PLANT STEM DISEASE DETECTION USING DEEP LEARNING

In recent years, advancements in machine learning have revolutionized the field of plant biology and agriculture, particularly in the automated classification of plant stems and the identification of associated diseases. Among the various machine learning techniques, Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), Random Forest and Adaboost have emerged as prominent tools for handling complex image-based classification tasks[1]. Support Vector Machines (SVMs) are widely recognized for their ability to construct optimal hyperplanes in high-dimensional feature spaces, making them effective in tasks where the separation of classes is crucial. SVMs have been successfully applied in diverse domains, including image classification, due to their robustness and ability to generalize well with appropriate kernel functions[9]. Convolutional Neural Networks (CNNs), on the other hand, have gained immense popularity in recent years, particularly in computer vision tasks. CNNs are designed to automatically learn hierarchical representations of data, especially in the context of images, by applying convolutions over input images and progressively extracting features at different spatial levels. This characteristic makes CNNs particularly well-suited for tasks such as object recognition and image classification without the need for extensive handcrafted feature engineering[6]. Adaboost (short for Adaptive Boosting) is a popular boosting algorithm used in machine learning to improve the accuracy of binary classification models. Boosting is a technique that combines multiple weak learning algorithms to create a strong learner that can make accurate predictions. Adaboost is particularly useful when complex relationships exist between the input features and the output variable. Machine learning, a fascinating blend of computer science and statistics, has witnessed incredible progress, with one standout algorithm being the Random Forest. In the domain of stem classification and disease identification in plants, both SVMs and CNNs offer distinct advantages and trade-offs. SVMs provide a strong theoretical foundation with well-understood principles of margin maximization and kernel methods, which can be advantageous in scenarios with limited training data or when interpretability of the model is critical. On the other hand, CNNs excel in capturing intricate patterns and spatial relationships within images, which is particularly beneficial in tasks where the visual appearance of stems and subtle disease symptoms play a significant role[2]. Through empirical analysis on a diverse dataset encompassing various plant species and stem conditions, we assess the strengths and limitations of each approach, providing insights into their applicability and effectiveness in real-world agricultural and environmental monitoring scenarios. The findings presented here contribute to a deeper understanding of the capabilities of SVMs and CNNs in automated plant biology and crop management applications[1].