An Enhanced Classification Model for Disease Detection in Apple Leaves

Abstract

Agriculture is a vital contributor to human life on Earth, playing a crucial role in providing newlinesustenance and driving economic growth in regions. However, plants can be affected by a newlinevariety of diseases caused by factors such as excessive use of chemicals, as well as bacterial, newlineviral, and fungal infections. Accurate diagnosis of these diseases is crucial, as the use of newlineincorrect treatments may lead to increased pathogen resistance and further harm to the plants. newlineTraditional manual diagnosis methods for leaf diseases can be time-consuming and lead to newlinedelays in effective treatment. Utilizing Deep Learning frameworks, however, presents an newlineopportunity to efficiently and accurately detect and classify plant disease. Apple Leave Disease newline(ALD) is a common problem that affects apple trees, causing significant damage and yield loss newlinein orchards. There are several types of ALD, including apple scabs, powdery mildew, and fire newlineblight, which are caused by different pathogens such as fungi, bacteria, and viruses. The newlinesymptoms of ALD may vary depending on the type of disease, but typically involve newlinediscoloration, spots, deformities, and eventually death of leaves, stems, and fruits. The spread newlineof ALD is often facilitated by factors such as moisture, temperature, and poor agricultural newlinepractices, such as the excessive use of chemicals. Managing ALD can be a challenging task, as newlineit requires accurate and timely detection and diagnosis of the disease. Traditional manual newlinedetection methods can be time-consuming and often rely on visual inspection, which can be newlinesubjective and prone to errors. In addition, incorrect treatments can lead to further damage and newlinethe development of pathogen resistance. To address these challenges, researchers have newlinedeveloped advanced techniques such as machine learning algorithms, spectral imaging, and newlineother digital technologies to detect and classify ALD with high accuracy and efficiency. T newline