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Predicting Apple Plant Diseases in Orchards Using Machine Learning and Deep Learning Algorithms

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Abstract

Apple cultivation in the Kashmir Valley is a cornerstone of the region’s agriculture, contributing significantly to the economy through substantial annual apple exports. This study explores the application of machine learning and deep learning algorithms for predicting apple plant diseases in orchards. By leveraging advanced computational techniques, the research aims to enhance early detection and diagnosis of diseases, thereby enabling proactive disease management. The study utilizes a dataset comprising diverse environmental and plant health factors to train and validate the models. Key highlights include the comparative analysis of machine learning and deep learning approaches, the identification of optimal feature sets, and the assessment of model performance. The findings contribute to the development of efficient and accurate tools for precision agriculture, facilitating timely intervention and sustainable orchard management. The apple industry in Kashmir faces a significant challenge due to the prevalence of various diseases affecting apple trees. One prominent disease that adversely impacts apple yields in the region is the Apple Scab, caused by the fungus Venturia inadequacies. Apple Scab is characterized by dark, scaly lesions on leaves, fruit, and twigs, leading to defoliation and reduced fruit quality. The disease thrives in cool and humid conditions, which are prevalent in the Kashmir Valley. This study addresses the limitations of traditional, labor-intensive, and time-consuming laboratory methods for diagnosing apple plant diseases. The goal is to provide an accurate and efficient deep learning-based system for the prompt identification and prediction of foliar diseases in Kashmiri apple plants. Our study begins involves the creation of a dataset annotated by experts containing approximately 10,000 high-quality RGB images that illustrate key symptoms associated with foliar diseases. In the next step, an approach to deep learning that utilizes convolutional neural networks (CNNs) was developed. Comparative analysis five different deep learning algorithms, including Faster R-CNN, showed that the method was effective in detecting apple diseases in real time. The proposed framework, when tested, achieves state-of-the-art results with a remarkable 92% accuracy in identifying apple plant diseases. A new dataset is presented that includes samples of leaves from Kashmiri apple plants that have three different illnesses. The findings hold promise for revolutionizing orchard management practices, ultimately benefiting apple growers and sustaining the thriving apple industry in the Kashmir Valley.

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Data Availability

The dataset can be accessed from the Plant Village dataset.

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All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version.

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Correspondence to Imtiaz Ahmed.

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My investigation involves employing machine learning techniques for the detection of diseases in agricultural plants.

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Ahmed, I., Yadav, P.K. Predicting Apple Plant Diseases in Orchards Using Machine Learning and Deep Learning Algorithms. SN COMPUT. SCI. 5, 700 (2024). https://doi.org/10.1007/s42979-024-02959-2

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