
TL;DR
This study develops an online grading model based on hyperspectral imaging and a lightweight convolutional neural network that provides a feasible technical pathway for intercepting mycotoxin contamination prior to grain storage.
Abstract
To address the urgent need for rapid, non-destructive, and full-scale inspection of moldy maize in grain storage scenarios, this study develops an online grading model based on hyperspectral imaging and a lightweight convolutional neural network. Using hyperspectral data cubes in the 400–1000 nm range as input, a third-order spatial–spectral joint convolution architecture is designed to enable mold severity classification from grade 0 to 4 at an 8×8×128 feature representation level. Trained on 250 laboratory samples, the model achieves an overall accuracy of 98.5% on an independent test set, with an F1-score of 98.0% for the critical grade 3 category. The inference time per kernel is less than 50 ms. In a blind test of 100 samples conducted at the Beilin National Grain Depot, the model demonstrates a consistency rate of 96.0% with manual inspection, confirming its capability to perform real-time identification of highrisk kernels within conveyor belt processing constraints. This provides a feasible technical pathway for intercepting mycotoxin contamination prior to grain storage.
A lightweight deep learning model, the Dual Multi-Layer Perceptron Feature Fusion Network (DMLPFFN) as a hyperspectral-based approach to the four tomato disease stages, which includes healthy, asymptomatic, early and late infection is demonstrated.
Poonam Chaudhary, Sneha Kandacharam· Indian Journal of Agricultur...· 0 citations
Reliable identification of millet cultivars is essential for maintaining grain quality, supporting seed authentication, and improving automation in post-harvest processing. Despite recent advances in computer vision, accurate classification of millet varieties remains challenging because many cultivars exhibit subtle visual differences in color, texture, shape, and grain arrangement, particularly when analyzed in bulk grain images. To address this challenge, a fusion-based convolutional neural network is developed that combines feature representations extracted at multiple spatial scales. The proposed architecture employs parallel convolutional branches with different receptive fields to capture local texture patterns, structural grain characteristics, and broader spatial relationships within bulk grain formations. The extracted features are integrated through a fusion mechanism to generate a richer and more discriminative representation for classification. To enhance model robustness under practical imaging conditions, image augmentation techniques including contrast variation, noise injection, sharpening, and rotation were incorporated during training. The proposed model was evaluated against AlexNet, VGG16 trained from scratch, pretrained VGG16, and pretrained InceptionV3 using the same experimental settings. Among all evaluated architectures, the fusion network consistently achieved the best performance, attaining a validation accuracy of 93.80% and a testing accuracy of 93.09%. The model further achieved a Precision of 93.02%, Recall of 92.04%, and F1-score of 92.52%, indicating balanced and reliable classification performance across millet cultivars. Analysis of the confusion matrix revealed strong class-wise discrimination with only limited confusion among visually similar varieties, while cultivars possessing distinctive color characteristics were identified with perfect accuracy. The results highlight the importance of multi-scale feature learning for fine-grained agricultural image classification and demonstrate that feature fusion can substantially improve cultivar recognition in bulk grain imagery. The proposed framework offers an effective solution for automated millet identification and can support the development of intelligent grain sorting and quality assessment systems for modern agricultural applications.
Dewendra Bharambe, Pushpalata G. Aher· Journal of Intelligent Decis...· 1 citation
This approach could enable automated instance segmentation of disease symptoms on citrus leaves and thus assist citrus growers in early intervention and contribute to the development of a cost-effective multispectral disease inspection system with selected bands.
Quentin Frederick, Thomas F. Burks, Md Zafar Iqbal et al.· Journal of the ASABE· 0 citations
Hyperspectral imaging is a well-established remote sensing technique for material detection and classification, relying on hundreds of reflectance bands to exploit the spectral signatures of surface materials. Although hyperspectral imagery has demonstrated excellent capabilities for material identification, its operational implementation may be constrained in some applications due to data volume and processing requirements. Consequently, there is growing interest in evaluating the capability of lower-dimensional multispectral imagery for material detection tasks. In this sense, this article proposes as its contribution the comparative evaluation of machine learning models and neural networks for asbestos–cement detection on VNIR imagery. For the development of this research, the CRISP-DM methodology was adapted into four phases: P1. Business and data understanding; P2. Data preparation; P3. Modelling and evaluation; P4. Model deployment. At the results level, three datasets with different numbers of bands were constructed, which were structured by adding to the original dataset an additional layer with the NDVI and two additional layers with the PCA components of the original image. Across the three datasets, four machine learning models and one neural network model were tuned and evaluated, yielding as a result that in all three datasets the KNN and neural network models achieved the best performance. Likewise, it was found that the detection capability of the models improved with the inclusion of the additional bands. The proposed approach serves as a reference to be extrapolated by research centres and universities for the detection of asbestos and other materials in VNIR images, with a view toward integration into resource-constrained systems and specifically into environmental monitoring systems.
G. Chanchí-Golondrino, Isaac Esteban Camargo Freile, Julio Eduardo Mejía Manzano et al.· Digital· 0 citations
Existing reviews on AI in tea production are either agriculture-generic or limited to isolated tasks. This review thoroughly compares vision technologies (RGB, hyperspectral, near-infrared, thermal, Light Detection and Ranging (LiDAR), Unmanned Aerial Vehicle (UAV)) and establishes a task-oriented algorithm selection framework for the tea industry. For small-sample or near-linear problems, traditional machine learning (ML) (support vector machine (SVM); partial least squares regression (PLSR)) remains effective. For unstructured field tasks, deep learning achieves superior performance: pest detection accuracy exceeds 97%, tea bud detection reaches 96.8% with RGB images, and hyperspectral imaging predicts nitrogen content with R2 > 0.90 and tea polyphenols with R2 up to 0.925. Algorithm choice further differentiates by task granularity: lightweight convolutional neural networks (CNNs) balance speed and accuracy for edge deployment at 16 fps; You Only Look Once (YOLO) series detectors enable real-time localization on mobile platforms at 93.1% accuracy, 24 ms per target. No single algorithm dominates all tea tasks; selection is a trade-off among accuracy, speed, data availability, and computational constraints. These findings outline a structured analysis of the challenges and pathways for transitioning computer vision (CV) from laboratory research toward field-deployable tools.
Zunren Chen, Jin-Feng Wang, Yilan Sun et al.· Foods· 0 citations
Corn Transfer Learning Network (CTL-Net), an end-to-end hybrid deep learning model for corn leaf disease identification, allows accurate and interpretable predictions in a computationally efficient manner, making it an excellent candidate for mobile and resource-limited applications in precision agriculture.
K. P. Kumar, Y. Kuma· Engineering, Technology &...· 0 citations