Abstract
Artificial intelligence (AI) has been a growing field in recent years, with the development of deep learning (DL) techniques providing new opportunities for plant disease classification. Convolutional neural networks (CNNs) and other advanced techniques such as transfer learning, deep ensemble learning, and others have been used to classify plant diseases with high accuracy. However, these techniques are not only complex but also challenging to implement, making it important to provide a comprehensive understanding of their use in plant disease classification. This chapter aims to explore the use of deep learning techniques for plant disease classification. It will provide an overview of the various DL techniques and their applications in the classification of plant diseases. It will also provide a comprehensive understanding of transfer learning, deep ensemble learning, and other advanced methods in plant disease classification. Additionally, the chapter will provide case studies to illustrate the practical applications of DL techniques in plant disease classification.
TopIntroduction
Every year, the population of the Earth grows by approximately 1.6%, leading to a heightened demand for plant-based products Ensuring crops are protected from diseases is important in meeting this increasing demand for food, not just in terms of quantity but also quality (Strange, R. N et al., 2005). The impact of plant diseases on the global economy is significant, costing around US$220 billion annually. The agricultural sector, which is crucial to India's economic development, drives the nation's economy in a significant way. With a contribution of 15.87% to the country's Gross Domestic Product (GDP), agriculture has significant weight in shaping the overall economic development of India. Additionally, this sector employs a large portion of the nation's /workforce, with an estimated 54.15% of the population being engaged in agriculture-related activities. In India, the loss of more than 35% of crop productivity due to pests and diseases, as reported by the Indian Council of Agricultural Research, puts food security at risk in light of the increasing number of pests and diseases (Uthayakumar et al., 2017).
Plants can be plagued by diseases resulting from various agents such as fungi, bacteria, viruses, pests, and others. These diseases can exhibit symptoms like leaf spots, blights, root rot, fruit rot, fruit spots, wilting, dieback and decline. This significantly impacts food security by reducing crop yields, leading to food scarcity and even starvation in certain regions. Plant diseases have risen, severely impacting agriculture and food security. Early identification of these diseases is essential for controlling their spread and treating affected plants. It is also a key factor in making informed decisions in agriculture production. Plant diseases usually exhibit distinctive marks or lesions on leaves, flowers, or fruits, and each disease has its own specific pattern for diagnosis. Leaves are the primary focus for detecting plant diseases, as most symptoms show up there.
The traditional method involves physically examining the leaves and crowns of plants for signs of disease. This can be a time-consuming and specialized process, particularly because different types of crops can exhibit different symptoms. The machine learning method, on the other hand, involves using algorithms to classify diseases based on pre-processed images of the plants.
Implementing machine learning involves a sequence of actions that segregate the infected areas via image preprocessing, extracting meaningful features from these images, and applying classification techniques like support vector machines, K-nearest neighbor, or random forest to classify the diseases founded on these features. Image characteristics, such as leaf texture, type, and color, are the frequently utilized features. (Lu, J et al., 2021).
The exposure of several AI techniques has resulted in the recognition of several methods for early disease detection. Among these AI techniques, Deep Learning (DL) technologies are advantageous due to their ability to extract and recognize features using a CNN model, thereby enabling automatic detection of plant leaf diseases. Deep learning has various applications in agriculture, including: Crop classification and yield prediction, Pest and disease detection, Soil moisture and nutrient mapping and Weather forecasting. This chapter will explore the classification of plants using Transfer Learning, Deep ensemble learning and some advanced techniques.
Key Terms in this Chapter
Optimization: Refers to the process of finding the best possible features for a given problem or objective, subject to constraints and limitations.
Image Processing: Image processing is a field of study that involves the analysis, manipulation, and enhancement of digital images using mathematical algorithms and computational techniques.
Feature Extraction: It is the process of automatically extracting or selecting a subset of relevant features or patterns from raw data, such as images or signals, to facilitate further analysis or classification. Its goal is to reduce the dimensionality of the data while preserving the most important information for downstream tasks.
Edge Computing: Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed.
Augmentation: It is a technique to artificially increase the size of a training dataset by creating modified copies of the original data. This is often done by applying various transformations, such as rotations, flips, and color changes, to the original data.
Object Detection: It is a computer vision technique that involves automatically identifying and localizing objects within an image or video frame.
Artificial Intelligence (AI): It is a field of computer science that focuses on developing intelligent machines that can perform tasks that typically require human intelligence, such as learning, problem-solving, perception, and decision-making.
Convolutional Neural Network (CNN): It is a type of deep neural network that is commonly used in computer vision tasks such as image recognition and classification. It uses convolutional layers to automatically learn and extract features from images.
Computer Vision: It is a field of artificial intelligence that focuses on enabling machines to interpret and understand visual data from the world. It involves developing algorithms and techniques to enable machines to see, perceive, and recognize objects, patterns, and features in images and videos.
Overfitting: Overfitting refers to a situation where a machine learning model is excessively complex and performs well on training data, but poorly on new, unseen data.
Neural Network: It is a type of machine learning algorithm modeled after the structure and function of the human brain, consisting of interconnected nodes that work together to process input data and produce output predictions.
Generative Adversarial Network (GAN): GAN is used in unsupervised machine learning to generate new data samples that are similar to a given dataset. It consists of two parts: a generator network that creates new samples, and a discriminator network that evaluates the authenticity of the generated samples.
Machine Learning (ML): ML is a subset of AI that involves training algorithms to make predictions or decisions based on input data, rather than being explicitly programmed to perform a specific task.
Transfer Learning: It refers to the process of leveraging knowledge from one task to another related task, allowing a model to learn more efficiently and effectively with less training data. It involves using a pre-trained model as a starting point and adapting it to a new problem domain.
Deep Learning (DL): DL is a subset of machine learning that uses artificial neural networks to model and solve complex problems by learning from large amounts of data.
Unsupervised Learning: An algorithm learns from unlabeled data to discover hidden patterns or relationships without being given specific output labels or feedback. The goal is to find structure or patterns in the input data.
Supervised Learning: It is a type of machine learning in which an algorithm learns from labeled data to make predictions or decisions about new, unseen data. The goal is to learn a mapping function from the input data to the output data.
Classification: An algorithm is trained to predict a category or class for new input data based on patterns it learned from labeled examples. The algorithm tries to categorize new data into pre-defined groups or classes.