Nakib Mohammad Imtiaz, Mridha M F
Department of Computer Science, American International University-Bangladesh, Dhaka, Bangladesh.
Data Brief. 2024 Feb 13;53:110182. doi: 10.1016/j.dib.2024.110182. eCollection 2024 Apr.
Plant diseases pose a significant obstacle to global agricultural productivity, impacting crop quality yield and causing substantial economic losses for farmers. Watermelon, a commonly cultivated succulent vine plant, is rich in hydration and essential nutrients. However, it is susceptible to various diseases due to unfavorable environmental conditions and external factors, leading to compromised quality and substantial financial setbacks. Swift identification and management of crop diseases are imperative to minimize losses, enhance yield, reduce costs, and bolster agricultural output. Conventional disease diagnosis methods are often labor-intensive, time-consuming, ineffective, and prone to subjectivity. As a result, there is a critical need to advance research into machine-based models for disease detection in watermelons. This paper presents a large dataset of watermelons that can be used to train a machine vision-based illness detection model. Images of healthy and diseased watermelons from the Mosaic Virus, Anthracnose, and Downy Mildew Disease are included in the dataset's five separate classifications. Images were painstakingly collected on June 25, 2023, in close cooperation with agricultural experts from the highly regarded Regional Horticulture Research Station in Lebukhali, Patuakhali.
植物病害对全球农业生产力构成重大障碍，影响作物质量和产量，给农民造成巨大经济损失。西瓜是一种常见的栽培肉质藤本植物，富含水分和必需营养物质。然而，由于不利的环境条件和外部因素，它易受各种病害影响，导致品质下降和重大经济挫折。迅速识别和管理作物病害对于减少损失、提高产量、降低成本和增加农业产出至关重要。传统的病害诊断方法往往劳动强度大、耗时、效率低且容易主观。因此，迫切需要推进基于机器的西瓜病害检测模型的研究。本文展示了一个可用于训练基于机器视觉的病害检测模型的西瓜大型数据集。该数据集的五个单独分类中包含了来自花叶病毒、炭疽病和霜霉病的健康和患病西瓜的图像。这些图像于2023年6月25日与来自帕图阿卡利勒布卡利备受赞誉的地区园艺研究站的农业专家密切合作精心收集。
