Agricultural productivity is an important factor for the economic development of a country. Therefore, the diagnosis of plant diseases is a field of research of utmost importance for the agricultural sector as it allows us to help recommend strategies to avoid the spread of diseases, thus reducing economic losses. Currently, with the rise of computer systems, computer systems have been developed that allow computer-assisted diagnosis in different research fields, including the agricultural sector. This work proposes the development of a methodology that allows the detection of three types of diseases in tomato leaves (late blight, tomato mosaic virus and Septoria leaf spot) by image analysis and pattern recognition. The methodology is divided into three stages: (1) segmentation of the leaf and of the lesion, (2) feature extraction using color moments and Gray Level Co-occurrence Matrix (GLCM) and (3) classification. For the segmentation process, it is proposed to use a range of pixel colors that represent healthy and diseased areas in tomato leaves using values proposed by an expert in the area of phytopathology. For the classification it is proposed to use a decision rule in which if two of the Support Vector Machines (SVM) classifiers, K Nearest Neighbors (K-NN) and Multilayer Perceptron (MLP) give the same result, then this is taken for the final decision. The result of the methodology is compared with other classifiers using the value of its accuracy and validated with cross validation.

Any cancer type is one of the leading death causes around the world. Skin cancer is a condition where malignant cells are formed in the tissues of the skin, such as melanoma, known as the most aggressive and deadly skin cancer type. The mortality rates of melanoma are associated with its high potential for metastasis in later stages, spreading to other body sites such as the lungs, bones, or the brain. Thus, early detection and diagnosis are closely related to survival rates. Computer Aided Design (CAD) systems carry out a pre-diagnosis of a skin lesion based on clinical criteria or global patterns associated with its structure. A CAD system is essentially composed by three modules: (i) lesion segmentation, (ii) feature extraction, and (iii) classification. In this work, a methodology is proposed for a CAD system development that detects global patterns using texture descriptors based on statistical measurements that allow melanoma detection from dermoscopic images. Image analysis was carried out using spatial domain methods, statistical measurements were used for feature extraction, and a classifier based on cellular automata (ACA) was used for classification. The proposed model was applied to dermoscopic images obtained from the PH2 database, and it was compared with other models using accuracy, sensitivity, and specificity as metrics. With the proposed model, values of 0.978, 0.944, and 0.987 of accuracy, sensitivity and specificity, respectively, were obtained. The results of the evaluated metrics show that the proposed method is more effective than other state-of-the-art methods for melanoma detection in dermoscopic images.

Early detection of plant diseases is key to ensuring food production, reducing economic losses, minimizing the use of agrochemicals, and maintaining the sustainability of the agricultural sector. Citrus plants, an important source of vitamin C, fiber, and antioxidants, are among the world’s most significant fruit crops but face threats such as canker and Huanglongbing (HLB), incurable diseases that require management strategies to mitigate their impact. Manual diagnosis, although common, I s imprecise, slow, and costly; therefore, efficient alternatives are emerging to identify diseases from early stages using Artificial Intelligence techniques. This study evaluated four deep learning models, specifically convolutional neural networks. In this study, we evaluated four convolutional neural network models (DenseNet121, ResNet50, EfficientNetB0, and MobileNetV2) to detect canker and HLB in citrus leaf images. We applied preprocessing and data-augmentation techniques; transfer learning via selective fine-tuning; stratified k-fold cross-validation; regularization methods such as dropout and weight decay; and hyperparameter-optimization techniques. The models were evaluated by the loss value and by metrics derived from the confusion matrix, including accuracy, recall, and F1-score. The best-performing model was EfficientNetB0, which achieved an average accuracy of 99.88% and the lowest loss value of 0.0058 using cross-entropy as the loss function. Since EfficientNetB0 is a lightweight model, the results show that lightweight models can achieve favorable performance compared to robust models, models that can be useful for disease detection in the agricultural sector using portable devices or drones for field monitoring. The high accuracy obtained is mainly because only two diseases were considered; consequently, it is possible that these results do not hold in a database that includes a larger number of diseases.

COVID-19, also known as coronavirus disease, is caused by the SARS-CoV-2 virus. People infected with COVID-19 may show a range of symptoms from mild to severe, including fever, cough, difficulty breathing, tiredness, and nasal congestion, among others. The goal of this study is to use machine learning to identify if a person has COVID-19 based on their symptoms and to predict how severe their illness might become. This could lead to outcomes like needing a ventilator or being admitted to an Intensive Care Unit. The methods used in this research include Artificial Neural Networks (specifically, Multi-Layer Perceptrons), Classification and Regression Trees, and Random Forests. Data from the National Epidemiological Surveillance System of Mexico City was analyzed. The findings indicate that the Multi-Layer Perceptron model was the most accurate, with an 87.68% success rate. It was best at correctly identifying COVID-19 cases. Random Forests were more effective at predicting severe cases and those requiring Intensive Care Unit admission, while Classification and Regression Trees were more accurate in identifying patients who needed to be put on a ventilator.

At the end of 2019, a type of pneumonia of unknown origin was detected in Wuhan, China. It was later determined that the illness was caused by the SARS-CoV-2 virus, and in 2020, the World Health Organization designated this disease as COVID-19. Various efforts have been made to enable timely detection of COVID-19 within the field of computational systems. This study proposes detecting COVID-19 based on the symptoms experienced by patients, utilizing data provided by the National Epidemiological Surveillance System (SINAVE) of Mexico City, from which 403,185 records were used. In situations where positive cases of COVID-19 are detected, it is predicted whether it will be a serious case in which the patient needs to be intubated or admitted to the Intensive Care Unit. For this, classification and regression decision trees (CART) are used. Different parameters were considered to define the CART model, and the stepwise variable selection process was also used to determine the significant variables that offer the best results, obtaining an accuracy of 87.04%. This study shows progress in the detection of COVID-19 using only the symptoms presented by the patients.