Yield sensing technologies for perennial and annual horticultural crops: a review

Yield maps provide a detailed account of crop production and potential revenue of a farm. This level of details enables a range of possibilities from improving input management, conducting on-farm experimentation, or generating profitability map, thus creating value for farmers. While this technology is widely available for field crops such as maize, soybean and grain, few yield sensing systems exist for horticultural crops such as berries, field vegetable or orchards. Nevertheless, a wide range of techniques and technologies have been investigated as potential means of sensing crop yield for horticultural crops. This paper reviews yield monitoring approaches that can be divided into proximal, either direct or indirect, and remote measurement principles. It reviews remote sensing as a way to estimate and forecast yield prior to harvest. For each approach, basic principles are explained as well as examples of application in horticultural crops and success rate. The different approaches provide whether a deterministic (direct measurement of weight for instance) or an empirical (capacitance measurements correlated to weight for instance) result, which may impact transferability. The discussion also covers the level of precision required for different tasks and the trend and future perspectives. This review demonstrated the need for more commercial solutions to map yield of horticultural crops. It also showed that several approaches have demonstrated high success rate and that combining technologies may be the best way to provide enough accuracy and robustness for future commercial systems

ENHANCED GRAIN CROP YIELD MONITOR ACCURACY THROUGH SENSOR FUSION AND POST-PROCESSING ALGORITHMS

Yield monitors have become an indispensable part of precision agriculture systemsbecause of their ability to measure the yield variability. Accurate yield monitor data availabilityis essential for the assessment of farm practices. The current technology of measuring grainyields is prone to errors that can be attributed to mass flow variations caused by the mechanismswithin a grain combine. Because of throughput variations, there are doubts regarding thecorrelation between the mass flow measurement and the actual grain volume produced at aspecific location. Another inaccuracy observed in yield monitor data can be attributed to inexactcut-widths values entered by the machine operator.To effectively address these yield monitor errors, two crop mass flow sensing deviceswere developed and used to correct yield monitor data. The two quantities associated with cropmaterial mass flow that were sensed were tension on the feeder housing drive chain and thehydraulic pressure on the threshing cylinder\u27s variable speed drive. Both sensing approacheswere capable of detecting zero mass flow conditions better than the traditional grain mass flowsensor. The alternative sensors also operate without being adversely affected by materialtransport delays. The feeder housing-based sensor was more sensitive to variations in cropmaterial throughput than the hydraulic pressure sensor. Crop mass flow is not a surrogate forgrain mass flow because of a weak relationship (R2 andlt; 0.60) between the two quantities. The cropmass flow signal does denote the location and magnitude of material throughput variations intothe combine. This delineation was used to redistribute grain mass flow by aligning grain andcrop mass flow transitions using sensor fusion techniques. Significant improvements (?? = 0.05)in yield distribution profile were found after the correction was applied.To address the cut-width entry error, a GIS-based post-processing algorithm wasdeveloped to calculate the true harvest area for each yield monitor data point. Based on theresults of this method, a combine operator can introduce yield calculation errors of 15%. Whenthese two correction methods applied to yield monitor data, the result is yield maps withdramatically improved yield estimates and enhanced spatial accuracy

Task-based agricultural mobile robots in arable farming: A review

In agriculture (in the context of this paper, the terms “agriculture” and “farming” refer to only the farming of crops and exclude the farming of animals), smart farming and automated agricultural technology have emerged as promising methodologies for increasing the crop productivity without sacrificing produce quality. The emergence of various robotics technologies has facilitated the application of these techniques in agricultural processes. However, incorporating this technology in farms has proven to be challenging because of the large variations in shape, size, rate and type of growth, type of produce, and environmental requirements for different types of crops. Agricultural processes are chains of systematic, repetitive, and time-dependent tasks. However, some agricultural processes differ based on the type of farming, namely permanent crop farming and arable farming. Permanent crop farming includes permanent crops or woody plants such as orchards and vineyards whereas arable farming includes temporary crops such as wheat and rice. Major operations in open arable farming include tilling, soil analysis, seeding, transplanting, crop scouting, pest control, weed removal and harvesting where robots can assist in performing all of these tasks. Each specific operation requires axillary devices and sensors with specific functions. This article reviews the latest advances in the application of mobile robots in these agricultural operations for open arable farming and provide an overview of the systems and techniques that are used. This article also discusses various challenges for future improvements in using reliable mobile robots for arable farmin

