Department of Agricultural Engineering

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Mechanisms of mechanical oil expression from rapeseed and cashew.
(University College Dublin, 1979-02) Mrema, Geoffrey C.
The conventional theory of oil expression from vegetable oilseeds suggests that before substantial oil expression can occur, the oilseed cellwalls have to be ruptured by a combination of physical (crushing) and thermal (cooking) pre-treatments. Results from oil expression tests using rapeseed and cashew on the Instron Universal Testing Machine have suggested an alternative mechanism in which up to 80% oil is expressed through a porous cellular microstructure under pressure without cellwall rupture and at ambient temperature. The porous mission electron microscopy. The cellwall pores (plasmodesmata) were of diameter 0.87 and 0.126 pm wall surface area for rapeseed and cashew respectively. The oil expression process has been successfully described by a mathematical model based on three modified form of Terr.aghi's equation for the consolidation of saturated soils., to describe the behaviour of the consolidating oilseed cakej the Hagen Poiseulle equation for flow of fluids to describe the flow of oil through the pores on the cellwall; and The model has Darcy 's law of flow of fluids through porous media to describe the flow of oil through the intra-kernel voids. in pipes, fundamental equations: a oil expression can occur, the oilseed cellwalls and average porosity of 0.093 and 0.171% of the cellnature of the cellwalls has been confirmed by trans been succeafully applied to experimental data which has revealed that the flow of oil across cellwalls in the seed kernel was the rate determining step. In addition the model was also used to analyse the performance of hydraulic and screw expellers. The study has suggested that the design of both hydraulic and screw expellers could be improved by incorporating an undrained compression pretreatment to rupture cellwalls 3 and by reducing the drainage area to 0.5% - 1.5%. Furthermore^ improved strategies for oil expression have been suggested in two cases, (a) For mechanical expression followed by solvent extraction it is proposed that the physical (pre-crushing) and thermal (cooking) pre-treatments be replaced by an undrained compression pre-treatment. are not r equired (b) where mechanical expression is the sole process^ the pre-crushing pre-treatment should replaced by an undrained compression pre-treatment.
Irrigation scheduling
(Sokoine University of Agriculture, 1984) Thadei, Simon y
This thesis deals with irrigation scheduling under rotational water supply. First, irrigation scheduling is defined and the general review of the principles, factors, methods, and scheduling techniques which must be considered before scheduling is given. This includes crop water requirements, factors affecting crop water requirements and methods for determining crop water requirements. Then soil waterholding capacity, irrigation requirements (efficiencies and leaching requirements), amount of water for irrigation, availability of water supply, and scheduling techniques are reviewed. Also, Tanzania's case study is given for two projects: Mbarali (a state farm) and Mombo (village owned farm). Finally, irrigation schedule is prepared for Mombo irrigation scheme.
Performance evaluation of an indigenous irrigation system at towero village, western Uluguru mountains, Tanzania
(Sokoine University of Agriculture, 2000) Kongola, Malongo John Mussa
Indigenous irrigation methods in mountainous areas are a result of people’s efforts to survive on limited land resource bases. The adoption of an irrigation method depends on whether it does not affect the soil. While surface methods are common in the Uluguru Mountains, drag hose sprinkling is receiving wide use at Towero. Drag hose sprinkler irrigation refers to the local use of sprinklers where water pressure is obtained by gravity flow. Effects of indigenous irrigation systems’ practices on soil erosion were evaluated using field data and aerial photographs. Field data were obtained from six slopes, ranging from 6 to 84%. Two versions of aerial photographs were used to produce land-use maps for 1964 and 1992, respectively. Traversing produced the land-use map of 1999. Land-use analysis revealed that between 1964 and 1999, the area under indigenous irrigation increased by 0.81 ha/ycar. Between 1964 and 1999 the settlement area increased by 0.83 ha/year. Deforestation rate was 6.48 ha/year. Mean crop yields for leeks ranged between 9.65 — 13.53 tonne/ha. Mean specific yields ranged between 0.65 - 1.09 kg/m3. Mean water conveyance, application and storage efficiencies were 83.72%, 86.20% and 99.64%, respectively. All fields wereii over-irrigated based on soil moisture data which were taken daily. Over-irrigation caused inequitable water distribution. Daily sediment load transport in canals A, B and C were 22.2 kg, 187.6 kg and 54.7 kg, respectively. Total sediment loss was 264.6 kg per day. Net downslope splash loss at 84, 70, 65, 24 and 15% slope was 28.9, 19.1, 12.0, 6.0, and 1.0 kg/ha, respectively. The effects of indigenous irrigation systems’ practices at Towero were: soil loss in the form of splash erosion and sediment load transport, and acceleration of deforestation in pursuit for more agricultural land. Hence, efforts to promote soil conservation practices at Towero are essential.
