Journal
BIOLOGY-BASEL
Volume 11, Issue 12, Pages -Publisher
MDPI
DOI: 10.3390/biology11121732
Keywords
agricultural plant; plant leaf disease classification; 2D discrete wavelet transform; 2D signal processing; flower pollination optimization; artificial intelligence; real-time detection
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This study develops a new hybrid classification model using machine learning and deep learning algorithms, supported by swarm optimization-based feature selection, for real-time classification of diseases in apple, grape, and tomato plants. Experimental results show that the model achieves high accuracy in real-time classification of plant leaf diseases.
Simple Summary Plant disease, defined as an abnormal condition that disrupts the normal growth of the plant, is one of the main causes of economic losses in the agricultural industry. Early diagnosis of plant disease is critical to increasing agricultural crop productivity. In this paper, a new robust hybrid classification model based on swarm optimization-supported feature selection, including machine learning and deep learning algorithms, that allows real-time classification of diseases in apple, grape, and tomato plants has been developed. In this way, it will be possible to diagnose the plant disease at an early phase and apply the appropriate treatment. The early detection and prevention of plant diseases that are an important cause of famine and food insecurity worldwide are very important for increasing agricultural product productivity. Not only the early detection of the plant disease but also the determination of its type play a critical role in determining the appropriate treatment. The fact that visual inspection, which is frequently used in determining plant disease and types, is tiring and prone to human error, necessitated the development of algorithms that can automatically classify plant disease with high accuracy and low computational cost. In this study, a new hybrid plant leaf disease classification model with high accuracy and low computational complexity, consisting of the wrapper approach, including the flower pollination algorithm (FPA) and support vector machine (SVM), and a convolutional neural network (CNN) classifier, is developed with a wrapper-based feature selection approach using metaheuristic optimization techniques. The features of the image dataset consisting of apple, grape, and tomato plants have been extracted by a two-dimensional discrete wavelet transform (2D-DWT) using wavelet families such as biorthogonal, Coiflets, Daubechies, Fejer-Korovkin, and symlets. Features that keep classifier performance high for each family are selected by the wrapper approach, consisting of the population-based metaheuristics FPA and SVM. The performance of the proposed optimization algorithm is compared with the particle swarm optimization (PSO) algorithm. Afterwards, the classification performance is obtained by using the lowest number of features that can keep the classification performance high for the CNN classifier. The CNN classifier with a single layer of classification without a feature extraction layer is used to minimize the complexity of the model and to deal with the model hyperparameter problem. The obtained model is embedded in the NVIDIA Jetson Nano developer kit on the unmanned aerial vehicle (UAV), and real-time classification tests are performed on apple, grape, and tomato plants. The experimental results obtained show that the proposed model classifies the specified plant leaf diseases in real time with high accuracy. Moreover, it is concluded that the robust hybrid classification model, which is created by selecting the lowest number of features with the optimization algorithm with low computational complexity, can classify plant leaf diseases in real time with precision.
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