SKRC & SCAND Chairman Discusses Swedish Village Project with Al-Najaf’s Urban Planning Department

 Enhancing Agriculture Crop Classification with Deep Learning

By Yasmin Makki Mohialden, with Nadia Mahmood Hussien, Saba Abdulbaqi Salman, Ahmed Bahaaulddin A. Alwahhab, and Mumtaz Ali

Agriculture stands at the forefront of global challenges such as food security, sustainability, and technological innovation. Among staple crops, rice plays a central role in feeding nearly half of the world’s population. 

Reliable identification and classification of rice crops are essential to optimize yield prediction, crop health monitoring, and effective agricultural decision-making. The growing capabilities of deep learning, particularly Convolutional Neural Networks (CNNs), provide promising solutions to address these challenges.

Our study, conducted in collaboration with my esteemed colleagues—Dr. Nadia Mahmood Hussien (Mustansiriyah University), Dr. Saba Abdulbaqi Salman (Aliraqia University), Dr. Ahmed Bahaaulddin A. Alwahhab (Middle Technical University), and Dr. Mumtaz Ali (Deakin University, Australia)—aimed to advance crop classification technologies using deep learning approaches. The findings of our research were published in the Babylonian Journal of Artificial Intelligence (2024).

Problem Statement

Traditional machine learning techniques in agriculture have demonstrated value; however, they often struggle with imbalanced datasets and complex crop imagery. A key obstacle we identified is label imbalance in available crop image datasets, which hampers the accuracy and reliability of models. While rice images are abundant, non-rice crop images (such as wheat, sugarcane, jute, and maize) are less represented, leading to biased performance and skewed results.

Our work directly addresses this imbalance, aiming to test and refine CNN-based classification methods to better distinguish rice from non-rice crops, while also laying the foundation for more robust agricultural applications.

Related Work

Recent years have seen significant progress in agricultural applications of deep learning. For instance, hyperspectral imaging (HSI) has been applied for rice seed classification under varying temperature conditions, achieving accuracies above 90%. Other studies have used transfer learning with CNNs to detect rice seed vigor across varieties with promising generalization results. Furthermore, deep-learning-based recognition of rice phenological stages has shown potential for long-term crop monitoring.

These advancements inspired our research and underscored the importance of refining deep learning frameworks to suit practical agricultural scenarios. Our work builds upon these foundations while focusing on binary crop classification.

Proposed Method

Our methodology follows a systematic pipeline, illustrated in Figure 1. The key steps include:

Figure 1 - Is the Block Diagram of the Proposed Method

1. Loading Images – Rice and non-rice images were collected and prepared.
2. Data Preprocessing – Images were resized (e.g., 100x100 pixels), converted into arrays, and augmented through horizontal flipping, rotation, and shifting to increase dataset diversity.
3. Dataset Splitting – Images were divided into training and testing sets for model evaluation.
4. CNN Architecture – We designed a sequential CNN model with multiple convolutional, pooling, and dense layers, concluding with a sigmoid activation for binary classification.
5. Training and Compilation – Using TensorFlow and Keras, the model was compiled with the Adam optimizer and binary cross-entropy loss, then trained with validation monitoring.
6. Evaluation – Model performance was assessed through accuracy, precision, recall, F1-score, and confusion matrices.
7. Visualization and Metrics – Accuracy and loss curves were plotted (see Figure 2 and Figure 3) and performance metrics saved for future analysis.

Figure 2 - The Model Accuracy

Figure 3 - The Model Loss

The Python environment supporting this work leveraged widely used libraries including NumPy, Pandas, Matplotlib, scikit-learn, TensorFlow, TensorBoard, and Keras (see Table 1).

Table 1 - The Python Libraries in the Proposed System

Results and Discussion

The model demonstrated 100% accuracy in both training and testing datasets. However, this apparent success must be interpreted cautiously.

Table 2 - The Classification Report

As shown in the classification report (Table 2), the model excelled at identifying rice crops (precision, recall, and F1-score all equal to 1). Yet, the performance on the non-rice class was poor, with precision and recall values of zero. Similarly, the confusion matrix (Table 3) revealed that the model failed to correctly classify any non-rice samples, while perfectly classifying all rice samples.

Table 3 - Confusion Matrix

This outcome reflects a severe class imbalance problem, where the model overfits to the dominant rice class, ignoring underrepresented non-rice classes. While the results confirm the strength of CNNs in detecting rice crops, they also highlight the necessity of balanced datasets and improved model designs for real-world applications.

Conclusion and Future Work

Our study demonstrates both the potential and limitations of deep learning in crop classification. The model achieved exceptional accuracy for rice crops but underperformed for non-rice crops, due to dataset imbalance.

Future research directions include:

• Expanding datasets with more non-rice crop images.
• Applying advanced data augmentation techniques.
• Leveraging transfer learning with pre-trained models such as VGG, ResNet, or Fusion networks.
• Tuning hyperparameters (e.g., learning rate, batch size, network depth).
• Exploring ensemble methods to improve classification robustness.

We believe these enhancements will strengthen the model’s reliability and make it more suitable for precision agriculture, enabling applications such as yield prediction, disease detection, and real-time crop monitoring.

Acknowledgment

I would like to express my gratitude to my co-authors Nadia Mahmood Hussien, Saba Abdulbaqi Salman, Ahmed Bahaaulddin A. Alwahhab, and Mumtaz Ali for their invaluable contributions to this research. Together, we continue to work toward advancing agricultural technologies that will contribute to global food security.

 

Conflicts of Interest and Funding

Our team confirms that there were no conflicts of interest in this study. Moreover, this research received no external funding or institutional sponsorship, underscoring its academic independence.

 

References

The full list of references is included in the published paper.

 

Research Link

This article is based on the peer-reviewed publication:
Enhancing Agriculture Crop Classification with Deep Learning, Babylonian Journal of Artificial Intelligence, 2024, Vol. 20–26.