A multi-plant disease classification using convolutional neural network

• Main Authors: Usman, Muhammad, Raja, Gulistan
• Format: Conference Proceeding
• Language: English
• Subjects: Accuracy; Artificial neural networks; Classification; Corn; Crop diseases; Crop production; Datasets; Machine learning; Plant diseases; Rice; Wheat
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0214499

The quantity and quality of food produced are often threatened by plant diseases, which can have a devastating impact. Therefore, early diagnosis of plant diseases is often crucial to prevent production losses. The development of deep learning modelshas opened up new avenues for achieving high accuracy in detecting and mitigating crop diseases. This paper proposes a CNN models for accurately classifying leaf diseases in crops, specifically focusing on rice, corn, and wheat plants. The proposed method utilizes DenseNet169 and InceptionV3 as the base models respectively trained on corn, rice, and wheat disease datasets. The proposed method has successfully achieved an accuracy of 99.89% using DenseNet169 and 99.25% using InceptionV3 for corn leaf disease dataset. The proposed method also obtained an accuracy of 98.52% and 98.58% for the wheat crop disease using DenseNet169 and InceptionV3 models respectively. For the Rice crop, rice dataset is separated into major and minor diseasesin order to comprehend the variety of diseases and obtained an accuracy of 99.18% and 91.77% for major and minor rice crops using DenseNet169 variants respectively. Overall, the proposed approach proves to be successful in accurately classifying diseasesacross rice, corn, and wheat crop diseases, providing reliable and detailed predictions.