Local Spinach Leaf Dataset
Description
1. The Significance of Local Spinach in Horticulture and Fresh and Non Fresh Challenges. In the horticulture industry, local Spinach leaves are highly valued for both their aesthetic value and economic significance. However, several that might harm these plants' well-being and productivity sometimes act as a barrier to their cultivation. Accurate and timely leaf Fresh and Non Fresh detection is necessary to lower these risks. The Local Spinach Leaf Dataset was developed in answer to this need, assisting scientists, horticulturists, and machine learning experts in identifying and classifying that affect local Spinach leaves. This collection includes high-resolution images of local Spinach leaves that display both healthy and diseased specimens. Notable ailments found in the dataset include Malabar spinach, water spinach, and red spinach that is not fresh. The dataset is further enhanced by the addition of metadata that provides vital contextual information about the locations and weather at the time of image capture. This contextual data enhances prediction model accuracy and is critical to understanding the mechanisms behind the onset of leaf Fresh and Non Fresh. 2. The Role of Deep Learning and Computer Vision. There are several chances for classification and detection tasks in the local spinach leaf disease dataset that can be handled by contemporary deep learning and computer vision techniques. 3. A Comprehensive Dataset for Deep Learning in local Spinach Fresh and Non Fresh Classification. This dataset was produced specifically to support the development of advanced deep learning models. The dataset's classifications were created in collaboration with subject matter experts from a prestigious agricultural university to guarantee accuracy and relevance. 4. Data Collection and Augmentation. 3999 Pictures were taken at the Maharabaha, kaliakoir location in Gazipur, Dhaka, Bangladesh. Examples of red Spinach fresh and non-fresh, Water Spinach fresh and non-fresh, and Malabar Spinach fresh and non-fresh were displayed in these pictures. To improve the dataset and expand the number of available data points, 2495 augmented images were created from the original photo collection. These improved samples were produced using a number of techniques, including flipping, rotating, shearing, brightness modification, and magnification.