A method for identifying pre-harvest latent infection in a plant, the method comprising: obtaining, by a processor, data describing a level of expression of one or more infection biomarkers present in the plant, the data including a list of one or more variants, the infection biomarkers indicating a likelihood of infection in the plant, wherein the one or more variants describe differences between a read sequence of the plant and a reference genome of a healthy plant of a same type as the plant, wherein the read sequence of the plant represents an order of nucleotides in a nucleic acid of the plant; selecting, by the processor, one or more machine learning models based on the obtained data, wherein the one or more machine learning models were previously trained, using data that correlates other data and determined one or more infection biomarkers of one or more other plants, to generate output indicating a likelihood that the plant is developing a latent infection pre-harvest, wherein the one or more machine learning models were trained using a process comprising: inputting, from a training data set and into the one or more machine learning models, (i) non-invasive measurements of the one or more other plants, (ii) invasive measurements of the one or more other plants, (iii) known plant information for the one or more other plants, and (iv) affirmative infection identifications for the one or more other plants, and determining predicted likelihoods that the one or more other plants are developing the latent infection pre-harvest based on inputting (i)-(iv) into the one or more machine learning models, and outputting the one or more machine learning models for runtime use; generating, by the processor, output indicating the likelihood that the plant is developing the latent infection pre-harvest based on applying the one or more machine learning models to the data; determining, by the processor, that the plant has a latent infection based on the output exceeding a predetermined threshold range; determining, by the processor, one or more operations to mitigate the latent infection in the plant; and outputting, by the processor, an indication that the plant has the latent infection and the one or more determined operations. obtaining, by a processor, data describing a level of expression of one or more infection biomarkers present in the plant, the data including a list of one or more variants, the infection biomarkers indicating a likelihood of infection in the plant, wherein the one or more variants describe differences between a read sequence of the plant and a reference genome of a healthy plant of a same type as the plant, wherein the read sequence of the plant represents an order of nucleotides in a nucleic acid of the plant; selecting, by the processor, one or more machine learning models based on the obtained data, wherein the one or more machine learning models were previously trained, using data that correlates other data and determined one or more infection biomarkers of one or more other plants, to generate output indicating a likelihood that the plant is developing a latent infection pre-harvest, wherein the one or more machine learning models were trained using a process comprising: inputting, from a training data set and into the one or more machine learning models, (i) non-invasive measurements of the one or more other plants, (ii) invasive measurements of the one or more other plants, (iii) known plant information for the one or more other plants, and (iv) affirmative infection identifications for the one or more other plants, and determining predicted likelihoods that the one or more other plants are developing the latent infection pre-harvest based on inputting (i)-(iv) into the one or more machine learning models, and outputting the one or more machine learning models for runtime use; inputting, from a training data set and into the one or more machine learning models, (i) non-invasive measurements of the one or more other plants, (ii) invasive measurements of the one or more other plants, (iii) known plant information for the one or more other plants, and (iv) affirmative infection identifications for the one or more other plants, and determining predicted likelihoods that the one or more other plants are developing the latent infection pre-harvest based on inputting (i)-(iv) into the one or more machine learning models, and outputting the one or more machine learning models for runtime use; generating, by the processor, output indicating the likelihood that the plant is developing the latent infection pre-harvest based on applying the one or more machine learning models to the data; determining, by the processor, that the plant has a latent infection based on the output exceeding a predetermined threshold range; determining, by the processor, one or more operations to mitigate the latent infection in the plant; and outputting, by the processor, an indication that the plant has the latent infection and the one or more determined operations.