As the world population increases and the need for food monoculture farms are using more and more agrochemicals, there is also an increase in the possibility of theft, misuse, environmental damage, piracy of products, and health problems. This article addresses these issues by introducing the agrochemical pervasive traceability model (APTM), which integrates machine learning, sensors, microcontrollers, gamification, and two blockchains. It contributes in two dimensions: (I) the study of the environmental, product piracy and regulatory of agrochemical control; (II) the technological dimension: application of an adequate set of sensors collecting multiple data; modeling and implementation of a system via machine learning for analyzing and predicting the behavior and use of agrochemicals; development of a scoring system via gamification for reverse use of agrochemicals; and presenting a record of transactions in a consortium of two blockchains, simultaneously. Its main advantage is to be a flexible, adaptable, and expansive model. Results indicated that the model has positive aspects, from detecting the agrochemical, its handling, and disposal, recording of transactions, and data visualization along the reverse supply chain. This study obtained a round trip time of 0.510 ms on average; data transfers between layer one and its persistence in the database were between 4 to 5 s. Thus, blockchain nodes consumed only 34 to 38% of CPU and recorded transactions between 2 to 4 s. These results point to a horizon of applicability in real situations within agricultural farms.

The growth of the world population forces a more outstanding and more efficient food production, forc-ing agribusiness into the race for greater productivity. Thus, agrochemicals as a tool for increasing and defending production have become more critical with each harvest. This work presents an ontology that describes the knowledge involved in the need for Agrochemical Pervasive Traceability Model (APTM). This proposed ontology is called ontology for pervasive traceability of agrochemicals (OntoPTA). We present classes and their relationships in a hierarchical way and a visualization from the OWL ontology language. This ontology fills the gap in the understanding and modeling of this type of agribusiness pro-cess. This modeling helps farm administrators and software developers to perform better analysis for the development, use and maintenance of systems in agribusiness.

Agrochemicals are products that, due to their hazardous nature and high cost, need to be monitored. The management of agrochemical packaging is generally precarious, and the supply chain needs more control due to its reverse characteristic. Traceability in the supply chain usually uses one sensor and not a combination of multiple sensors. The proposed model allows one to trace agrochemicals with reliable and immutable information coming from various sensors, solving problems of unreliable traceability, product theft, and product tampering. Unlike related work, this model contributes with a proposal segmented in modules that focus on security and scalability in controlling used packaging. In this case, the proofs of concept indicate detection of the opening movement of the safe cabinet from 5 lx (unit of illuminance) and movement of packages after a radius of 2 cm, and the data sending time between the model layers was around 1 second. The positive aspects are benefits for detecting intact products and packages openly within the production process and persistently; other benefits include better use of assets and management of the production chain, real‐time production data collection, classification, grouping, and prediction of events. Monitoring the packaging reverse chain and the possibility of transaction auditing. Benefits include better use of assets and management of the production chain, real‐time production data collection, classification, grouping, and prediction of events generated by the production operation, and persistence of this information for future transactions and audits. Farmers and society benefit from making production and supply chains cleaner and safer and reducing the risk of costly and environmental accidents.