The world population has increased by more than 7.5 billion in recent years. This condition has posed a significant challenge to achieving and maintaining food security. As the demand for food is growing, prevention to provide food supply is required. Plant leaf disease has a devastating effect on crop yield, leading to reduced food availability and affecting nutrition and health. Moreover, plant disease can have a severe impact in developing countries where agriculture is the primary source of livelihood. Early detection to prevent the disease from spreading in agricultural areas is vital for maintaining food supply. The advanced detection of plant leaf disease has contributed significantly to the management strategies.
In this dissertation, the exploration of plant leaf disease detection is presented. At first, we explore the effectiveness of hybrid feature fusion in the classification of coffee leaf diseases. It investigates different model selection techniques to determine the optimal fusion of multiple features for disease classification. The results demonstrate the significance of feature fusion in achieving improved accuracy in coffee leaf disease identification.
Secondly, we introduce DFNet, a convolutional neural network based on dense fusion specifically designed to identify plant leaf disease. DFNet leverages the power of dense connections and fusion strategies to enhance the learning capabilities of the network. Through experimental evaluation, the DFNet outperforms other benchmarks and existing architectures regarding the accuracy and other metrics, demonstrating its efficacy in accurate plant leaf disease classification.
In the end, a study on enhancing the accuracy of plant leaf disease detection through autonomous deep learning is presented. It proposes an autonomous deep learning framework that can self-regulate the number of nodes and layers in the classification layers. By integrating the autonomous deep learning approach into the disease detection pipeline, the proposed network reports significant improvements in accuracy compared to traditional methods and existing methods.

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