Application of Machine Learning Techniques to Discern Optimal Rearing Conditions for Improved Black Soldier Fly Farming

Abstract

As the world population continues to grow, there is a need to come up with alternative sources of feed and food to combat the existing challenge of food insecurity across the globe. The use of insects, particularly the black soldier fly (BSF) Hermetia illucens (L.) (Diptera: Stratiomydiae), as a source of feed stands out due to its sustainability and reliability. Black soldier fly larvae (BSFL) have the ability to convert organic substrates to high-quality biomass rich in protein for animal feed. They can also produce biodiesel and bioplastic and have high biotechnological and medical potential. However, current BSFL production is low to meet the industry’s needs. This study used machine learning modeling approaches to discern optimal rearing conditions for improved BSF farming. The input variables studied include the cycle time in each rearing phase (i.e., the rearing period in each phase), feed formulation type, length of the beds (i.e, rearing platforms) at each phase, amount of young larvae added in the first phase, purity score (i.e, percentage of BSFL after separating from the substrate), feed depth, and the feeding rate. The output/target variable was the mass of wet larvae harvested (kg per meter) at the end of the rearing cycle. This data was trained on supervised machine learning algorithms. From the trained models, the random forest regressor presented the best root mean squared error (RMSE) of 2.91 and an R-squared value of 80.9%, implying that the model can be used to effectively monitor and predict the expected weight of BSFL to be harvested at the end of the rearing process. The results established that the top five ranked important features that inform optimal production are the length of the beds, feed formulation used, the average number of young larvae loaded in each bed, feed depth, and cycle time. Therefore, in that priority, it is expected that tuning the mentioned parameters to fall within the required levels would result in an increased mass of BSFL harvest. These data science and machine learning techniques can be adopted to understand rearing conditions and optimize the production/farming of BSF as a source of feed for animals e.g., fish, pigs, poultry, etc. A high production of these animals guarantees more food for humans, thus reducing food insecurity.

Unveiling Environmental Influences on Sustainable Fertilizer Production through Insect Farming

“…The long production time and low-quality of organic fertilizer associated with conventional composting [5] have encouraged a major shift to the use of insects in recycling organic waste since insects shorten the composting time from 12-24 weeks to only 5 weeks [6]. For instance, the black soldier fly (BSF) (Hermetia illucens (L.) (Diptera: Stratiomydiae) can accelerate the composting of organic wastes into quality organic fertilizer and nutrient-rich insect biomass that can be used as a source of feed for poultry, fish, pigs, and other animals [7][8][9][10][11]. The efficacy of BSF frass fertilizer in boosting soil health, plant defense mechanism, crop productivity, and profit margins has been clearly demonstrated [6,7,12].…”

“…Notably, the agronomic effectiveness of frass fertilizer is largely influenced by the quality in terms of nutrients and level of maturity and stability [7]. On the other hand, frass fertilizer quality depends on the type and physical-chemical properties of the insect-rearing substrates and environmental conditions (such as temperature, humidity, and aeration rate) provided during the waste valorization process [9,13]. The provision of sub-optimal conditions and low-quality substrates affects the bioconversion process and the quality of frass fertilizer generated.…”