Plant Disease Detection Using Deep Learning-Based Approach

Abstract

Food security is threatening due to the exponentially growing global population. There are many reasons for food scarcity, such as exponential population, environmental dis- asters, climate change, the impact of COVID-19, and wars. Agriculture’s productivity has decreased in the last decade due to climate change and inappropriate usage of wa- ter, fertilizer, and pesticides, which stimulate plant diseases. Plant diseases and pests are also the cause of reducing the production of food all over the globe. Plant diseases cause around 20% to 40% loss in the production of agricultural products. Plant diseases extensively impact agrarian production growth. It results in a price hike on food grains and vegetables. Early detection of plant disease is essential to reduce economic loss and predict yield loss. Early perception of pathogens and insinuating proper medications are crucial to enhance crop yield and quality. Current plant disease detection involves the physical presence of domain experts to ascertain the disease. As a result, timely plant disease recognition entails sustained crop supervision from the start. Some research works have contemporarily been proposed as curative control measures. However, such an approach requires expensive equipment that is out of reach for small or middle-scale yeoman. Deep learning-based approaches vary in network architecture, and learning of the features by each model varies from one another in some aspects. To take this as an ad- vantage, this study proposed an ensemble-based deep learning approach using AlexNet, ResNet, and VGGNet. Seven different plant disease dataset is used with the binary and multiclass dataset. This ensemble-based approach enhances the classification result by minimizing the miss-classification effect. It constructively perceives plant diseases by analyzing plant leaf images. A broad set of experiments were conducted using differ- ent plant leaf image datasets such as Cardamom, Cherry, Grape, Maize, Pepper, Potato, and Strawberry to assess the agility of the proposed approach. Experiential results show that the proposed method attained a maximal detection accuracy of 100% for binary and 99.53% for multiclass datasets. Deep learning-based plant disease detection is proposed in this work by address- ing some of the challenges. Precise plant disease detection is essential, where more than one disease has similar symptoms and nature, and also to achieve excellent per- formance in spite of the imbalanced data. This study proposed a Multilevel Feature Fusion Network (MFFN), which combines the features learned at different levels of the network and also uses the adaptive attention technique by employing channel and pixel attention mechanism, which fabricates the network more robust by considering the ideeper network features which are shown in different channels and also with the pixel level features, with this the network is able to classify the diseases precisely on tomato plant dataset. The proposed deep learning-based approach is trained and tested on a tomato plant leaves dataset and achieved 99.36% training accuracy, 99.88% validation accuracy, and 99.5% external testing accuracy. It outperformed the existing approaches relevant to the tomato plant dataset. Further, this work also proposes a pesticide pre- scription module that provides pesticide information based on the type of tomato leaf disease. Plant disease detection using a complex background and images captured in differ- ent conditions is one of the challenges; this study proposed a cardamom plant disease detection approach by collecting the images in a complex background using different electronic gadgets. This study proposed a hybrid deep learning-based approach consist- ing of two stages: the background removal stage and the classification stage. U2 -Net is used for the background removal task, and EfficientNetV2 is used for the classifica- tion task. This makes the network more robust to handle the plant leaf images captured in complex nature.A large number of experiments were conducted to evaluate the pro- posed approach’s performance and compare it to other models such as EfficientNet and Convolutional Neural Network (CNN). According to the experimental results, the pro- posed approach achieved a detection accuracy of 98.26%. The approaches proposed in this study are producing prominent results. This study also suggested a pesticide prescription module for tomato plant leaf diseases. The pro- posed solutions in this study contribute to the field of plant disease detection, which can be adopted for real-time plant disease application. The overall aim of this study is to provide an efficient and robust plant disease detection approach.