ABSTRACT The low quality of the water currently used for irrigation purposes harms the quality of the products and can lead to a reduction in production. Thus, the objective of this work was to verify the effect of saline water in irrigation water, on biometric variables of pumpkin crop using a system based on fuzzy rules. The agronomic experimental part of this work was carried out in a greenhouse. The experimental design was in randomized blocks, with 5 doses of salinity (0, 1.25, 2.5, 3.75, and 5 dS m-1) and with 5 repetitions. The salinity doses and evaluations carried out throughout the cycle (days after transplanting) were defined as input variables in the mathematical model. For the output variables, the collected biometric responses were defined: number of leaves, number of flowers, leaf fresh mass, leaf dry mass, stem fresh mass, stem length, root fresh mass, and root length. After evaluation, the mathematical model was developed and its validation was carried out using statistical methods and Receiver Operating Characteristic Curves (ROC). It was observed that salinity affects the bush pumpkin crop with a reduction in the evaluated parameters. The mathematical model proved to be efficient for this evaluation.

ABSTRACT Brazil is currently the 4th world’s largest banana producer, producing around 7 million tons. In this scenario, several studies have been developed with a large amount of data, such as climatic, morphological, and nutritional data, in an attempt to improve these numbers even further. This study aims to classify banana ripening stages by artificial neural networks (ANN) as a function of plant physical, physicochemical, and biochemical parameters. The used ANN consisted of a three-layer feedforward backpropagation network, with eight neurons in the input layer (physical, physicochemical, and biochemical parameters), ten neurons in the intermediate layer, and two neurons in the output layer (classification of banana ripening stages). The results showed three configurations. ANN presented an excellent result for the training phase, with 100% accuracy in the sample classification for the three configurations. The validation and testing phases, that is, the classification of samples that were not part of the training, showed 91.6% and 94.4% accuracy in the first and second configurations, respectively, and 89.5% accuracy in the third configuration.

ABSTRACT The present work aimed to develop mathematical fuzzy models to evaluate the effects of different doses of sewage sludge and irrigation with wastewater and potable water. Such models were elaborated from an experiment carried out at the Faculty of Agronomic Sciences, in the Department of Soil and Environmental Resources, from the Sao Paulo State University, in Brazil. The experiment was carried out in a randomized block design, in a 6 × 2 factorial scheme, with 6 doses of sewage sludge (0, 25, 50, 75, 100 and 125 of the recommended dose of N), and in the presence and absence of wastewater. In the development of the fuzzy model, the Mamdani method was used for the defuzzification. As input variables, the doses of sewage sludge and the types of water were used. For the output variables, it was sought to evaluate the biometric and developmental components of the culture. It can be inferred that the model developed presented a good fit when compared to the regression model, and that the use of sewage sludge may prove to be a potential future replacement of mineral nitrogen.