Research on student performance prediction has evolved from the early application of statistical techniques to later use of computational techniques. Results in this field are varied, thus, we have to take advantage of previous research results. This study proposes a Multi-layer Adaptive Neuro-Fuzzy Inference System (MANFIS) for student performance prediction in online Higher Education settings. The MANFIS was trained and tested using a dataset integrated by the scores obtained by students in four online Higher Education courses. The MANFIS prediction accuracy was compared against the accuracies of Multilayer neural network, Radial Basis Function Neural Network, and General Regression Neural Network. The accuracy of the MANFIS prediction statistically outperformed at least one neural network (out of three possible) in each dataset. The Results indicate that MANFIS is an alternative model to predict student performance in online Higher Education settings.

Agriculture is essential for food production and raw materials. A key aspect of this sector is harvest, the stage at which the commercial part of the plant is separated. Timely harvesting minimizes post-harvest losses, preserves product quality, and optimizes production processes. Globally, a substantial amount of food is wasted, impacting food security and natural resources. To address this problem, an Adaptive Neuro-Fuzzy Inference System was developed to predict timely harvesting in crops. Stevia, a native plant from Brazil and Paraguay, with an annual production of 100,000 to 200,000 tons and a market of 400 million dollars, is the focus of this study. The system considers soil pH, Brix Degrees, and leaf colorimetry as inputs. The output is binary: 1 indicates timely harvest and 0 indicates no timely harvest. To assess its performance, Leave-One-Out Cross-Validation was used, obtaining an $r^2$ of 0.99965 and an Absolute Residual Error of 0.00064305, demonstrating its accuracy and robustness. In addition, an interactive application that allows farmers to evaluate crop status and optimize decision-making was developed.

Stevia rebaudiana Bertoni Morita II, a perennial plant native to Paraguay and Brazil, is also widely cultivated in the state of Colima, Mexico, for its use as a sweetener in food and beverages. The optimization of soil parameters is crucial for maximizing biomass production and stevioside levels in stevia crops. This research presents the development and implementation of a monitoring system to track essential soil parameters, including pH, temperature, humidity, electrical conductivity, nitrogen, phosphorus, and potassium. The system employs a wireless sensor network to collect quasi-real-time data, which are transmitted and stored in a web-based platform. A Mamdani-type fuzzy logic model is utilized to process the collected data and provide farmers an integrated assessment of soil quality. By comparing the quality data output of the fuzzy logic model with a linear regression model, the system demonstrated acceptable performance, with a determination coefficient of 0.532 for random data and 0.906 for gathered measurements. The system enables farmers to gain insights into the soil quality of their stevia crops and empowers them to take preventive and corrective actions to improve the soil quality specifically for stevia crops.

Agriculture is essential for food production and raw materials. A key aspect of this sector is harvest, the stage at which the commercial part of the plant is separated. Timely harvesting minimizes post-harvest losses, preserves product quality and optimizes production processes. Globally, a substantial amount of food is wasted, impacting food security and natural resources. To address this problem, an Adaptive Neuro-Fuzzy Inference System was developed to predict timely harvesting in crops. Stevia, a native plant from Brazil and Paraguay, with an annual production 100,000 to 200,000 tons and a market of 400 million dollars, is the focus of this study. The system considers soil pH, Brix Degrees and leaf colorimetry as inputs. The output is binary: 1 indicates timely harvest and 0 indicates no timely harvest. To assess its performance, Leave One Out Cross Validation was used, obtaining an r² of 0.99965 and a Residual Absolute Error of 0.00064305, demonstrating its accuracy and robustness. In addition, an interactive application that allows farmers to evaluate crop status and optimize decision making was developed.

Stevia Rebaudiana Bertoni Morita II, a perennial plant native to Paraguay and Brazil, is also widely cultivated in the state of Colima, Mexico, for its use as a sweetener in food and beverages. The op-timization of soil parameters is crucial for maximizing biomass production and stevioside levels in stevia crops. This research presents the development and implementation of a monitoring system to track essential soil parameters, including pH, temperature, humidity, electrical conductivity, ni-trogen, phosphorus, and potassium. The system employs a wireless sensor network to collect qua-si-real-time data, which is transmitted and stored in a web-based platform. A Mamdani-type fuzzy logic model is utilized to process the collected data and provide to farmers an integrated assessment of soil quality. By comparing the quality data output of the fuzzy logic model with a line-ar-regression model, the system demonstrated acceptable performance, with a determination coef-ficient of 0.532 for random data and 0.906 for gathered measurements. The system enables farmers to gain insights into the soil quality of their stevia crops and empowers them to take preventive and corrective actions to improve the soil quality specifically for stevia crops.

COVID-19 has had a disproportionate impact on the people of Tamil Nadu-a state on the southeast coast of India. Forty percent of the population farms land that is 5 acres in size on average. The pool of available truck drivers to deliver produce from farms to markets has shrunk substantially due to restrictions and fears of COVID transmission. Many regional markets have also closed due to COVID fears. As a result, farmers are forced to sell their produce locally at substantially lower prices, that are unsustainable in providing basic income. Even prior to COVID, the small size of Indian farms are not conducive for economies of scale, equipment/technology investments or supply chain efficiencies. Farmers also have limited ability to engage technology due to a lack of education and resources. Improvements to the agriculture supply chain in India are critical, both during COVID and in order to build a more sustainable future for farmers. This paper proposes a Micro Agriculture Logistics System which leverages existing technologies to facilitate transportation and distribution through a community-based infrastructure. The solution will increase efficiencies in connecting drivers with farmers, identify alternative transportation modes, decrease spoilage, and digitally deliver payments throughout the value chain.