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 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 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 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 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 from the result of applying said machine learning model ; storing said predictions in data storage means ; enriching said decision making algorithms with said predictions . <EOS>