AI-Driven Plant Pathology with Multifaceted Snake Gourd Leaf Disease Dataset
Description
1. The Significance of Snake Gourd in Horticulture and Disease Challenges In the horticulture sector, snake gourds are highly valued for their aesthetic appeal as well as their economic worth. However, a number of illnesses that can seriously impair plant health and productivity frequently make it difficult to cultivate these plants. To lessen these risks, leaf diseases must be identified promptly and accurately. The Snake Gourd Disease Dataset was created expressly to help horticulturists, researchers, and machine learning experts identify and categorize illnesses that impact snake gourd leaves in order to meet this demand. This dataset includes high-resolution photos of snake gourd leaves that show both disease-free and healthy specimens. Among the important illnesses mentioned are yellowing and anthracnose. Precise model training and validation are made possible by the thorough annotation of every image, which includes specifics about the nature and severity of the disease. The collection is further enhanced with metadata, which offers important background information about the locations and weather at the time of picture acquisition. Contextual information is essential for comprehending the variables influencing the occurrence of disease and improves the precision of prediction models. 2. The Role of Deep Learning and Computer Vision The Snake Gourd Disease Dataset has a plethora of opportunities for classification and detection tasks that can be addressed by modern deep learning and computer vision techniques. 3. A Comprehensive Dataset for Deep Learning in Snake Gourd Disease Classification This dataset was created especially to aid in the creation of sophisticated deep learning models. To ensure accuracy and relevance, the dataset's classifications were developed in conjunction with subject matter specialists from a leading agricultural institute. 4. Data Collection and Augmentation A total of five hundred photos were taken at the Savar demonstration location in Dhaka, Bangladesh. These photos included instances of yellowing, anthracnose, and healthy leaves. From the original collection of photos, 15,000 augmented images were created in order to improve the dataset and expand the amount of data points that were available. These augmented samples were made by applying several techniques like flipping, rotating, shearing, brightness modification, and magnification.