Integration of Advanced Design Patterns in Deep Learning for Agriculture Along With Waste Processing

Abstract

Over the past few decades, there has been a tremendous development in machine learning (ML), particularly in the areas of deep learning (DL) and transfer learning (TL). Deep learning has emerged as a powerful approach for solving complex problems in various domains such as computer vision, natural language processing, and speech recognition. At the same time, transfer learning has proven to be an effective technique for leveraging pre-trained deep learning models in new application domains with limited data. Design patterns, that are formalized best practices, offer a way to capture common problems and provide reusable solutions using generic and well-proven machine learning designs. This chapter aims to provide an overview of the advancements in deep learning and transfer learning, while emphasizing the significance of design patterns in addressing common challenges during the design of machine learning applications and systems. This work explores the implementation and results of various machine learning models on the mushroom classification dataset. The dataset comprises descriptions of 23 species of gilled mushrooms, with diverse features like cap shape, color, odor, and more. The goal was to classify mushrooms as edible, poisonous, or of unknown edibility. Among the models considered, the multi-layer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), autoencoders, and Boltzmann machine were trained and evaluated. The MLP, RNN, and LSTM exhibited exceptional performance, achieving perfect training and testing accuracies of 1.0000. These models successfully learned the underlying patterns and features, resulting in accurate predictions on both training and shown test data. Deep learning can optimize mushroom waste processing by classifying waste types, optimizing composting conditions, and extracting nutrients for reuse, enhancing sustainability and resource recovery in agriculture. It also predicts market demand, automates quality control, and facilitates predictive maintenance, improving efficiency and reducing environmental impact.