# APTM: A Model for Pervasive Traceability of Agrochemicals
###### Abstract
Literature does not present solutions involving computational technologies to solve the problems of traceability of pesticide packages, even knowing the great importance of detecting movements and correct usage. As a countermeasure to piracy, sensors are envisioned to detect and manipulate products due to their intrinsic characteristics and nature [13]. The work of [PERSON] et al. [14] uses edge networks for decentralized data collection via sensors and preprocessing, used an edge blockchain scheme to optimize transaction path selection. The work of [PERSON] et al. [15] shows the occurrence that the IoT literature on supply chain management focuses on radio frequency identification (RFID), industry 4.0 technologies, and reverse logistics; it provides a better understanding of the use of IoT in the supply chain. RFID is employed to identify products, prices, among other things, facilitating product control. Reverse logistics guarantee the return of packages to origin [16]. Moreover, a blockchain is a structure of linked blocks (data structure) so that a block can point to its predecessor through a field that has a _hash_ of the data from the previous block, and so on. These technologies mentioned here can be adopted and combined in tracking crop protection packages. It is perceived as an open research gap the lack of computational technologies integration, from sensors, processing and machine learning (ML) [17]. In the previously presented context, this article proposes the agrochemical pervasive traceability model (APTM). This model works with a technological mix involving IoT, ML, and blockchain technologies used along the reverse chain of agrochemical packaging. This model defines sensors and controllers willing to inspect the status and mobility of packages along the reverse supply chain and on the farm. The main highlight of this contribution is the processing of data received from the IoT with machine learning; gamification of the rural producer's process, and recording information in public and private blockchains. Therefore, the proposed model allows visualization of all aspects of the agrochemicals movement, contrasting with those detected unauthorized and suspicious. The model contributes in the combination of emerging technologies (IoT, machine learning, and blockchain) with the gamification trend for cultural change while dealing with agrochemicals; this solution even offers legal protection by meeting social needs to preserve the health of workers and the environment, in addition, to avoid losses, such as the misuse of products. This proposal fills the gap in the lack of machine learning usage after IoT and data recording in two blockchain tiers, including gamification as an incentive for this process. The main contributions are (a) in a single process unifies the use of IoT, machine learning, gamification, and blockchain. (b) proposes gamification as a contribution to changing culture in the use of agrochemicals. (c) By providing a blockchain for the farmer to enter information in the reverse chain, it allows the farmer to claim tax credits and carbon credits for farm compliance. (d) features a secure locker with IoT capabilities to keep custody of high-cost agrochemicals. (e) presents a layer of machine learning that identifies, classifies, groups, and predicts different actions by analyzing historical data. (f) this is a low-cost model, allowing farmers in developing countries to adopt it. This article is organized as follows. Section 1 presents an introduction to the topic addressed in this work and its relevance today. Section 2 introduces a fundamental concepts section that covers three subjects required to understand our model. Section 3 discusses the related works. Section 4 presents the pervasive traceability model for agrochemicals, our main contribution. Section 6 presents APTM implementation, as well as an evaluation methodology for judging its functioning. Finally, we have the conclusion in Section 5, commenting on achieved contributions, limitations, and future work.