Indonesia’s cocoa processing industry has a high demand for fermented cocoa but only about 49% of the fermented cocoa is available. Fermentation is critical because it kills the cotyledons and generates aroma precursors. Indonesian farmers, on the other hand, are hesitant to ferment due to the lengthy fermentation process (6-7 days). The purpose of this study is to conduct a review of several modified cocoa fermentation techniques to determine how they can be used to shorten the fermentation time. The database was compiled from articles published in peer-reviewed journals on cocoa fermentation. The literature search was conducted using the website openknowledgemaps.org, which utilized Pubmed (life science) to discover full-text articles published in English between January 1990 and January 2021. The results indicated that fermentation time could be decreased by (1) adding inoculum, (2) determining the pH and temperature required to activate an intracellular enzyme, (3) Addition of external enzymes during fermentation, and (4) reducing the pulp content. Combining these methods to improve fermentation techniques can enhance the quality of farmers’ cocoa beans in a shorter processing time.

The alternative solution to the lack of availability of shallot tuber as planting material is TSS (True Seed of Shallot) technology. The purpose of the study is to determine the growth and yield of several seed shallots (TSS) varieties grown on dry land in Sigi, Central Sulawesi. The research was conducted on farmers’ land in Kotarindau Village, Dolo subdistrict, Sigi from June to October 2018. The study used a Randomized Block Design (RBD) in 4 (four) varieties of Bima, Trisula, Lokananta and Sanren in 5 (five) replications. Observations included plant height, number of leaves, number of tubers per clump, tuber weight per clump, and productivity (t/ha). Data were analyzed using Analysis of Variance (ANOVA) and Duncan New Multiple Range Test (DMRT) at 5%. The results show that the Lokananta variety provide the best growth and yield on all parameters observed (plant height = 36.22 cm, number of leaves = 12.56, number of tubers per clump = 2.44 tubers, tuber weight per clump = 40.43 g and productivity 21.84 t/ha) compared to three other varieties.

Shallot (Allium ascalonicum L.) cultivation is conducted on dry land and requires irrigation. A pressurized irrigation system which is highly water efficiency was suitable in on dry land. The study purpose is to determined irrigation system effect on the growth and yield of shallot. The research location was Kotarindau Village, Dolo subdistrict, Sigi in April-June 2019. The study used a Randomized Block Design (RBD) in 3 (three) treatment of irrigation systems (I1 = permanent sprinkler irrigation, I2 = portable sprinkler irrigation and I3 = conventional irrigation) in 5 (five) repetitions. The shallot variety is Tajuk. Observations were plant height, tillers, plants and tubers in fresh weight, plants and tubers in dry weight, and yields. Data were analyzed using variance at 5% and DMRT test. The results showed that the application of permanent sprinkler irrigation systems (except plant height and number of tillers) gave the best results when compared to portable sprinkler watering systems and conventional irrigation.

The application of artificial intelligence (AI) in modern agriculture has attracted increasing attention since its automation has the potential to accelerate food production with efficiency in resource use. Fuzzy logic, as one AI method, can be applied in hydroponics as an automation function of a nutrient mixing machine. There have been some inventions of nutrient mixing machines in commercial-scale agribusiness but not yet at the level of the small and medium farms that are mostly found in developing countries. This study constructed a hydroponics nutrient mixing machine employing a fuzzy logic method, calculated the machine’s efficiency, and evaluated its economic application. The automated nutrient mixing machine using fuzzy logic was efficient, and both theoretical field capacity and actual field capacity indicators were higher with the use of the nutrient mixing machine compared to manual nutrient mixing. This machine saves 78% of the labor normally used for mixing nutrients, with a saving of up to 42.86% in the nutrients used compared with mixing manually.