Municipal Solid Waste (MSW) management presents a significant challenge for traditional separation practices, due to a considerable increase in quantity, diversity, complexity of types of solid waste, and a high demand for accuracy in classification. Image recognition and classification of waste using computer vision techniques allow for optimizing administration and collection processes with high precision, achieving intelligent management in separation and final disposal, mitigating environmental impact, and contributing to sustainable development objectives. This research consisted of evaluating and comparing the effectiveness of four Convolutional Neural Network models for MSW detection, using a Raspberry Pi 4 Model B. To this end, the models YOLOv4-tiny, YOLOv7-tiny, YOLOv8-nano, and YOLOv9-tiny were trained, and their performance was compared in terms of precision, inference speed, and resource usage in an embedded system with a custom dataset of 1883 organic and inorganic waste images, labeled with Roboflow by delimiting the area of interest for each object. Image preprocessing was applied, with resizing to 640 × 640 pixels and contrast auto-adjustments. Training considered 85% of images and testing considered 15%. Each training stage was conducted over 100 epochs, adjusting configuration parameters such as learning rate, weight decay, image rotation, and mosaics. The precision results obtained were as follows: YOLOv4-tiny, 91.71%; YOLOv7-tiny, 91.34%; YOLOv8-nano, 93%; and YOLOv9-tiny, 92%. Each model was applied in an embedded system with an HQ camera, achieving an average of 86% CPU usage and an inference time of 1900 ms. This suggests that the models are feasible for application in an intelligent container for classifying organic and inorganic waste, ensuring effective management and promoting a culture of environmental care in society.

Municipal Solid Waste (MSW) is a common problem in all cities worldwide; it is expected to increase to 3400 billion tons by 2050. In Mexico, an average of 108,146 tons of MSW are generated daily. Artificial Intelligence (AI) is a computer tool that allows the development of systems that facilitate the recycling process. However, most AI programs focus on classifying paper, plastic, glass and metal; therefore, wood and textile waste have received little attention. Using texture techniques such as Local Binary Pattern (LBP), Gray-Level Co-occurrence Matrix (GLCM), Histogram of Oriented Gradients (HOG), Canny/Sobel edge detection, Fractal Dimension (FD), feature values were extracted and integrated from 4396 images belonging to wood and textile categories. Using the Random Forest Importance method, the most significant features were selected to train three Machine Learning (ML) algorithms. Multilayer Perceptron (MLP) achieved the best performance in accuracy with 96.70%, followed by Random Forest (RF) at 95.45% and Support Vector Machine (SVM) with 95.22%. The implementation of these comparisons will serve as a basis for the development of new technological tools with low computational cost that carry out a proper waste separation.

Automatic classification of Ataulfo mango (Mangifera indica L.) ripeness is essential to ensure consistent quality, standardize post-harvest processes, and reduce the subjectivity of traditional visual inspection, which is unreliable and error-prone. This paper aims to develop a computationally efficient image classification system for Ataulfo mango ripeness by combining explicit color and texture feature extraction with a traditional machine learning model, thereby reducing the high computational costs typically associated with deep convolutional architectures. For this purpose, a dataset containing 10,400 images was created and divided into four maturity categories: green-ripe, partially ripe, firm-ripe, and soft-ripe. We select an optimal Multilayer Perceptron trained on compact 33-dimensional feature vectors and compare its performance with classical machine learning algorithms and pretrained deep neural networks, including MobileNetV2, MobileNetV3, and ResNet18. Our proposal achieves an accuracy of 0.8821, a macro-F1 score of 0.8784, and an AUC of 0.9751, which are better than those of classical classifiers and MobileNet-family models, while reducing computational cost by three orders of magnitude (GFLOPs). The ResNet18 model achieved a 3.56% relative improvement in macro-F1 score compared to our proposal, but its computational cost increased by four orders of magnitude in GFLOPS. In all evaluated architectures, the remaining classification errors occur between adjacent maturity stages and likely reflect the visual similarity inherent in the continuous ripening process. These findings demonstrate that manual feature engineering and model selection via hyperparameter tuning remain highly competitive and more sustainable for low-cost edge implementations in agriculture.