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Neural Image Analysis for the Determination of Total and Volatile Solids in a Composted Sewage Sludge and Maize Straw Mixture
Waste management is one of most important challenges in environmental protection. Much effort is put into the development of waste treatment methods for further use. A serious problem is the treatment of municipal sewage sludge. One method that is useful for this substrate is composting. However, it is reasonable to compost a sewage sludge mixed with other substrates, such as maize straw. To carry out the composting process properly, it is necessary to control some parameters, including the total solids and volatile solids content in the composted mixture. In this paper, a method for the determination of the total solids and volatile solids content based on image analysis and neural networks was proposed. Image analysis was used for the determination of the colour and texture parameters. The three additional features describing the composted material were percentage of sewage sludge, type of maize straw, and stage of compost maturity. The neural models were developed based on various combinations of the input parameters. For both the total solids and volatile solids content, the most accurate models were obtained using all input parameters, including 30 parameters for image colour and texture and three features describing the composted material. The uncertainties of the developed models, expressed by the MAPE error, were 2.88% and 0.59%, respectively, for the prediction of the total solids and volatile solids content.
Predicting Starch Content in Early Potato Varieties Using Neural Networks and Regression Models: A Comparative Study
Starch content serves as a crucial indicator of the quality and palatability of potato tubers. It has become a common practice to evaluate the polysaccharide content directly in tubers freshly harvested from the field. This study aims to develop models that can predict starch content prior to the harvesting of potato tubers. Very early potato varieties were cultivated in the northern and northwestern regions of Poland. The research involved constructing multiple linear regression (MLR) and artificial neural network (ANN-MLP) models, drawing on data from eight years of field trials. The independent variables included factors such as sunshine duration, average daily air temperatures, precipitation, soil nutrient levels, and phytophenological data. The NSM demonstrated a higher accuracy in predicting the dependent variable compared to the RSM, with MAPE errors of 7.258% and 9.825%, respectively. This study confirms that artificial neural networks are an effective tool for predicting starch content in very early potato varieties, making them valuable for monitoring potato quality.
Advancements in plant protection - the application of machine learning to the detection of maize infestations
Plant infestations cause significant economic losses in agriculture, necessitating rapid and accurate detection for optimized agrotechnical operations and reduced environmental pollution. This study addresses this challenge by proposing a customized convolutional neural network (CNN) architecture for detecting corn leaf worm infestations in maize. The research focuses on developing unique CNN models through extensive experimentation, systematically adjusting hyperparameters like optimizers, filter numbers, and kernel sizes. The study’s main contributions include the design of an accurate CNN classifier, and its implementation in a user-friendly smartphone application. The research highlights the importance of hyperparameter tuning in CNN performance, demonstrating that optimal configurations lead to high accuracy (up to 95% for accuracy, precision, recall, specificity, and F1-score). While the current model focuses on a single pest, the findings underscore the potential of custom CNN classifiers in vision systems for automated crop inspection, offering a promising solution for minimizing crop losses and the environmental impact of chemical plant protection products.