Modelling the water balance of a small catchment: a case study of Muhu catchment in Southern highlands of Tanzania
(Sokoine University of Agriculture, 2000) T. Shiba, Sipho Simeon S.
I'he water balance ol'Muhu catchment located in the Southern Highlands of Tanzania in Iringa region was modelled by establishing the empirical relations that exist between storage parameters, rainfall parameters and runoff components. Storage parameters included soil moisture storage and interception. Rainfall parameters included rainfall amount, intensity, duration. throughfalL stemflow and evaporation. Runoff components included total runoff, direct runoff and base How. The catchment's physical and hydrological characteristics that affect these parameters were determined. 1 he assessment of hydrological and physical properties showed that the soils were predominantly sandy clay, having high organic matter content, with a moderately rapid hydraulic conductivity (Ks) of 4.2 cm/h and infiltration rale of 3.8 cm/ h. The bulk density was generally low with an average of 0.9 g/cnT for 0-15 cm depth: 1.1 Ig/cm5 for 15-30 cm depth and 1.30 g/cm’ for 30 - 45 cm depth. The catchment had a slope steepness of 35 % and a varying vegetal percentage cover of about 56 %. The 1997/98 waler year was exceptional with high rainfall (1934 mm) mainly due to the El-nino phenomenon. Sixty-seven percent of the rainfall received in the catchment penetrated the canopy to reach the forest floor as throughfalL On average 3.3 % of the rainfall reached the forest floor as stem flow' while 25.5% of the rainfall was intercepted by the canopy. ThroughfalL stemflow and interception were linearlyIll related to rainfall. Die regression coefficients of all the relationships were significantly different from zero al 1% level (fteO). With increasing percentage surface cover, interception increased while throughfall decreased. The storage capacity of the forest cover was estimated to be 0.7 mm. Il has been found in this study that stream flow and runoff have gradually been increasing since the 1994/95 season. However the rainfall trend docs not support this development. A consideration of runoff curve numbers showed that the observed trend was partly due to catchment degradation, farming activities in the area have gradually been substituting the forest with arable land, thus reducing surface cover. Records indicated that the lowest recorded daily mean How was 0.27 m'/s. while the highest was 1.6 m'/s. I he water balance was positive during the first five months of the wet season. The highest water balance was in April. During this period there was more recharge to the soil moisture and ground water storage. Water balance was negative in the remaining seven months of the water year, with the lowest in September. The developed direct runoff model and water balance model were found to be valid and useful in estimating the respective parameters in forested catchments of the southern highlands of Tanzania.
Repair costs of tractors and comparison of mechanization strategies under Tanzanian conditions
(University of Munich, 2000-08) Mpanduji, Sylvester Michael
An examination of alternative fertilizer transportation, warehousing and application systems for agricultural cooperatives
(Sokoine University of Agriculture, 2004) Kilima Fredy Timothy Mlyavidoga
The difficulty of controlling cost in a dynamic industry where competitiveness and costs are changing over time has long been recognized. Conventional wisdom suggests that players who succeed in such an industrial setting are those who capture the opportunities presented by a new business environment while maintaining economic efficiency (Bello, Lohtia and Sangtani; Flint). In recent years, one of the business challenges for fertilizer suppliers in the United States has been to keep pace with the changing business environment. The changes arise from changing demand, growing global competition, increased regulations in the industry for environmental and safety reasons, and improvements in the transportation and application methods. The improvement in fertilizer distribution and application methods is by and large a reflection of changes in the physical condition and operating characteristics of highways, and changes in farm transportation and application equipment (USDA, Agricultural Cooperative Service). Changes in fertilizer demand and increased market competition are attributable to changes in farm application systems, and consolidation of farming business that has decreased the number of farms and increased the average farm size (Norton).
