Image-Based Seed Damage Detection Using a CNN Model

Image-Based Seed Damage Detection Using a CNN Model

Prof. Poonam More

Department of ECE,

Usha Mittal Inst. of Technology, SNDT Womens University, Mumbai

Urvashi Sawant

Department of ECE,

Usha Mittal Inst. of Technology, SNDT Womens University, Mumbai

Dr. Seema Hanchate

Department of ECE,

Usha Mittal Inst. of Technology, SNDT Womens University, Mumbai

Pragya Pandita

Department of ECE,

Usha Mittal Inst. of Technology, SNDT Womens University, Mumbai

Vanshika Varma

Department of ECE,

Usha Mittal Inst. of Technology, SNDT Womens University, Mumbai

AbstractAccurate seed quality assessment is essential in agriculture. Several automated and machine learning-based methods have been developed to overcome the limitations of manual inspection. However, challenges such as computational complexity, scalability, and deployment on low-resource devices still persist. This work proposes an image-based seed damage detection system using an optimized Convolutional Neural Network (CNN). The proposed approach uses preprocessing techniques such as image resizing, normalization, and controlled data augmentation to enhance robustness and performance. The model is developed, trained, and evaluated using Python with TensorFlow and Keras. It enables effective classication of seeds into damaged and undamaged categories. The trained model is further converted into TensorFlow Lite format for deployment on resource-constrained edge devices. This reduces dependence on cloud-based processing. Evaluation on a balanced dataset consisting of four seed types, namely Apple, Bitter Gourd, Custard Apple, and Mosambi, demonstrates that the proposed system is reliable, scalable, and cost-effective. Performance is evaluated using accuracy, precision, recall, F1-score, specicity, and ROCAUC, with the model achieving a training accuracy of 99.18%, validation accuracy of 90.32%, and test accuracy of 95.83%, demonstrating strong generalization performance.

Keywords Seed Damage Detection, Convolutional Neural Network (CNN), Deep Learning, Image Processing, Binary Classication, TensorFlow, Agricultural Automation

1. Introduction

   Agriculture plays an important role in food security and eco-nomic sustainability, particularly in developing regions where crop productivity directly inuences rural livelihoods. Among the various factors affecting crop performance, seed quality plays a decisive role in determining germination rate, plant vigor, and overall yield. However, in many practical scenarios, seed quality assessment continues to rely on manual visual inspection. This traditional process is time-consuming and may lead to inconsistent results, resulting in inaccurate grading and potential yield losses.

   Early research efforts attempted to automate seed evalu-ation using basic image processing techniques such as shape and color feature extraction [1]. Subsequent studies incorpo-

   rated articial neural networks and pattern recognition meth-ods to improve classication reliability and reduce human dependency [2]. Digital image processing approaches were further explored for seed purity analysis and defect detection [3], while structured visual analysis methods were applied to specic crop varieties to enhance classication accuracy [4]. More recent work demonstrated that computational image analysis can effectively support seed evaluation tasks under controlled conditions [5].

   Despite these advancements, many conventional ap-proaches rely heavily on handcrafted features and traditional classiers, which often struggle to generalize across varying illumination, orientation, and texture conditions. These limi-tations emphasize the need for more adaptive and automated feature-learning mechanisms capable of handling real-world variability.

   In this work, we present a complete software-based seed damage detection system built using a Convolutional Neural Network (CNN). Unlike traditional machine learning tech-niques that depend on manually extracted features, CNNs automatically learn hierarchical visual representations directly from raw image data. The proposed system is implemented as an end-to-end software pipeline comprising structured dataset management, image preprocessing, model training, perfor-mance evaluation, and deployment-ready inference modules. By leveraging deep learning for binary classication of seeds into Damaged and Undamaged categories, the system provides a consistent, scalable, and computationally efcient solution suitable for real-time agricultural applications.

2. Related Work

   Recent advancements in agricultural image analysis increas-ingly emphasize deep learning techniques for seed and crop classication. While earlier systems relied on handcrafted de-scriptors, modern convolutional neural networks have demon-strated superior capability in learning discriminative features directly from image data. A comprehensive deep learning study involving classication across a large and diverse set of

   seed types demonstrated that CNN architectures can achieve high accuracy without manual feature engineering, highlight-ing their scalability and robustness across diverse seed varieties [6]. This work laid a strong foundation for applying deep learning to agricultural image analysis.

   Expanding on this direction, Loddo et al. proposed a CNN-based framework for seed image classication and retrieval, focusing on robustness under varying illumination conditions, background complexity, and seed orientation changes [7]. Their ndings reinforced the adaptability of deep learning models in practical agricultural environments compared to conventional image processing methods.

