Effects of Orchard Characteristics and Operator Performance on Harvesting Rate of a Mechanical Sweet Cherry Harvester

A model was developed to simulate sweet cherry harvesting with a mirrored-pair mechanical harvest system that removes fruit by transferring vibrational energy to tree limbs through an impactor. Six orchard characteristic variables (ST – tree spacing; SR – row spacing; NT – number of trees per row; NR – number of rows; NTB – number of branches per tree; and WTF – total weight of fruit per tree) and three harvester/operator characteristic variables (vH – forward speed of harvester, tIP – time to position impactor on actuation point, and tPS – shaking time per actuation point) were the main inputs to the model. Total harvest time (tTH) and harvesting rate were the two output variables of the model. Harvesting rate was evaluated with three different measures: time rate of area coverage (RAC), time rate of tree coverage (RTC), and time rate of fruit removal by weight (RFR). The model was validated with field data, showing very close predictions with modeling efficiencies of 99%, 86%, 82% and 84% respectively for tTH, RAC, RTC, and RFR. Local sensitivity analysis was conducted varying the input variables in five different levels in order to observe their effect on the output variables. A global sensitivity analysis was also performed to identify input variables with significant effects on the output variables. Data from a complete factorial experiment with three levels of input variables in 19,683 combinations was used to perform the global sensitivity analysis. . It was revealed that ST and SR only affected RAC by defining the unit area occupied by a single tree. NT and NR only affected tTH by determining the number of trees to be harvested, but had no effect on harvesting rate. NTB greatly affected harvesting time and all measures of harvesting rate, and was identified as the most important variable. WTF only affected RFR by determining how much fruit is removed in a single shaking event. Of the harvester/operator variables, tIP affected all the outputs the most whereas vH affected none. Except for RAC, which was least affected by SR, tPS had the least effect amid all the significant input variables. These results provide explanation for achieving different harvesting rates in different orchard settings, and can be used to optimize orchard characteristics and adjust operator behaviors for improved performance in mechanical sweet cherry harvesting

Open and Closed Loop System Characteristics of a Tractor and an Implement Dynamic Model

Accurate guidance of towed implements is important for performing agricultural field operations and for gaining the ultimate benefit from an agricultural automatic guidance system. The study of open and closed loop system responses can be helpful in the design of practical guidance controllers. A dynamic model of a tractor and a towed implement system was developed. Open loop analysis of the kinematic and dynamic models revealed that the dynamic model was essential for capturing the higher order dynamics of the tractor and implement system at higher operating velocities. In addition, a higher fidelity dynamic model was also developed by incorporating steering dynamics and tire relaxation length dynamics. Closed loop system characteristics were studied by using a linear quadratic regulator (LQR) controller. The tractor position and heading and implement heading states along with respective rate states were fed back to close the loop. The higher fidelity closed loop system used a practical range of steering angles and rates to keep the response within nominal off-road vehicle guidance controller design specifications in the forward velocity range of 0.5 m/s to 10 m/s (1.8 km/h to 36 km/h). These simulation studies provided understanding about the characteristics of the tractor and towed implement system and showed promise in assisting in the development of automatic guidance controllers

Design, Integration, and Field Evaluation of a Robotic Blossom Thinning System for Tree Fruit Crops

