IoT-based Bacillus Number Prediction in Smart Turmeric Farms Using Small Datasets

—The Bacillus genus is one of the most commercially exploited bacteria in the agro-biotechnology industry, and the Bacillus information is very useful for crop growth. Most existing studies on the analysis of the amount of Bacillus were conducted in laboratories. Performing such a task on open field farming is difficult because only a small dataset is available during a long observation period for the soil analysis of Bacillus. For example, turmeric growth takes 9 months with one soil sample per month, and we found that increasing the frequency of soil analysis for turmeric growth is not practically useful. Therefore, we can only collect a very small dataset for AI training. This paper proposes the AgriTalk approach that predicts the amount of Bacillus based on novel IoT and machine learning technologies. AgriTalk uses a small dataset (5 data items) per farm for training and performs prediction for the subsequent 4 months. Good results are obtained. Specifically, the inference MAPEs (Mean Absolute Percentage Errors) range from 6.73% to 19.76%. In the experiments of five farm fields, we have correctly captured the trends for the number of changes of Bacillus . Such prediction provides useful information for fertilization management. Our prediction is more accurate for farms covered by peanut shells (the average MAPE is 13.24%) than for farms covered by rice husks (the average MAPE is 15.43%).