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Implementation and applications of harvest fleet route planning

In order to support the growing global population, it is necessary to increase food production efficiency and at the same time reduce its negative environmental impacts. This can be achieved by integrating diverse strategies from different scientific disciplines. As agriculture is becoming more data-driven by the use of technologies such as the Internet of Things, the efficiency in agricultural operations can be optimised in a sustainable manner. Some field operations, such as harvesting, are more complex and have higher potential for improvement than others, as they involve multiple and diverse vehicles with capacity constraints that require coordination. This can be achieved by optimised route planning, which is a combinatorial optimisation problem. Several studies have proposed different approaches to solve the problem. However, these studies have mainly a theoretical computer science perspective and lack the system perspective that covers the practical implementation and applications of optimised route planning in all field operations, being harvesting an important example to focus on. This requires an interdisciplinary approach, which is the aim of this Ph.D. project.The research of this Ph.D. study examined how Internet of Things technologies are applied in arable farming in general, and in particular in optimised route planning. The technology perspective of the reviewing process provided the necessary knowledge to address the physical implementation of a harvest fleet route planning tool that aims to minimise the total harvest time. From the environmental point of view, the risk of soil compaction resulting from vehicle traffic during harvest operations was assessed by comparing recorded vehicle data with the optimised solution of the harvest fleet route planning system. The results showed a reduction in traffic, which demonstrates that these optimisation tools can be part of the soil compaction mitigation strategy of a farm. And from the economic perspective, the optimised route planner of an autonomous field robot was employed to evaluate the economic consequences of altering the route in selective harvesting. The results presented different scenarios where selective harvest was not economically profitable. The results also identified some cases where selective harvest has the potential to become profitable depending on grain price differences and operational costs. In conclusion, these different perspectives to harvest fleet route planning showed the necessity of assessing future implementation and potential applications through interdisciplinarity

Yield prediction in ryegrass with UAV-based RGB and multispectral imaging

Forage grass breeding is time-consuming and costly, with the need for special knowledge and experience to make the right decisions for future forage grass production. All measurements for decision-making require manual labor and hands-on inspections. For the yield trait, the traditional method of measurement is cutting and weighing the grass. New methods for yield prediction and measurement with Unmanned Aerial Vehicle (UAV) have been tested on different crops with good results. For perennial ryegrass (Lolium perenne L.) yield prediction has earlier been performed on plots with a flight altitude for image capturing at 20 meters and which has yielded promising results for our study. This study has been exploring different flight altitudes for ryegrass yield prediction using UAV imagery. The sensors that have been used in this study are multispectral and RGB cameras integrated in the UAVs. Our study consists of two trials with pre-selected varieties of perennial ryegrass, one with diploid varieties and one with tetraploid varieties and mixtures between diploid and tetraploid varieties, were investigated. Both trials were seeded at two different locations in Norway. Varieties were planted as rows for the first location (Vollebekk, Ås, Norway) while for the second location (Arneberg, Ilseng, Norway) the two trials were planted as both rows and plots. The dry matter yield (DMY) data were collected with traditional harvest four times for the rows, and three times for the plots. The UAV-images were collected at different flight altitudes with both multispectral and RGB cameras. The full data processing routine was conducted on the first and second cut for both locations. Multivariate regression model was applied for DMY prediction based on UAV imagery. The results correlated well with the predictions at Ås for both multispectral images as well as RGB methods of image acquisition. Our results indicated a high correlation between the actual DMY and the predicted DMY from both RGB images as well as multispectral images with a correlation coefficient on 0.92 for both, but at different assessment dates. The maximum correlation was acquired for the first cut from location Ås. For location Arneberg, the acquired images could not yield results of sufficient quality, and thus, no predictions could be made