Phenotypic studies require large datasets for accurate inference and prediction. Collecting plant data in a farm can be very labor intensive and costly. This paper presents the design, architecture (hardware and software) and deployment of a multi-robot system for row crop field data collection. The proposed system has been deployed in a soybean research farm at Iowa State University.Robotics 2018, 7, 61 2 of 15 Several agricultural robots have been developed in the recent past. Unlike industrial robots, the challenges for robots in agriculture are diverse. First, agricultural fields do not have a structured and controlled environment, in contrast to industrial facilities. Second, agricultural robots operate on farms with infrastructure and operating conditions different from industrial robots. Third, industrial processes can be designed by modules to complete particular tasks by a particular robot, whereas the complex tasks in agriculture sometimes cannot be split into simple actions. Due to the aforestated reasons, agricultural applications require more versatile and robust robots.One of the earliest robots deployed in agricultural applications was the German BoniRob [6]. This robot was initially developed by AMAZONEN-WERKE (Hasbergen, Germany) together with the Osnabruck University of Applied Sciences, Robert Bosch GmbH (Stuttgart, Germany) and other partners, and is now a part of Deepfield Robotics, a Bosch start-up company (Stuttgart, Germany). BoniRob was developed to eliminate some of the most tedious tasks in modern farming, plant breeding, and weeding. Another agricultural application example was RHEA [7]. It was a automatic and robotic systems with a fleet of heterogeneous ground and aerial robots developed for effective weed and pest control. Many universities have developed their own robots, for example, the multi-purpose Small Robotic Farm Vehicle (SRFV) from Queensland University of Technology, Brisbane, Australia [8]. This modular design allows the SRFV to undertake a range of agricultural tasks and experiments, including harvesting, seeding, fertilizing and weeding management. Another example is the Thorvald II [9], a completely modular platform both on the hardware and the software side. It can be reconfigured to obtain the necessary physical properties to operate in different production systems, for example, tunnels, greenhouses and open fields. Another example of a university-developed agricultural robot is the Phenobot 1.0 [10] from Iowa State University. It is an auto-steered and self-propelled field-based phenotyping platform for tall dense canopy crops.Companies that work on commercial robots exists as well. For the open field, there is the ANATIS from Carre, the Robotti from AGROINTELLI and Asterix from Adigo [11]. Ecorobotix is developing a solar powered robot for ecological and economical weeding of row crops, meadows and intercropping cultures, while Naio technologies has developed the robots OZ, BOB, TED and DINO. Other robots are designed for greenhouses. One example is the fully-auto...
Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop a machine learning (ML) approach adept at soybean (Glycine max L. (Merr.)) pod counting to enable genotype seed yield rank prediction from in-field video data collected by a ground robot. To meet this goal, we developed a multiview image-based yield estimation framework utilizing deep learning architectures. Plant images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding decisions. We used data from controlled imaging environment in field, as well as from plant breeding test plots in field to demonstrate the efficacy of our framework via comparing performance with manual pod counting and yield estimation. Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort and opening new breeding method avenues to develop cultivars.
This paper presents the design, modeling, control and navigation for a novel ground-based mobile sensing platform that can collect multi-modal data in agricultural research farms for high throughput modular plant phenotyping. The platform will have the following capabilities (i) Navigate in a row-crop farm to collect data with minimal human intervention during operation (ii) Autonomous decision making i.e, it can take its own decisions for maximizing the value of information of the acquired data and (iii) Scalable in terms of the size of the farmland. The design requirements for such a platform or robot is formulated, and a detailed discussion on realizing such a design is presented. The dynamics of the robot is presented in the state space form and it is abstracted in the form of a control flow diagram for the automatic steering system. An adaptive sampling approach has been taken to generate an estimated belief-space which is leveraged in the proposed opportunistic sensing scheme to generate way-points for navigation.
The aim of this research was to compare growth performance, nutrient utilization, rumen fermentation, blood parameters, antioxidant status, and meat colour stability of lambs fed diets that partially or completely substituted corn with sorghum. Twenty-four male German merino weaned lambs (16.19 ± 2.05 kg body weight) were divided into four treatments in a completely randomized design. The diets for four treatments contained 450 g kg−1 of ground corn (C), 300 + 150 g kg−1 of ground corn and sorghum (CSM1), 150 + 300 g kg−1 of ground corn and sorghum (CSM2) and 450 g kg−1 ground sorghum (S); all diets consisted of 70% concentrated feed and 30% Leymus chinensis hay. The lambs were fed the experimental diets for 56 d. Inclusion of sorghum tended to increase average daily weight gain and total gain (P = 0.06), and lower feed conversion ratio (P = 0.10). Significant increase in nitrogen (N) intake and fecal N excretion was observed after substitution of corn with sorghum, and the apparent digestibility of crude protein was significantly reduced. Concentrations of ammonia N in rumen fluid were affected by treatment (P = 0.01) and an interaction (P < 0.01) between treatment and sampling time. No significant effects were found on blood parameters among treatments. Replacement of corn with sorghum significantly decreased b* (yellowness) values of meat during storage. Sorghum instead of corn is feasible in lamb diets, and it has positive effects on the lamb growth and meat quality.
Phenotypic studies require large datasets for accurate inference and prediction. Collecting 1 plant data in a farm can be very labor intensive and costly. This paper presents the design, architecture 2 (hardware and software) and deployment of a distributed modular agricultural multi-robot system 3 for row crop field data collection. The proposed system has been deployed in a soybean research 4 farm at Iowa State University. 5
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