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Abstract: Knowledge of the vertical distribution of surface residues, chemicals, or seeds following tillage operations is of great importance to a wide variety of soil research areas. This paper describes a 1D empirical vertical soil tillage distribution model with 1cm grid spacing (SeedChaser) that predicts vertical redistribution of weed seeds following user selected (a) sequences of tillage implements, and (b) initial seed distribution values. Results of this model are particularly suited for weed seed emergence modeling. However, the model can be adapted easily to any surface broadcasted agrochemical or incorporated residues. The present model can handle up to 20 passes of user selected sequences of 16 different implements. The majority of prior models examined only the impact of a more limited list of implements at much larger depth intervals, which reduced the predictability of fine-scale vertical movement that may be needed for simulating movements of seeds or chemical granules. SeedChaser consolidates the results from these previous models along with new data on conservation tillage implements into a prediction tool that would have applications both in weed science, as well as other soil research areas. SeedChaser was developed in JAVA and is publicly available via the Internet. [Copyright &y& Elsevier]

Sunflower floral dimensions are essential for assessing pollinators attraction and estimating seed yields. Dimensions measured manually at present are subjective and time-intensive, therefore, an image processing method was developed as an alternative, which was objective, non-destructive, produces various outputs, and rapid. An ImageJ user-coded plugin with a field image acquisition method was developed to measure the dimensions of individual sunflower components, such as head, disc, and ray florets. Two measurement methods, direct (using the thresholded binary image) and wrapping-polygon (using a polygonal enclosure) were tested. The ‘pixel-march’ method made multiple radial dimension measurements (diameter) on the ray florets binary image in a single computation. The effect of multiple measurements (2, 4, 8, 16, 32, 64, 128, and 180 along 0–180° angles) was studied to determine an effective number of measurements, and user-friendly sunflower dimension prediction models from ImageJ’s standard output parameters were developed. Results indicated that (i) a minimum of 32 measurements for the sunflower head and ray florets dimensions, but only eight measurements for the sunflower disc, were necessary; (ii) wrapping-polygon method was efficient compared to direct; (iii) equivalent diameter (ED) and fitted ellipse minor axis (MinA) were well correlated ( r ≥ 0 . 88 ) to the accurate mean 180 measurements ( D 180 ) for all sunflower components; (iv) linear models for predicting D 180 using ED and MinA performed better ( R 2 > 0.99 ) for head and disc than for ray florets ( R 2 > 0.76 ); (v) user-friendly linear models using the mean of two manual measurements of the head ( D 2 h ) for predicting D 180 and area were good only for the head ( R 2 > 0 . 92 ), and not suitable for disc ( R 2 ≤ 0 . 62 ) and ray florets ( R 2 ≤ 0 . 43 ); and (vi) the developed image processing method results were accurate, quick ( ≈ 11 s in Windows 10, Intel Core i5, and 8 GB RAM laptop), and have the potential to be adapted to other species. [ABSTRACT FROM AUTHOR]

Economic analysis of forage production and handling aids in making an educated decision related to growing or buying forage, setting forage prices, and purchasing additional equipment. Manual calculation of economics is highly complex, tedious, and time-consuming. Therefore, a multi-device web-based tool, called the "forage economics calculator web tool" (FECWT) was developed using HTML, CSS, and JavaScript. The tool's interface was designed for a user-friendly experience with easy access to commands set in an easy-to-follow layout. The FECWT uses 29 input variables to generate results "dynamically" for 37 output variables, based either on the supplied default data or with user-provided inputs (imperial or metric units). The tool emphasizes the cost of the bale collection activity performed using tractor or tractor pulling an "automatic bale picker" (ABP), while the cost of other activities (planting, fertilizer & chemical application, harvesting, and baling) are provided as direct inputs. The economic analysis outputs include net return, break-even ratio, payback period, and return on investment. Features of FECWT include the sensitivity of profit to changes in input values, and downloadable reports and charts for record-keeping. Two case studies using farmers' actual field data inputs demonstrated the FECWT's effectiveness, with its generated results corroborating well with farmers' records. The tool allows for "what-if" scenario analysis related to equipment purchase, effect of forage crop, net-return, and low-inputs scenario for different field areas suggests that an ABP purchase for field areas greater than ≥ 65 ha (≥ 160 ac) could be profitable; alfalfa and corn were the most profitable forage and grain-forage crops, where the grain revenue stream of grain-forage crops easily covers the costs and makes a profit; and a low-inputs scenario (no fertilizers, chemicals, and/or seeds) for forages (alfalfa and grass) decreased the net returns on average by 8.1 times at 50% forage yield potential suggesting the use of all inputs and increased field areas (≥ 65 ha) make a better profit in this low-inputs scenario. Potential users of this web tool include farmers, hay producers, custom hay operators, educators, agricultural extension and financial personnel, and others interested in the economics of handling bales. [Display omitted] • Web tool calculator developed uses 29 inputs to generate 37 forage economics outputs. • Economics of bale collection using tractor and automatic bale picker is emphasized. • Tool's features include inputs sensitivity and downloadable report and chart. • Tool allows what-if scenario analysis for many practical field situations. • Actual field data case studies reported satisfactory performance of the tool. [ABSTRACT FROM AUTHOR]

[Display omitted] • Plant stand count methods using unmanned aerial vehicle imagery were reviewed. • Computer vision methods produced promising results in plant stand count analysis. • Several research gaps were identified and recommendations provided. • Results will be helpful to stakeholders like farmers, producers, and researchers. Plant stand count helps in estimating the yield and evaluating the planter's efficiency and seed quality. Traditional methods of counting the plants by manual measurement are time-consuming, laborious, and error-prone. In contrast, the ground-based sensing methods are limited to smaller spaces. High spatial resolution images obtained from unmanned aerial vehicles (UAV) can be used in conjunction with computer vision algorithms to evaluate plant stand count, as it directly influences the yield. In spite of the importance of high-throughput plant stand count in row crop agriculture, no synthesized information in this specific subject matter is available. Therefore, the objective of this paper was to perform a systematic literature review of the current studies that focus on evaluating plant stand count using UAV imagery to provide well-synthesized information, identify research gaps, and provide suitable recommendations. In this study, a comprehensive literature search was performed on three academic databases (Agricola, Web of Science, and Scopus), and a total of 29 articles were found based on search terms and selection criteria for review. From the systematic review, it can be concluded that: an appropriate stage after plant emergence without canopy overlap is necessary for image acquisition; optimal flying height should be selected to balance the field coverage and accuracy; L*a*b* color space can provide better segmentation; hyperspectral camera imagery can provide good discrimination; deep learning with data augmentation and transfer learning models can be used to reduce the computational time and resources; the stand count methodology that is successful with corn and cotton could be extended to other row crops and horticultural crops; and application of direct image processing and use of open-source platforms is required for stakeholder participation. The review will be helpful to the farmers, producers, and researchers in selecting and employing the UAV-based algorithms for evaluating plant stand count. [ABSTRACT FROM AUTHOR]