Root-zone soil moisture redistribution in cropping systems under freeze-thaw conditions
(University of Manitoba, 2008) Kahimba, Frederick Cassian
The availability and distribution of soil moisture within the root zone is a key factor in ensuring better crop growth performance and attaining improved yield. The soil moisture is influenced by farm management practices such as cover cropping that affect the freeze-thaw processes during the fall. This in turn may influence accumulation and redistribution of soil moisture during the winter, and thereafter, the soil’s response to thawing during spring, and availability of soil moisture for the subsequent season. The impact of cover cropping systems on soil temperature, infiltration, and soil moisture redistribution due to soil freezing and thawing was investigated. In addition, the effect of cover crop on the within-season and subsequent-season crop performance and yield was also investigated. Time Domain Reflectometry (TDR) and Neutron Scattering (NS) methods were used to measure the unfrozen and total water contents, respectively. Soil temperature was measured using thermocouples embedded in the soil profile. Soil moisture and soil temperature data were collected from August 2005 to September 2007. Laboratory calibration of the TDR miniprobes indicated the maximum cable length for the RG-58 50 Q coaxial cable to be 40.0 m when 35 mm TDR miniprobes were used. Since the TDR was found to overestimate the liquid water content at soil temperatures below 25°C, a method to correct the field measured TDR soil moisture for temperature effects was developed. During soil freeze-up, water from unfrozen soil layers below the freeze front migrated towards frozen layers above. Compared to non-cover crop treatment, the cover crop treatment did not freeze earlier during the fall, froze to a shallower depth during the i
Real-time 3-D measurement of cotton boll positions using machine vision under field conditions
(2018 Beltwide Cotton Conferences, San Antonio, 2018-01) Fue, K. G.; Rains, G. C.; Porter, W. M.; Tifton, G. A.
Cotton harvesting is performed by expensive combine harvesters that hinder small to medium-size cotton farmers Advances in robotics provide an opportunity to harvest cotton using small and robust autonomous rovers that can be deployed in the field as an “army” of harvesters. This paradigm shift in cotton harvesting requires high accuracy 3D measurement of the cotton boll position under field conditions. This in-field high throughput phenotyping of cotton boll position includes real-time image acquisition, depth processing, color segmentation, features extraction and determination of cotton boll position. In this study, a 3D camera system was mounted on a research rover at 82° below the horizontal and took 720p images at the rate of 15 frames per second while the rover was moving over 2-rows of potted defoliated cotton plants. The software development kit provided by the camera manufacturer was installed and used to process and provide a disparity map of cotton bolls. The system was installed with the Robot Operating System (ROS) to provide live image frames to client computers wirelessly and in real time. Cotton boll distances from the ground were determined using a 4-step machine vision algorithm (depth processing, color segmentation, feature extraction and frame matching for position determination). The 3D camera used provided distance of the boll from the left lens and algorithms were developed to provide vertical distance from the ground and horizontal distance from the rover. Comparing the cotton boll distance above the ground with manual measurements, the system achieved an average R2 value of 99% with 9 mm RMSE when stationary and 95% with 34 mm RMSE when moving at approximately 0.64 km/h. This level of accuracy is favourable for proceeding to the next step of simultaneous localization and mapping of cotton bolls and robotic harvesting.
Evaluating the effect of planter downforce and seed vigor on crop emergence and yield in Hill-drop vs Singulated Cotton.
(2018 Beltwide Cotton Conferences, San Antonio, 2018-01) Virk, S. S.; Porter, W. M.; Fue, K. G.; Snider, J. L.; Whitaker, J.
Selection of correct planting parameters and their optimization based on current field conditions is crucial in achieving high crop emergence, which can translate to higher yields. A study was conducted during 2017 to evaluate the effect of planter downforce and seed vigor on crop emergence and yield in two cotton varieties planted with singulated and hill-drop seed plates. For this study, two cotton varieties (a small seeded low vigor variety and a large seeded high vigor variety) were planted at 1-inch seeding depth using two different planters to obtain singulated and hill-drop planting conditions. Two seeding rates of 29,000 seeds/ac and 42,500 seeds/ac were used to represent a typical low and high population for planting cotton in Georgia. Planter downforce treatments consisting of low, medium and high downforce values (100, 200 and 300 lbs., respectively) were implemented using the available downforce technology on both planters. Field data collection consisted of emergence counts at one and three weeks after planting and yield data from the center two rows of a four row plot at the end of the season. Data analysis indicated that singulated seeds were more effective in low downforce treatments independent of the crop variety. Hill-drop seeds exhibited better crop emergence (75-81%) in higher downforce treatments as compared to crop emergence (62-72%) obtained with singulated seeds. Yield data also suggested that singulated cotton can maximize emergence in low to medium downforce conditions for large seeded high vigor varieties whereas hill-drop cotton yields better with small seeded low vigor varieties planted at medium to high downforce. Results showed that low vigor varieties require higher seeding rates (more seeds per foot) when planted using low downforce to provide an overall high crop emergence rate whereas this trend was not observed in the high vigor variety. A comparison among seeding rates showed that higher seeding rates did not maximize crop emergence when planted as hill-drop. Overall results from this study emphasized the importance of using correct planting parameters (downforce, seeding rate, and variety) based on existing field conditions to maximize crop emergence and yield.