   Further research explored the integration of digital image processing and machine learning techniques to automate seed testing workows. Saduwale et al. developed an image-based seed testing system aimed at minimizing human intervention while improving evaluation consistency [8]. Similarly, Koppad et al. investigated automated seed segregation using image analysis methods, demonstrating the feasibility of intelligent sorting mechanisms within agricultural operations [9]. Rajku-mar et al. analyzed seed testing methodologies to improve cultivation efciency through computational image processing techniques [10].

   Complementing this effort, Meshram et al. introduced a structured agricultural image dataset designed to facilitate machine learning experimentation and benchmarking [11] (Meshram et al.), highlighting the importance of curated datasets in building reliable classication models. More re-cently, Raghavan et al. investigated seed quality determination using machine learning frameworks to automate inspection processes traditionally dependent on manual observation [12] (Raghavan et al.). Their study emphasized improvements in reliability, repeatability, and scalability achieved through algorithm-driven evaluation systems.

   Overall, previous studies show a gradual shift, from traditional image processing techniques toward deep learn-ingbased automated solutions for seed classication and quality assessment. While much of the existing literature focuses on multi-class seed variety identication and large scale classication tasks, comparatively fewer works address structured software pipelines specically tailored for binary seed damage detection. The present work addresses this gap by developing a modular, end-to-end CNN-based software framework optimized for efcient, reliable, and deployment ready seed damage classication.

3. Prposed Model

   The proposed system is designed as an end-to-end software framework for automated seed damage detection using Con-volutional Neural Networks (CNN). The architecture follows a structured ow beginning with image acquisition and prepro-cessing, progressing through hierarchical feature extraction, and concluding with binary classication into Damaged or Undamaged categories.

   Fig. 1: Proposed System Model Pipeline

   1. Input (as image)

      Seed images serve as the primary input to the system. The dataset is organized into structured training, validation, and testing directories to facilitate supervised learning and sys-tematic evaluation.

   2. Preprocessing Stage

      To ensure consistency and enhance robustness, input images undergo preprocessing operations including:

      • Resizing to 128 × 128 resolution

      • Pixel normalization to the range [0,1]

      • Controlled augmentation (rotation, zoom, shear, width and height shift, and horizontal ip for the training set)

        These steps standardize input dimensions and improve the models ability to generalize across variations in orientation, scale, and illumination.

   3. CNN Based Feature Extraction

   This is the core of the system model. The Convolutional Neural Network automatically extracts visual features that distinguish damaged from undamaged seeds. The CNN model consists of several layers:

   1. Convolutional Layers These layers apply multiple learn-able lters to the input image. Functions:

      • Capture local visual features such as edges, texture patterns, cracks, and surface irregularities.

      • Learn increasingly complex features at deeper layers.

   2. Activation Layers (ReLU) After each convolution oper-ation, an activation function (typically ReLU) is applied.

      Functions:

      • Introduces non-linearity.

      • Helps the model learn complex relationships between features.

      • Prevents vanishing gradient issues.

   3. Pooling Layers (Max Pooling) Pooling layers reduce the spatial dimensions of feature maps.

      Functions:

      • Down-sample information

      • Reduce computational complexity

      • Retain the most important features

4. Implementation

   This section describes the practical realization of the proposed system, including dataset preparation, model conguration, training procedure, evaluation methodology, and inference workow.

   1. Dataset Preparation

      The dataset is organized into two primary directories, namely Train and Test, which are used for model training and per-formance evaluation, respectively. Each directory contains two subfolders corresponding to the class labels: Damaged and Un-damaged. This directory structure enables efcient loading and labeling of images during training and testing. Additionally, a validation subset was created from the training data using an 80:20 split to monitor model performance during training and prevent overtting. Strict separation between training, validation, and test datasets was maintained to prevent data leakage.

      All images are resized to a xed resolution to ensure uni-form input dimensions for the convolutional neural network. The dataset is loaded using the ImageDataGenerator util-ity, which facilitates efcient data handling and preprocessing during runtime. Pixel values are normalized by rescaling them to a range of [0, 1], which helps in faster convergence and stable training.

      To improve the robustness and generalization capability of the model, data augmentation techniques such as rotation, hor-izontal ipping, zooming, and shearing are applied exclusively to the training dataset. These transformations help the model learn invariant features and reduce overtting. The test dataset is not augmented and undergoes only normalization to ensure unbiased performance evaluation.

      These preprocessing steps help maintain consistent input data during training, allowing the CNN model to effectively learn discriminative features for accurate classication be-tween damaged and undamaged images.

