Sugarcane is one of the main agro-industrial products consumed worldwide, and, therefore, the use of suitable soils is a key factor to maximize its production. As a result, the need to evaluate soil matrices, including many physical, chemical, and biological parameters, to determine the soil’s aptitude for growing food crops increases. Machine learning techniques were used to perform an in-depth analysis of the physicochemical indicators of vertisol-type soils used in sugarcane production. The importance of the relationship between each of the indicators was studied. Furthermore, and the main objective of the present work, was the determination of the minimum number of the most important physicochemical indicators necessary to evaluate the agricultural suitability of the soils, with a view to reducing the number of analyses in terms of physicochemical indicators required for the evaluation. The results obtained relating to the estimation of agricultural capability using different numbers of parameters showed accuracy results of up to 91% when implementing three parameters: Potassium (K), Calcium (Ca) and Cation Exchange Capacity (CEC). The reported results, relating to the estimation of the physicochemical parameters, indicated that it was possible to estimate eleven physicochemical parameters with an average accuracy of 73% using only the data of K, Ca and CEC as input parameters in the Machine Learning models. Knowledge of these three parameters enables determination of the values of soil potential in regard to Hydrogen (pH), organic matter (OM), Phosphorus (P), Magnesium (Mg), Sulfur (S), Boron (B), Copper (Cu), Manganese (Mn), and Zinc (Zn), the Calcium/Magnesium ratio (Ca/Mg), and also the texture of the soil.

The current condition of soils is a major area of interest due to the lack of certainty in their physicochemical properties, which can guarantee the quality and the production of a specific crop. Additionally, methodologies to improve land management must be implemented in order to address the consequences of many environmental issues. To date, many techniques have been implemented to improve the accuracy—and more recently the speed—of analysis, in order to obtain results while in the field. Among those, Near Infrared (NIR) spectroscopy has been widely used to achieve the objectives mentioned above. Nevertheless, it requires particular knowledge, and the cost might be high for farmers who own the fields and crops. Thus, the present work uses a system that implements capacitance spectroscopy plus artificial intelligence algorithms to estimate the physicochemical variables of soil used to grow sugar cane. The device uses the frequency response of the soil to determine its magnitude and phase values, which are used by artificial intelligence algorithms that are capable of estimating the soil properties. The obtained results show errors below 8% in the estimation of the variables compared to the analysis results of the soil in laboratories. Additionally, it is a portable system, with low cost, that is easy to use and could be implemented to test other types of soils after evaluating the necessary algorithms or proposing alternatives to restore soil properties.

The use of fossil fuels is losing versus the use renewable energy sources such as biomass and biogas, due to the environmental impacts that they generate. In Mexico, Veracruz has an area of 7.24 x 106 hectares, representing 3.7% of the national area, being the main provider of agroindustrial products due to its diversity of ecosystems. The objective of this paper is to evaluate the bioenergy potential of organic solid waste generated from the main agroindustrial products of the state of Veracruz. To carry out this research, ten main crops of Veracruz were selected through a literature review, determining the percentage of waste generation and heating value of each of them. With the previous data, the tons of agroindustrial waste and the bioenergy potential were estimated. Finally, the total bioenergy potential of agroindustrial wastes was calculated. As part of the results, Veracruz produces approximately 25.5 x 106 tons of agroindustrial products made up of sugarcane, orange, lemon, pineapple, coffee, banana, grapefruit, watermelon, rice and pear. Derived from the ago-industrial activity, 6.97 x 106 tons of waste are generated annually, being the sugarcane waste the most with 75% equivalent to 5.28 x 106 tons, followed by citrus around 0.98 x 106 tons. Likewise, and as a consequence of agroindustrial waste, Veracruz has a bioenergy potential close to 130.00 PJ per year, which would place it as the largest supplier of renewable energy from biomass.