Malabar Spinach dataset for diseases classification using deep learning approach
Description
(1) Malabar Spinach disease is a widespread problem affecting the productivity and quality of agricultural production. It has a detrimental impact on the quality of Malabar Spinach crops. Malabar Spinach is a leafy green vegetable frequently grown for its nutritional value and taste. However, under certain non-biological circumstances, diseases can severely harm the yield and quality of Malabar Spinach, resulting in significant economic losses for farmers. Traditional methods of diagnosing these diseases are often time-consuming, labor-intensive, ineffective, and subjective. (2) In recent years, computer vision approaches have shown great promise in addressing the challenges of disease classification and detection in Malabar Spinach crops. (3) To develop machine vision-based algorithms for detecting Malabar Spinach diseases, a comprehensive dataset has been curated. This dataset comprises images representing various Malabar Spinach diseases, including Anthracnose_leaf_spot, Straw_mite, and Healthy Malabar Spinach. The classification of Malabar Spinach diseases was accomplished with the collaboration of experts from agricultural research institutes. (4) A total of 603 images of Malabar Spinach plants were collected from real fields. Additionally, to expand the dataset, 5868 augmented images were generated from the original ones using techniques such as flipping, shearing, zooming, and rotation. This augmentation is crucial for enhancing the accuracy and robustness of the machine vision algorithms for detecting and classifying Malabar Spinach diseases.