The US apple industry relies heavily on semi-skilled manual labor force for essential field operations such as training, pruning, blossom and green fruit thinning, and harvesting. Blossom thinning is one of the crucial crop load management practices to achieve desired crop load, fruit quality, and return bloom. While several techniques such as chemical, and mechanical thinning are available for large-scale blossom thinning such approaches often yield unpredictable thinning results and may cause damage the canopy, spurs, and leaf tissue. Hence, growers still depend on laborious, labor intensive and expensive manual hand blossom thinning for desired thinning outcomes. This research presents a robotic solution for blossom thinning in apple orchards using a computer vision system with artificial intelligence, a six degrees of freedom robotic manipulator, and an electrically actuated miniature end-effector for robotic blossom thinning. The integrated robotic system was evaluated in a commercial apple orchard which showed promising results for targeted and selective blossom thinning. Two thinning approaches, center and boundary thinning, were investigated to evaluate the system ability to remove varying proportion of flowers from apple flower clusters. During boundary thinning the end effector was actuated around the cluster boundary while center thinning involved end-effector actuation only at the cluster centroid for a fixed duration of 2 seconds. The boundary thinning approach thinned 67.2% of flowers from the targeted clusters with a cycle time of 9.0 seconds per cluster, whereas center thinning approach thinned 59.4% of flowers with a cycle time of 7.2 seconds per cluster. When commercially adopted, the proposed system could help address problems faced by apple growers with current hand, chemical, and mechanical blossom thinning approaches

The Use of Agricultural Robots in Orchard Management

Book chapter that summarizes recent research on agricultural robotics in orchard management, including Robotic pruning, Robotic thinning, Robotic spraying, Robotic harvesting, Robotic fruit transportation, and future trends.Comment: 22 page

Identifying Water Stress in Potatoes Using Leaf Reflectance as an Indicator of Soil Water Content

Soil water content must be monitored and maintained at adequate level for optimal productivity. Accuracy of traditional sensors used to monitor soil water content depends on the installation technique and proper contact between soil and sensor, which is difficult to achieve in light textured sandy soils. Non-contact sensing technique does not have the limitation of contact with soil and can monitor plant status continuously. In this study, hyperspectral imaging was used as a non-contact technique for detecting changes in spectral reflectance of Umatilla Russet potato plants grown under varying soil water content. An experiment was carried out in a greenhouse to subject potato plants at different levels of soil water content from extreme stress to surplus. Yield data was also collected, which showed that maximum yield for Umatilla Russet potato can be achieved at 18% to 21% soil moisture content. Various spectral indices were calculated using spectral reflectance data at different water stress levels. Principal component analysis was used to identify indices that represented maximum variability in the data. Simple Ratio Index and Modified Red Edge Simple Ratio Index were identified as the two most relevant indices for differentiating soil water content. K-Means clustering with these two indices resulted in an accuracy of 75% in identifying highly stressed plants and 92% accuracy in identifying stressed plants (that included both high and low stress levels). These results showed a promise for development of a non-contact sensor for detecting plant water stress in potatoes, which may lead to an automated irrigation system for maintaining optimal soil water content during potato growing season

Using Spatial Uncertainty of Prior Measurements to Design Adaptive Sampling of Elevation Data

Field sampling can be a major expense for planning within-field management in precision agriculture. An efficient sampling strategy should address knowledge gaps, rather than exhaustively collect redundant data. Modification of existing schemes is possible by incorporating prior knowledge of spatial patterns within the field. In this study, spatial uncertainty of prior digital elevation model (DEM) estimates was used to locate adaptive re-survey regions in the field. An agricultural vehicle equipped with RTK-DGPS was driven across a 2.3 ha field area to measure the field elevation in a continuous fashion. A geostatistical simulation technique was used to simulate field DEMs using measurements with different pass intervals and to quantitatively assess the spatial uncertainty of the DEM estimates. The high-uncertainty regions for each DEM were classified using image segmentation methods, and an adaptive re-survey was performed on those regions. The addition of adaptive re-surveying substantially reduced the time required to resample and resulted in DEMs with lower error. For the widest sampling pass width, the RMSE of 0.46 m of the DEM produced from an initial coarse sampling survey was reduced to 0.25 m after an adaptive re-survey, which was close to that (0.22 m) of the DEM produced with an all-field re-survey. The estimated sampling time for the adaptive re-survey was less than 50% of that for all-field re-survey. These results indicate that spatial uncertainty models are useful in an adaptive sampling design to help reduce sampling cost while maintaining the accuracy of the measurements. The method is general and thus not limited to elevation data but can be extended to other spatially variable field data