Assessing the Feasibility and Scalability of Using Spark for Identifying Tip Burn Diseases in Strawberry Leaves

Tip burn is a common calcium deficiency symptom found in strawberry leaves, which can deform, fold over, and discolor the tips or edges of budding leaves. To help farmers detect diseases early and manage their crops more effectively, this work aims to build a solution that employs image processing and machine learning techniques to detect tip burn in strawberry leaves on a farm. The proposed work utilizes a dataset of 1431 images of ‘diseased’ and ‘healthy’ strawberry leaves. A Random Forest classifier, which is an ensemble learning model built on decision trees, CNN, or convolutional neural networks, and VGG-16, a transfer learning-based approach were all fitted in the PySpark environment to assess the feasibility and scalability of using Spark for distributed computing. The results indicate the deep learning approaches outperformed the traditional machine learning approach in accuracy and execution speed, making them a promising tool for identifying tip burn diseases in strawberry leaves. Overall, this work provides farmers with a fast, feasible, and reliable method for detecting tip burn, aiding in crop management, and reducing crop loss.