A method for performing a pest forecast, comprising: at a plurality of traps, capturing data comprising: pictures of trap contents and associated metadata captured for a certain period of time and other data comprising GPS coordinates of the respective trap; and sending said data (10, 10a, 10b) through communication means to a remote end located in the cloud; from the pictures of trap contents and associated metadata captured at each trap, applying a visual processing and learning stage comprising applying a predictive model for estimating a number of identified insects in each trap for said certain period of time; preprocessing at least said estimated number of identified insects in each trap, said metadata associated to the pictures and said other data captured at each trap, thus obtaining a set of preprocessed data, said preprocessing comprising applying machine learning techniques, said preprocessing comprising the generation of features encoding said estimated number of identified insects in each trap, said metadata associated to the pictures and said other data captured at each trap, in a form for processing with machine learning and decision making algorithms; storing the pre-processed data in data storage means, thus keeping historical data along time; from the recently preprocessed data and the historical data, performing a pest forecast for each trap for the next N days by applying decision making algorithms, wherein performing a pest forecast comprises: feeding a machine learning model with said historical data and obtaining results of the executed model; making numerical or qualitative predictions (29, 38) from the result of applying said machine learning model; storing said predictions in data storage means; enriching said decision making algorithms with said predictions. at a plurality of traps, capturing data comprising: pictures of trap contents and associated metadata captured for a certain period of time and other data comprising GPS coordinates of the respective trap; and sending said data (10, 10a, 10b) through communication means to a remote end located in the cloud; from the pictures of trap contents and associated metadata captured at each trap, applying a visual processing and learning stage comprising applying a predictive model for estimating a number of identified insects in each trap for said certain period of time; preprocessing at least said estimated number of identified insects in each trap, said metadata associated to the pictures and said other data captured at each trap, thus obtaining a set of preprocessed data, said preprocessing comprising applying machine learning techniques, said preprocessing comprising the generation of features encoding said estimated number of identified insects in each trap, said metadata associated to the pictures and said other data captured at each trap, in a form for processing with machine learning and decision making algorithms; storing the pre-processed data in data storage means, thus keeping historical data along time; from the recently preprocessed data and the historical data, performing a pest forecast for each trap for the next N days by applying decision making algorithms, wherein performing a pest forecast comprises: feeding a machine learning model with said historical data and obtaining results of the executed model; making numerical or qualitative predictions (29, 38) from the result of applying said machine learning model; storing said predictions in data storage means; enriching said decision making algorithms with said predictions. feeding a machine learning model with said historical data and obtaining results of the executed model; making numerical or qualitative predictions (29, 38) from the result of applying said machine learning model; storing said predictions in data storage means; enriching said decision making algorithms with said predictions.