Plant Disease Detection Strategy Based on Image Texture and Bayesian Optimization with Small Neural Networks

Juan Felipe Restrepo-Arias (), John W. Branch-Bedoya and Gabriel Awad
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Juan Felipe Restrepo-Arias: Escuela de Ciencias Aplicadas e Ingeniería, Universidad EAFIT, Medellín 050022, Colombia
John W. Branch-Bedoya: Facultad de Minas, Universidad Nacional de Colombia, Sede Medellín, Medellín 050041, Colombia
Gabriel Awad: Facultad de Minas, Universidad Nacional de Colombia, Sede Medellín, Medellín 050041, Colombia

Agriculture, 2022, vol. 12, issue 11, 1-18

Abstract: A novel method of disease diagnosis, based on images that capture every part of a diseased plant, such as the leaf, the fruit, the root, etc., is presented in this paper. As is well known, the plant genotypic and phenotypic characteristics can significantly impact how plants are affected by viruses, bacteria, or fungi that cause disease. Assume that these data are unknown at the outset and that the appropriate precautions are not taken to prevent classifications skewed toward uninteresting traits. An approach to avoid categorization bias brought on by the morphology of leaves is suggested in this study. The basis of this approach is the extraction of textural features. Additionally, Bayesian Optimization is suggested to obtain training hyperparameters that enable the creation of better-trained artificial neural networks. First, we initially pre-processed the images from the PlantVillage dataset to remove background noise. Then, tiles from images were used to reduce any potential bias from leaf form. Finally, several cutting-edge tiny convolutional neural networks (CNNs), created for contexts with little processing power, were trained on a new dataset of 85 × 85 × 3 px images. MobileNet, which had a 96.31% accuracy rate, and SqueezeNet, which had a 95.05% accuracy rate, were the models that predicted the best performance. The results were then examined using Precision and Recall measures, which are important for identifying plant diseases.

Keywords: disease detection; smart farming; computer vision; texture features; artificial intelligence; hyperparameter optimization (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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