Abstract: A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.

Abstract: Methods of producing sugar cane transplant units that includes planting sugar cane propagation materials in a non-segregated planting medium to produce a sugar cane where the planting of the sugar cane propagation materials results in an emerged plant density of 25 to 200 plants per square meter; growing the sugar plant to an age of at least 4 months; harvesting the stalks of the sugar cane plant when the stalks have a length of 10 to 100 centimeters; cutting the harvested stalks into stalk segments, wherein the stalk segments are cut to a length of 1 to 5 centimeters; and planting one or more of the stalk segments into a planting container that has a volume from 10 to 200 cubic centimeters.

Abstract: A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.

Abstract: A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.

Abstract: A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.

Abstract: Methods of producing sugar cane transplant units that includes planting sugar cane propagation materials in a non-segregated planting medium to produce a sugar cane where the planting of the sugar cane propagation materials results in an emerged plant density of 25 to 200 plants per square meter; growing the sugar plant to an age of at least 4 months; harvesting the stalks of the sugar cane plant when the stalks have a length of 10 to 100 centimeters; cutting the harvested stalks into stalk segments, wherein the stalk segments are cut to a length of 1 to 5 centimeters; and planting one or more of the stalk segments into a planting container that has a volume from 10 to 200 cubic centimeters.

Abstract: A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.