   2. CNN Architecture Implementation

      The implemented CNN model consists of:

      • Three convolutional blocks with lters (32, 64, 128)

      • Two Conv2D layers in each block

      • Batch Normalization after convolution layers

      • Dropout layers (0.30.4) for regularization

      • MaxPooling after each convolutional block

      • Flatten layer

      • Dense layer (64 neurons)

      • Dropout (0.6) before output layer

      • Output Dense layer (1 neuron, Sigmoid activation)

      • L2 regularization applied to convolutional and dense layers

        Fig. 2: CNN Layer Architecture with Input and Output Shapes

        Fig. 2 illustrates the dimensional transformation of feature maps through successive convolution and pooling operations, demonstrating progressive spatial reduction and feature ab-

        straction.

   3. Training Conguration

      The model was compiled using:

      • Optimizer: Adam (learning rate = 8e-5)

      • Loss Function: Binary Cross-Entropy

      • Evaluation Metric: Accuracy

        Training was conducted for up to 25 epochs with early stopping. Learning rate reduction was applied when validation performance plateaued. Performance was monitored using training and validation accuracy and loss curves.

        1. Accuracy

        2. Loss

           Fig. 3: Training and Validation Accuracy & Loss Curves

           Fig 3 demonstrates overall convergence behavior, with val-idation performance closely following training performance, showing that the model learns effectively while limiting over-tting.

   4. Performance Evaluation

      Model performance was evaluated using:

      • Confusion Matrix

      • Accuracy

      • Precision

      • Recall

      • F1 Score

      • Specicity

      • ROCAUC

        Fig. 4: Confusion Matrix

        The confusion matrix indicates good classication perfor-mance with low misclassication and only a small number of false negatives during damaged and undamaged seed classi-cation.

        Fig. 5: Performance Metrics Summary The evaluation results indicate:

      • Accuracy 95.83%

      • High precision and recall for both classes

      • ROCAUC 0.981

        These results conrm the robustness and reliability of the proposed CNN model for binary seed damage detection.

   5. Prediction Workow

      For single-image inference, the following steps are executed:

      • Upload image

      • Convert to RGB format

      • Resize to 128 × 128

      • Normalize pixel values

      • Expand dimensions for model input

      • Predict probability

      • Assign class label (Damaged / Undamaged)

      • Display result with accuracy score

5. RESULTS AND ANALYSIS

   To evaluate the effectiveness of the proposed CNN-based seed damage detection system, representative test samples from multiple seed categories were analyzed. The system was tested on both damaged and undamaged seeds across Apple, Bitter Gourd, Custard Apple, and Mosambi varieties. The prediction outputs include the classied label and corresponding accuracy score.

   1. Observations

      1. High Classication Accuracy: Most predictions produced high accuracy values, demonstrating strong discriminative capability.

      2. Robust Performance Across Seed Types: The model maintains consistent performance across different seed varieties, indicating effective feature generalization.

      3. Minor Variations in Accuracy: Slightly lower accuracy values in certain cases may be attributed to subtle visual similarities or lighting variations.

      4. Correct Binary Classication: Representative samples were successfully classied into Damaged and Undam-aged categories.

   2. Overall Model Performance

   Comprehensive evaluation on the full test dataset yielded:

   • Accuracy 95.83%

   • Precision 0.998

   • Recall 0.918

   • F1-Score 0.956

   • Specicity 0.998

   • ROCAUC 0.981

6. Conclusion

This work developed an image-based seed damage detec-tion system built around a optimized CNN-based software pipeline. The combination of image preprocessing, compact CNN feature extraction, and lightweight TensorFlow Lite inference enables accurate and reliable classication of seeds as Damaged or Undamaged on resource-constrained embedded platforms without reliance on cloud computing. The system

Fig. 6: Representative Predictive Results

can support reliable and scalable seed quality assessment suitable for agricultural use. Future enhancements may include multi-class damage assessment, cloud-based storage, mobile application integration, and expanded datasets for improved generalization.

References

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2. S. Desai and A. Rao, Seed Quality Analysis Using Image Processing and ANN, IJTSRD, 2017.

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4. SKSVMACET CSE Dept., Quantifying and Analysing Maize Seed Variety Using Image Processing, 2018.

5. B. Lurs-t-w-u-t and C. Porn-pa-nom-chai, Image analysis for rice seed germination evaluation, Mahidol Univ., 2019.

6. B. Tug-r-ul et al., Deep Learning-Based Classication of 111 Different Seed Types, Adv. Theory Simul., 2019.

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