Deep learning based Real-time GPU-accelerated tracking and counting of cotton bolls under field conditions using a moving camera
(2018 ASABE Annual International Meeting, 2018-08) Fue, Kadeghe G.; Porter, Wesley; Rains, Glen
Robotic harvesting involves navigation and environmental perception as first operations before harvesting of the bolls can commence. Navigation is the distance required for a harvester’s arm to reach the cotton boll while perception is the position of the boll relative to surrounding environment. These two operations give a 3D position of the cotton boll for picking and can only be achieved by detection and tracking of the cotton bolls in real-time. It means detection, tracking and counting of cotton bolls using a moving camera allows the robotic machine to harvest easily. GPU-accelerated deep neural networks were used to train the convolution networks for detection of cotton bolls. It was achieved by using pretrained tiny yolo weights and DarkFlow, a framework which translates YOLOv2 darknet neural networks to TensorFlow. A method to connect tracklets using vectors that are predicted using Lucas-Kanade algorithm and optimized using robust L-estimators and homography transformation is proposed. The system was tested in defoliated cotton plants during the spring of 2018. Using three video treatments, the counting performance accuracy was around 93% with standard deviation 6%. The system average processing speed was 21 fps in desktop computer and 3.9 fps in embedded system. Detection of the system achieved an accuracy and sensitivity of 93% while precision was 99.9% and F1 score was 1. The Tukey’s test showed that the system accuracy and sensitivity was the same when the plants were rearranged. This performance is crucial for real-time robot decisions that also measure yield while harvesting.
Visual row detection using pixel-based algorithm and stereo camera for cotton-picking robot
(2019 ASABE Annual International Meeting, Boston, Massachusetts, 2019) Fue, K. G.; Georgia, Tifton; Porter, W. M.; Barnes, E. M.; Rains, G. C.
Precision farming still depends heavily on RTK-GPS to navigate the rows of crops. However, GPS cannot be the only method to navigate the farm for robots to work as a “swarm” on the same farm; they also require visual systems to navigate and avoid collisions. Also, plant growth and canopy changes are not accommodated. Hence, the visual system remains a complementary method to add to the efficiency of the GPS system. In this study, optical detection of cotton rows is investigated and demonstrated. A stereo camera is used to detect the row depth, and then, a pixel- based algorithm is used to calculate and determine the upper and lower part of the canopy of the cotton rows by assuming the normal distribution of the high and low pixels. The left and right row are detected by using perspective transform and pixel-based sliding window algorithms. Then, the system determines the Bayesian score of the detection and calculates the center of the rows for smooth navigation of the cotton-picking robot. The 92.3% accuracy and F1 score of 0.951 are sufficient to deploy the algorithm for robotic operations. The deployment and testing of the robot navigation will be done in 2019.
Visual inverse kinematics for cotton picking robot
(2019 Beltwide Cotton Conferences, New Orleans, Louisiana., 2019) Fue, K. G.; Tifton, Georgia; Porter, W. M.; Barnes, E. M.; Rains, G. C.
Fast cotton picking requires a fast-moving arm. The Cartesian arm remains the most simple and quick moving arm compared to other configurations. In this study, an investigation of the 2D Cartesian arm controlled with a stepper- drive is investigated. The arm is designed and mounted to a research rover. Two stereo cameras are installed and used to take the images of the cotton plants in two different angles. One camera is directly pointing downward while the other camera is pointing perpendicular to the row. This configuration allows the robot to view the cotton plants and bolls. The robot arm can move upward and downward or left and right. The rover uses two linear servos connected to a variable displacement pump swashplate for powering four hydraulic wheel motors and the engine accelerator linkage to move forward. The forward and backward movement of the rover makes the cotton-picking robot arm movement 3-dimensional. The downward camera gives feedback to the robotic system on the position of the arm. The rover moves forward along the row and stops whenever the cotton boll is perpendicular to the cartesian arm. The sideways camera gives an alternative view of the cotton boll that allows the robot servos to stop accurately. The arm uses vacuum suction to pick the cotton bolls. The vacuum suction end effector is mounted on the arm and pointing perpendicular to the row. In this paper, the kinematics and movement of the cotton arm and boll picking are demonstrated.
