New strategies for row-crop management based on cost-effective remote sensors

Agricultural technology can be an excellent antidote to resource scarcity. Its growth has led to the extensive study of spatial and temporal in-field variability. The challenge of accurate management has been addressed in recent years through the use of accurate high-cost measurement instruments by researchers. However, low rates of technological adoption by farmers motivate the development of alternative technologies based on affordable sensors, in order to improve the sustainability of agricultural biosystems. This doctoral thesis has as main objective the development and evaluation of systems based on affordable sensors, in order to address two of the main aspects affecting the producers: the need of an accurate plant water status characterization to perform a proper irrigation management and the precise weed control. To address the first objective, two data acquisition methodologies based on aerial platforms have been developed, seeking to compare the use of infrared thermometry and thermal imaging to determine the water status of two most relevant row-crops in the region, sugar beet and super high-density olive orchards. From the data obtained, the use of an airborne low-cost infrared sensor to determine the canopy temperature has been validated. Also the reliability of sugar beet canopy temperature as an indicator its of water status has been confirmed. The empirical development of the Crop Water Stress Index (CWSI) has also been carried out from aerial thermal imaging combined with infrared temperature sensors and ground measurements of factors such as water potential or stomatal conductance, validating its usefulness as an indicator of water status in super high-density olive orchards. To contribute to the development of precise weed control systems, a system for detecting tomato plants and measuring the space between them has been developed, aiming to perform intra-row treatments in a localized and precise way. To this end, low cost optical sensors have been used and compared with a commercial LiDAR laser scanner. Correct detection results close to 95% show that the implementation of these sensors can lead to promising advances in the automation of weed control. The micro-level field data collected from the evaluated affordable sensors can help farmers to target operations precisely before plant stress sets in or weeds infestation occurs, paving the path to increase the adoption of Precision Agriculture techniques

A Low-cost Depth Imaging Mobile Platform for Canola Phenotyping

To meet the high demand for supporting and accelerating progress in the breeding of novel traits, plant scientists and breeders have to measure a large number of plants and their characteristics accurately. A variety of imaging methodologies are being deployed to acquire data for quantitative studies of complex traits. When applied to a large number of plants such as canola plants, however, a complete three-dimensional (3D) model is time-consuming and expensive for high-throughput phenotyping with an enormous amount of data. In some contexts, a full rebuild of entire plants may not be necessary. In recent years, many 3D plan phenotyping techniques with high cost and large-scale facilities have been introduced to extract plant phenotypic traits, but these applications may be affected by limited research budgets and cross environments. This thesis proposed a low-cost depth and high-throughput phenotyping mobile platform to measure canola plant traits in cross environments. Methods included detecting and counting canola branches and seedpods, monitoring canola growth stages, and fusing color images to improve images resolution and achieve higher accuracy. Canola plant traits were examined in both controlled environment and field scenarios. These methodologies were enhanced by different imaging techniques. Results revealed that this phenotyping mobile platform can be used to investigate canola plant traits in cross environments with high accuracy. The results also show that algorithms for counting canola branches and seedpods enable crop researchers to analyze the relationship between canola genotypes and phenotypes and estimate crop yields. In addition to counting algorithms, fusing techniques can be helpful for plant breeders with more comfortable access plant characteristics by improving the definition and resolution of color images. These findings add value to the automation, low-cost depth and high-throughput phenotyping for canola plants. These findings also contribute a novel multi-focus image fusion that exhibits a competitive performance with outperforms some other state-of-the-art methods based on the visual saliency maps and gradient domain fast guided filter. This proposed platform and counting algorithms can be applied to not only canola plants but also other closely related species. The proposed fusing technique can be extended to other fields, such as remote sensing and medical image fusion

X Congreso Ibérico de Agroingeniería = X Congresso Ibérico de Agroengenharia : Libro de actas = Livro de atas

In 2017, the Food and Agriculture Organization (FAO) issued a report on the challenges that Agriculture is facing and will face into the 21st century, which can be summarized in one question: will we be able to sustainably and effectively feed everyone by 2050 and beyond, while meeting the additional demand for agricultural commodities due to non- food uses? Agricultural engineers can contribute in this process by releasing the biological and technical constraints on crop and animal productivity, reducing the contribution of the agricultural sector to environmental degradation, and enabling agricultural practices to adapt to environmental changes. To achieve optimal results for agribusiness and the society, the expertise of agricultural engineers must be integrated with expertise from other sciences: breakthrough technologies are needed for agricultural enterprises to meet the increasing list of standards and norms in the areas of energy, animal welfare, product quality, water, and volatile emissions. Recognition of trends in society and networking and participation in debates have thus become important activities for agricultural engineers. The Iberian Agroengineering Congress series brings together Spanish and Portuguese engineers, researchers, educators and practitioners to present and discuss innovations, trends, and solutions to the aforementioned challenges in the interdisciplinary field of Agricultural and Biosystems Engineering. This biennial congress, jointly organized by the Spanish Society of Agroengineering and the Specialized Section of Rural Engineering of the Sociedade de Ciências Agrárias de Portugal, has proven to be an excellent opportunity to network and discuss future developments. In its 10th edition, the Congress has been held from 3-6 September in Huesca (Spain), at the Escuela Politécnica Superior, located on the Huesca Campus of the University of Zaragoza. The topics of the Congress have included the main areas of Agricultural Engineering: mechanization; soils and water; animal production technology and aquaculture; rural constructions; energy; information technologies and process control; projects, environment, and territory; postharvest technology; and educational innovation in agroengineering. The Congress has received 123 participants, who have submitted 144 papers, 86 oral communications and 58 poster. 22 universities, 4 research centers and 8 companies/professional associations have been represented. The quality of the papers presented to the congress is endorsed not only by the long trajectory of the Iberian Agroengineering Congress, but also by the edition of a Special Issue of Agronomy journal (ISSN 2073-4395) entitled “Selected Papers form 10th Iberian Agroengineering Congress”