# A Low-Cost Sensorized Vehicle for In-Field Crop Phenotyping
## 1 Introduction  
During the past 20 years, molecular technologies have brought significant advances in plant genomics that provide valuable tools for plant breeding [1]. As reported by [PERSON] et al. [2], advances in these techniques, for example, DNA sequencing, could provide potential increases in the rate of genetic improvement for molecular breeders. Phenotyping is the basis of any breeding selection process [3]. However, the lack of access to phenotyping resources limits the possibility of analyzing the genetics of the quantitative traits correlated with growth, yield, and adaptation to stress [4]. Generally, plant breeders selected phenotypes long before the discovery of DNA and molecular markers. In this context, to identify the best genetic variation, the more crosses and environments used for the selection, the greater the probability of identifying a superior variation [2]. In addition, the plant phenotype includes complex traits and parameters to evaluate, for example, the morphology, biomass, fruit characteristics, photosynthetic activity, and response to biotic and abiotic stress [5; 6; 7]. For all these reasons, as reported by [PERSON] and [PERSON] [1], phenotyping limits the progress in both knowledge and the application of research. To overcome this bottleneck, modern technologies (for example, remote sensing, robotics, computer vision, and artificial intelligence) could provide advantages with respect to the levels of production, applicability under adverse field conditions, and usefulness in breeding processes. As reported by [PERSON] et al. [3], modern plant phenotyping often employs noninvasive technologies and digital technologies, to direct the selection toward productive plants suitable for their environment. In particular, field phenotyping is crucial in a context where crop breeding under different climatic conditions is essential to allow the production of plants to increase [8]. The development of high-throughput field phenotyping, which uses modern detection technologies and advanced data processing algorithms, could increase productivity and make phenotypic evaluation in the field more efficient by collecting large amounts of data with no or minimal human assistance. To date, there are many high-throughput field phenotyping systems, both aerial and ground based. Field-based ground phenotyping systems can be used, for example, to measure many morphological traits of crops with low-cost digital cameras [9] or to measure phenotypic traits related to plant physiological activities, detecting plant diseases, fruit maturity, and plant water stress, using multispectral and hyperspectral cameras and thermal imaging cameras [10]. Instead, remote sensing can be used to dynamically phenotype large numbers of field plots with great efficiency and greater coverage, quickly and economically, effectively supporting crop breeding against biotic and abiotic stresses [11]. Generally, ground systems, being able to carry heavier sensors and equipment than aerial systems, provide data with a higher resolution and quality, being able to perform phenotyping at more precise levels, with more uniform scanning, while also controlling for environment and climatic conditions. In addition to improving the efficiency and precision of selection in breeding programs and expanding the genetic variation in germplasm by evaluating plant phenotypic traits, high-throughput field phenotyping is successfully used to monitor crops with the early detection of pathogens, pests, and weeds, using advanced sensors integrated into field equipment [12]. This allows more samples to be screened with a greater accuracy and low cost, improving yields and reducing the environmental impact. High-throughput plant phenotyping systems are also very effective in selecting crops and characterizing germplasm for drought tolerance [13] and disease resistance [14] by using spectral sensor data in combination with machine learning.