Visual control of cotton-picking Rover and manipulator using a ROS-independent finite state machine
(2019 ASABE Annual International Meeting, 2019-07) Fue, Kadeghe; Barnes, Edward; Porter, Wesley; Rains, Glen
Small rovers are being developed to pick cotton as bolls open. The concept is to have several of these rovers move between rows of cotton, and when bolls are detected, use a manipulator to pick the bolls. To accomplish this goal, each cotton-picking robot needs to accomplish three movements; rover must move forward/backward, left/right and the manipulator must be able to move to harvest the detected cotton bolls. Control of these actions can have several states and transitions. Transitions from one state to another can be complex but using ROS-independent finite state machine (SMACH), adaptive and optimal control can be achieved. SMACH provides task level capability to deploy multiple tasks to the rover and manipulator. In this research, a cotton-picking robot using a stereo camera to locate end-effector and cotton bolls is developed. The robot harvests the bolls using a 2D manipulator that moves linearly horizontally and vertically. The boll 3-D position is determined by calculating stereo camera parameters, and the decision of the finite state machine guides the manipulator and the rover to the destination. PID control is deployed to control rover movement to the boll. We demonstrate preliminary results in a direct-sun simulated environment. The system achieved a picking performance of 17.3 seconds per boll. Also, it covered the task by navigating at a speed of 0.87 cm per second collecting 0.06 bolls per second. In each mission, the system was able to detect all the bolls but one.
Field testing of the autonomous cotton harvesting Roverin undefoliated cotton field
(2020 Beltwide Cotton Conferences, Austin, Texas, 2020) Fue, K. G.; Porter, W. M.; Tifton, G. A.; Barnes, E. M.; Cary, N. C.; Rains, G. C.
This study proposes the use of an autonomous rover to harvest cotton bolls before defoliation and as the bolls open. This would expand the harvest window to up to 50 days and make cotton production more profitable for farmers by picking cotton before the quality is at risk. We developed a cotton harvesting rover that is a center-articulated vehicle with an x- y picking manipulator and a combination vacuum and rotating tines end-effector to pull bolls off the plant. The rover uses a stereo camera to see rows, RTK-GPS to localize itself, fisheye camera for heading, and stereo camera to locate the cotton bolls. The SMACH library is a ROS-independent task-level architecture used to build state machines for the rapid implementation of the robot behavior. First, the GPS waypoints are obtained, and then, the rover passes over the rows while picking the cotton bolls. The navigation is controlled by a modified pure-pursuit technique together with a PID controller. Two parallel programs organize the entire rover regarding when to pick and when to navigate. While navigating, the rover looks for harvestable bolls, and when bolls are discovered, the robot will stop and pick. It will do this repetitive work until it finishes all the rows. The rover navigation had an absolute error mean of 0.189 m, a median of 0.172 m, a standard deviation of 0.137 m, and a maximum of 0.986 m. The largest errors occurred during turning around at the end of rows and were caused by wet conditions and tire slippage. The rover picked cotton bolls at the average Action Success Ratio (ASR) of 78.5% and was able to reach 95% of the bolls. Most bolls that were not picked could not be pulled into the vacuum using the rotating tines on the end-effector.
An extensive review of mobile agricultural robotics for field operations: focus on cotton harvesting
(MDPI, 2020-03-04) Kadeghe, G. Fue; Barnes, Edward M.; Rains, Glen C; Porter, Wesley M
In this review, we examine opportunities and challenges for 21st-century robotic agricultural cotton harvesting research and commercial development. The paper reviews opportunities present in the agricultural robotics industry, and a detailed analysis is conducted for the cotton harvesting robot industry. The review is divided into four sections: (1) general agricultural robotic operations, where we check the current robotic technologies in agriculture; (2) opportunities and advances in related robotic harvesting fields, which is focused on investigating robotic harvesting technologies; (3) status and progress in cotton harvesting robot research, which concentrates on the current research and technology development in cotton harvesting robots; and (4) challenges in commercial deployment of agricultural robots, where challenges to commercializing and using these robots are reviewed. Conclusions are drawn about cotton harvesting robot research and the potential of multipurpose robotic operations in general. The development of multipurpose robots that can do multiple operations on different crops to increase the value of the robots is discussed. In each of the sections except the conclusion, the analysis is divided into four robotic system categories; mobility and steering, sensing and localization, path planning, and robotic manipulation.
