Palm Oil Plantation Fruit Inventory Counting
Targeted >85% AI counting accuracy for individual fresh fruit bunches in visually complex foliage where CCTV was infeasible
A regional palm oil producer replaced fatigue-prone manual fresh-fruit-bunch counting with a Tictag-built augmented-AI counting solution combining crowdsourced annotation and ML automation, targeting >85% counting accuracy.
Our client had observed significant inventory discrepancies, particularly in tracking the oil palm fresh fruit bunches known in the industry as FFB) harvested from each plantation. The primary challenge stemmed from the fact that individual oil palm fruit bunches are difficult to distinguish when grouped together due to their hairy texture and clustered appearance. Human counters, responsible for tracking the harvested fruit, faced issues such as eye fatigue, which increased counting errors.
These inventory discrepancies not only hindered the company's operational productivity but also jeopardized its compliance with auditors, threatening potential fines and revenue losses. The inability to track oil palm inventory accurately also left the company vulnerable to unaccounted losses, including the possibility of fruit theft.
To address these challenges, the client sought a more reliable and scalable solution that would improve counting accuracy and streamline inventory management. Tictag was engaged to develop an AI-driven solution tailored to the client's unique environment, which was complicated by dense foliage that prevented the use of traditional surveillance methods like high-mounted CCTVs.
Proposed Solution
Tictag proposed an innovative augmented AI solution that combined crowdsourced human intelligence with AI-based automation. The goal was to create an AI model capable of accurately distinguishing individual fruit bunches within a harvest even in visually complex environments, with an accuracy rate of over 85%.
Tictag's approach involved using human intelligence to manually annotate images of palm oil fruit bunches, ensuring that the AI would have access to a high-quality dataset for training. This method also respected the client's constraints, such as not being able to use certain hardware solutions like CCTV cameras due to environmental factors.
