Sugarcane plant detection and mapping for site-specific management - Maldaner, Leonardo Felipe (2021)
The sugarcane production sector is one of the most adept at adopting technology to manage equipment and sugarcane fields. Developing new technologies and optimizing the use of the technologies already used in other production systems is essential for successful field management. The optimized use of technologies will help in the localized management to increase viability, maximize profitability, and minimize the environmental impacts of sugarcane production. Technologies to detect, measure, and spatialize plants can be one of the solutions for the row level management. Moreover, this data can be used to temporally follow the development of sugarcane fields, being essential data for localized field management. The spatialization of plants and plant spacing can help in the investigation of factors that influence sugarcane yield. In this context, the overall objective of the thesis was to explore tools and methods for detecting plants at row level to improve and support localized management of sugarcane plantations. An approach to sugarcane plant detection using photoelectric and ultrasonic sensors was developed and evaluated. Aerial image and ground sensors have been tested to detect and measure sugarcane plant spacing. Temporal evaluation of sensors and aerial images during four different stages of sugarcane development was made to propose the best time to detect sugarcane plants and measure the plant spacing. High-resolution images were used to map plant population and plant spacing. These two data were used to check the relationship between slope, path angle, and the plant population, furthermore, map regions with higher susceptibility to plant reduction over the years. At last, a spatio-temporal analysis of yield and plant spacing was performed to verify the relationship between these two variables in regions with different yield potentials in commercial crops. Results show that ultrasonic and photoelectric sensor fusion associated with the machine learning model has accuracy above 95%. These two sensors and high-resolution images had the best accuracy and precision in detecting and measuring plant spacing at 31 and 47 days after harvest. Spatial and temporal analysis showed that regions with a terrain slope of 5-8% and greater than 8% with curved paths have an inferior number of plants compared to other regions. The local analysis identified that regions with steeper slopes and curved paths have high susceptibility of plant reduction over the years compared to other regions. Finally, yield loss within the sugarcane row occurs with increasing plant spacing. Regions with different yield potentials require different optimum populations to maximize yield. Low-yielding regions require a larger plant population and are more susceptible to lose in yield within the row with increasing plant spacing.