Center-articulated hydrostatic cotton harvesting Rover using visual-servoing control and a finite state machine
(MDPI, 2020-07-30) Kadeghe, Fue; Wesley, Porter; Edward, Barnes; Changying, Li; Glen, Rains
Multiple small rovers can repeatedly pick cotton as bolls begin to open until the end of the season. Several of these rovers can move between rows of cotton, and when bolls are detected, use a manipulator to pick the bolls. To develop such a multi-agent cotton-harvesting system, each cotton-harvesting rover would need to accomplish three motions: the rover must move forward/backward, turn left/right, and the robotic manipulator must move to harvest cotton bolls. Controlling these actions can involve several complex states and transitions. However, using the robot operating system (ROS)-independent finite state machine (SMACH), adaptive and optimal control can be achieved. SMACH provides task level capability for deploying multiple tasks to the rover and manipulator. In this study, a center-articulated hydrostatic cotton-harvesting rover, using a stereo camera to locate end-effector and pick cotton bolls, was developed. The robot harvested the bolls by using a 2D manipulator that moves linearly horizontally and vertically perpendicular to the direction of the rover’s movement. We demonstrate preliminary results in an environment simulating direct sunlight, as well as in an actual cotton field. This study contributes to cotton engineering by presenting a robotic system that operates in the real field. The designed robot demonstrates that it is possible to use a Cartesian manipulator for the robotic harvesting of cotton; however, to reach commercial viability, the speed of harvest and successful removal of bolls (Action Success Ratio (ASR)) must be improved.
Robust edge detection method for the segmentation of diabetic foot ulcer images
(2020-08) Mwawado, Rehema
Segmentation is an open-ended research problem invarious computer vision and image processing tasks. This pre-processing operation requires a robust edge detector to generateappealing results. However, the available approaches for edgedetection underperform when applied to images corrupted by noise or impacted by poor imaging conditions. The problembecomes significant for images containing diabetic foot ulcers, which originate from people with varied skin color. Comparative performance evaluation of the edge detectors facilitates the process of deciding an appropriate method for image segmentation of diabetic foot ulcers. Our research discovered that the classical edge detectors cannot clearly locate ulcers in images with black-skin feet. In addition, these methods collapse for degraded input images. Therefore, the current research proposes a robust edge detector that can address some limitationsof the previous attempts. The proposed method incorporates a hybrid diffusion-steered functional derived from the total variation and the Perona-Malik diffusivities, which have been reported to can effectively capture semantic features in images. The empirical results show that our method generates clearer and stronger edge maps with higher perceptual and objective qualities. More importantly, the proposed method offers lower computational times—an advantage that gives more insights into the possible application of the method in time-sensitive tasks.
Evaluation of a stereo vision system for cotton row detection and boll location estimation in direct sunlight
(MDPI, 2020-08-05) Kadeghe, Fue; Wesley, Porter; Edward, Barnes; Changying, Li; Glen, Rains
Cotton harvesting is performed by using expensive combine harvesters which makes it difficult for small to medium-size cotton farmers to grow cotton economically. Advances in robotics have provided an opportunity to harvest cotton using small and robust autonomous rovers that can be deployed in the field as a “swarm” of harvesters, with each harvester responsible for a small hectarage. However, rovers need high-performance navigation to obtain the necessary precision for harvesting. Current precision harvesting systems depend heavily on Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) to navigate rows of crops. However, GNSS cannot be the only method used to navigate the farm because for robots to work as a coordinated multiagent unit on the same farm because they also require visual systems to navigate, avoid collisions, and to accommodate plant growth and canopy changes. Hence, the optical system remains to be a complementary method for increasing the efficiency of the GNSS. In this study, visual detection of cotton rows and bolls was developed, demonstrated, and evaluated. A pixel-based algorithm was used to calculate and determine the upper and lower part of the canopy of the cotton rows by assuming the normal distribution of the high and low depth pixels. The left and right rows were detected by using perspective transformation and pixel-based sliding window algorithms. Then, the system determined the Bayesian score of the detection and calculated the center of the rows for the smooth navigation of the rover. This visual system achieved an accuracy of 92.3% and an F1 score of 0.951 for the detection of cotton rows. Furthermore, the same stereo vision system was used to detect the location of the cotton bolls. A comparison of the cotton bolls’ distances above the ground to the manual measurements showed that the system achieved an average R2 value of 99% with a root mean square error (RMSE) of 9 mm when stationary and 95% with an RMSE of 34 mm when moving at approximately 0.64 km/h. The rover might have needed to stop several times to improve its detection accuracy or move more slowly. Therefore, the accuracy obtained in row detection and boll location estimation is favorable for use in a cotton harvesting robotic system. Future research should involve testing of the models in a large farm with undefoliated plants.
