O objetivo no trabalho foi de construir um protótipo e automatizá-lo com base em processos utilizados na indústria de alimentos, sendo representado por uma esteira transportadora. Sua função foi de realizar a contagem e identificar desconformidades na posição de embalagens, tendo o sistema operado através do microcontrolador Basic Step 1, e acionamento do código por meio da ação de controle On/Off, a partir do comando de um algoritmo desenvolvido no software do compilador Basic Step BS1. Foi realizada a montagem do protótipo a partir de materiais reciclados, como carcaça de esteira ergométrica, lona plástica preta, motor de êmbolo de colhedora e blocos de madeira. O display de cristal líquido, relé e contator foram interligados em uma matriz de contato formando o circuito integrado, que após a sua construção foi conectada ao protótipo para a realização de testes e averiguação do desempenho. Todos os componentes utilizados funcionaram corretamente durante as etapas de testes, o protótipo identificou o posicionamento das caixas e realizou a contagem desativando o sistema quando as embalagens se encontravam posicionadas de maneira incorreta. O projeto se mostrou ser uma alternativa de simples construção e de baixo custo, podendo ser aplicada em indústrias pequeno porte.

Precision agriculture (PA) relies on data collection and analysis to understand spatial variability in agricultural crops, using technologies such as sensors, GNSS, remotely piloted aircraft (RPAs), and management software. This study aimed to evaluate the potential of a LiDAR sensor coupled to an RPA to map damage in coffee plants after frosts and fill scientific gaps. To this end, the experiment was conducted in Santo Antônio do Amparo, Brazil, in May 2023, obtaining data on height and canopy diameter of three coffee plantations, aged 3, 4, and 10 years, affected by frosts in different years (2019 and 2021). Multivariate statistical analyses were performed using non-hierarchical clustering tools to classify the coffee plants into two groups according to frost incidence. Two new classifications were proposed for the two groups, based on k-means non-hierarchical cluster analysis. Thus, the study showed significant differences in the variables, with a p-value of <2.2 × 10⁻¹⁶ for the proposed groupings 1 and 2. Therefore, the groups are statistically different.

Studies related to the mechanization of coffee transplanting combined with precision agriculture techniques demonstrate a need to verify the quality of the operation to optimize future production processes in the field and reduce costs. The objective of this study was to analyze the operational costs arising from a semi-mechanized coffee transplanting process using an autopilot. The study was conducted on a rural property in the municipality of Santo Antônio do Amparo—MG—where 7458 coffee seedlings were transplanted using a planting platform pulled by a tractor equipped with an autopilot and a GNSS antenna, over a period of 4 days. The date and time data of the operation, recorded every second by the autopilot, were collected and recorded in spreadsheets to assist in calculating operational costs. Two semi-mechanized transplanting scenarios were compared: one using autopilot and the other using conventional semi-mechanized transplanting. The results indicated that the hourly cost of operation with autopilot was US$2130.42 h⁻¹, while the conventional system presented US$326.03 h⁻¹. The effective operational cost was US$3975.61 ha⁻¹ for the system with autopilot and US$442.31 ha⁻¹ for the conventional system in 2020. After monetary updating to 2025, the operational costs increased to US$1845.19 ha⁻¹ and US$116.28 ha⁻¹, respectively. The investment analysis indicated an Internal Rate of Return of 89.7%, highlighting the potential return on investment in the study. Therefore, it was emphasized that even with a high investment cost, the application of autopilot is viable for improving tractor steering during operation and ensuring uniformity in the positioning of coffee seedlings.