Malabar Spinach dataset for diseases classification using deep learning approach

(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.

（1）马拉巴尔菠菜病害是一种广泛影响农业生产产量与品质的问题。该病害对马拉巴尔菠菜的作物品质产生有害影响。马拉巴尔菠菜是一种常用于其营养价值与口感而广泛种植的叶菜类蔬菜。然而，在特定的非生物环境中，病害可能会严重损害马拉巴尔菠菜的产量与品质，从而给农民带来巨大的经济损失。传统的病害诊断方法往往耗时费力、效果不佳且主观性强。

（2）近年来，计算机视觉技术在应对马拉巴尔菠菜病害分类与检测的挑战方面展现出巨大的潜力。

（3）为了开发基于机器视觉的马拉巴尔菠菜病害检测算法，一个综合性的数据集已被精心整理。该数据集包括代表各种马拉巴尔菠菜病害的图像，如黑斑病、叶螨和健康马拉巴尔菠菜。马拉巴尔菠菜病害的分类是在农业研究机构的专家们的协作下完成的。

（4）共收集了603张马拉巴尔菠菜植物的真实田野图像。此外，为了扩展数据集，通过翻转、剪切、缩放和旋转等技术，从原始图像中生成了5868张增强图像。这种增强对于提高机器视觉算法检测和分类马拉巴尔菠菜病害的准确性和鲁棒性至关重要。