Autonomous navigation of a center-articulated and Hydrostatic transmission rover using a modified Pure pursuit algorithm in a cotton field
(MDPI, 2020-08-07) Kadeghe, Fue; Wesley, Porter; Edward, Barnes; Changying, Li; Glen, Rains
This study proposes an algorithm that controls an autonomous, multi-purpose, center-articulated hydrostatic transmission rover to navigate along crop rows. This multi-purpose rover (MPR) is being developed to harvest undefoliated cotton to expand the harvest window to up to 50 days. The rover would harvest cotton in teams by performing several passes as the bolls become ready to harvest. We propose that a small robot could make cotton production more profitable for farmers and more accessible to owners of smaller plots of land who cannot afford large tractors and harvesting equipment. The rover was localized with a low-cost Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS), encoders, and Inertial Measurement Unit (IMU)s for heading. Robot Operating System (ROS)-based software was developed to harness the sensor information, localize the rover, and execute path following controls. To test the localization and modified pure-pursuit path-following controls, first, GNSS waypoints were obtained by manually steering the rover over the rows followed by the rover autonomously driving over the rows. The results showed that the robot achieved a mean absolute error (MAE) of 0.04 m, 0.06 m, and 0.09 m for the first, second and third passes of the experiment, respectively. The robot achieved an MAE of 0.06 m. When turning at the end of the row, the MAE from the RTK-GNSS-generated path was 0.24 m. The turning errors were acceptable for the open field at the end of the row. Errors while driving down the row did damage the plants by moving close to the plants’ stems, and these errors likely would not impede operations designed for the MPR. Therefore, the designed rover and control algorithms are good and can be used for cotton harvesting operations.
Assessing potential land and surface water resources available and suitable for irrigated agriculture in the wami sub-basin Morogoro
(Sokoine University of Agriculture, 2021) Malekela, Charles John
Assessing potential land and water resources suitable for surface irrigation is essential for proper planning of their utilization types. The assessment has a great role in satisfying subsistence requirements, increasing agricultural production and hence reducing poverty. Despite efforts made by various stakeholders to improve agricultural productivity by increasing irrigated areas, Tanzania is still facing a daunting task of reaching the one million hectare target of irrigated area. This indicates that land and water resources are not presently effectively utilized. This study was initiated with the objective of assessing the land and water resources suitable for irrigated agriculture along with the extent of small-scale irrigation in the Wami sub-basin. Geographical Information System (GIS) based on Multi Criteria Decision Analysis (MCDA) was used along with various spatial tools including a model builder which was used to create geo-referenced maps of land and water resources. Ten layers (irrigation suitability factors) were used in the analysis for identification of potential land suitable for irrigated agriculture. Results indicate that, based on the suitability factors about 841.39 km 2 (3.11% of the total area), is highly suitable for surface irrigation, about 18,244.41 km 2 (67.51%), is moderately suitable and 7939.87 km 2 (29.38%), is marginally suitable for surface irrigation. Furthermore, results shows that the extent of small-scale irrigation is about 1958.87 km 2 . Moreover, results indicates that, approximately 1958 km 2 of land assumed to represent the extent of small- scale irrigated areas in Morogoro region including Dakawa and Mvomero in particular. As such, the exploration of various resources as observed in this study, including land, soil and water was well demonstrated by the integration of GIS-Based Multi Criteria Decision Analysis (MCDA), and the weighted overlay technique for land suitability analysis.

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