A Novel Transfer Learning Approach for Detection of Pomegranates Growth Stages

Abstract

Pomegranates are nutrient-rich fruits renowned for their vibrant ruby-red seeds and antioxidant properties. With a rich history rooted in various cultures, pomegranates have gained widespread popularity for their distinct flavor and potential health benefits. Timely detection and understanding of the growth stages of pomegranates can facilitate optimized resource allocation, targeted interventions, and efficient crop management. Additionally, early detection contributes to maximizing crop yield, ensuring product quality, and mitigating potential risks such as diseases and pest infestations. The primary goal of the present study is the early detection of pomegranate growth stages using an efficient approach. We conducted our experiments using standard image data of the pomegranate growth stages, comprising 5857 files categorized into five classes: Bud, Early-Fruit, Flower, Mid-growth, and Ripe. We propose a transfer learning-based CRnet approach to capture spatial features from pomegranate images depicting the five stages of pomegranate growth. The extracted spatial features serve as inputs for the random forest method, resulting in the creation of a new probabilistic feature set. These new probabilistic features assist the proposed model in performing the detection of pomegranate growth stages. To evaluate performance, we implemented state-of-the-art image classification techniques, including a Convolutional Neural Network (CNN), K-Neighbors Classifier (KNC), Gaussian Naive Bayes (GNB), and Logistic Regression (LR). To ensure the accuracy of applied machine learning methods, we utilized a hyperparameter optimization approach and a k-fold-based cross-validation technique. Additionally, computational complexity is determined. Extensive analysis of research results shows that by using the proposed features, the random forest model outperformed state-of-the-art methods with a high accuracy of 98% in predicting pomegranate growth stages. Our proposed scheme has the potential for the timely detection of pomegranate growth stages, assisting farmers in maximizing crop yield and mitigating potential risks.

Keywords

• Pomegranates
• pomegranate growth
• machine learning
• image processing
• deep learning
• transfer learning