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Production of California table olives has declined significantly in recent years due to hand harvesting costs, often over 60% of gross return. Mechanical harvesting could sharply decrease harvest costs, increasing economic viability. Mechanical harvester efficiency is a combination of the percentage of the total fruit on a tree removed by a harvester, and the time required to do so. A comparison between an experimental canopy contact shaker and a commercial trunk shaker demonstrated low harvest efficiencies and no significant differences in harvester efficiency between the two, averaging no more than 8%. However, simultaneously combining both shaking methods increased fruit removal to an economically feasible 75% and produced better fruit quality. Combining both shaking methods increased the price per ton by 63% versus trunk shaking and 35% versus canopy shaking. These results suggest a mechanical olive harvester that simultaneously combines trunk and canopy shaking is more efficient than either shaking method alone, and, has potential for economically feasible mechanical table olive harvesting.

Objective Lodging is the permanent horizontal growth of stem and has been considered as a limiting factor in cereal production. Identification of traits that affect grain yield is very important in breeding programs. Furthermore, genetic diversity is essential for breeding programs and increasing selection efficiency. This research was set up to study genetic diversity, evaluation of biomechanical traits, assess lodging stem and detect quantitative traits loci (QTLs) in a RIL population of bread wheat. Materials and methods A large population including 225 bread wheat lines was assessed in a randomized complete block design with two replications in research field of Shahid Bahonar University of Kerman during 2017-2018 growing season. To define biomechanical properties of stems, a universal testing machine equipped with 3-point bend and shear test probes was used. Results Results of ANOVA showed significant difference between wheat genotypes for all traits. Modulus of elasticity of stem had the highest response to selection (21.65) in comparison with other traits. Moreover, this trait had high genetic variation. Based on these results, modulus of elasticity of each individual plant stem can be used as an effective factor in selection programs. Plant height had the highest narrow sense heritability (0.59) among evaluated traits. Cross section area had strong and significant correlation with moment of inertia (r=0.818**). In the molecular assay of this research, linkage map and QTL mapping of traits performed by inclusive composite interval mapping (ICIM) method. Linkage map was constructed based on diversity array technology (DArTs) and SSR markers. Conclusions A total of 12 QTLs have been identified which had LOD higher than 2.5. These QTLs were controlling 6 biomechanical traits including: specific shear energy, maximum shear energy, moment of inertia, flexural stiffness, maximum specific bending strength and modulus of elasticity. The information of identified QTLs could be used in wheat breeding programs using marker assisted selection.

The accurate evaluation of maize plants’ uniformity aligned with an effective improvement in germination and biomass assessment is of paramount importance for farmers. Early detection of stand count provides details on uneven emergence for farmers to make a prompt decision for replanting and applying proper agricultural inputs at defective zones. However, conventional, ground-based stand count methods are costly, time-intensive, and the accuracy of the counting method heavily depends on the selected area. This study focuses on validating the potential application of high resolution unmanned aerial vehicle (UAV) images for detecting the total number of maize plants and stand uniformity soon after maize germination. A field experiment was conducted to evaluate proper image processing algorithm for detecting the maize crop and calculating the distance between adjacent maize plants within a row. In the mosaicked images, the pixels including maize plants were segmented using two methods including excess green index (EXG) method and k-means clustering-segmentation technique. The mean accuracy using EXG method was 46%, however, the k-means clustering-segmentation method satisfactorily identified plants with mean accuracy of 91% in the field. The planting uniformity of maize plants was also evaluated based on three indices including miss index, multiple index, and coefficient of precision. Results showed that plant stand assessment using mosaicked images was closer to the values set on the planting machine. The marginal discrepancy between estimated and observed plant stand count and other indices confirms the high accuracy of UAV mosaicked images in plant density estimation for growers to make appropriate management decisions.

As the size and weight of agricultural equipment have increased significantly in the past few decades, the severity and depth of compacted zone may have increased proportionately. Past research indicates that soil compaction affects crop growth and grain yield. Very few studies have been conducted in North Dakota (ND) to understand soil compaction under the current machinery, and its effect on crop growth and yield. The research was conducted on a no-till crop field at Jamestown, ND, USA for 2013 (corn) and 2014 (soybean) growing season. The objective of this study was to evaluate the effect of wheel traffic on soil strength indices and its impact on crop emergence, development and yield. The study also evaluated the effect of winter freezing-thawing cycle on soil compaction in the study field. The experiment consisted of five soil transects and two traffic conditions based on machinery traffic in the field for both years such as most trafficked (MT) rows and least trafficked (LT) rows, laid out in a randomized complete block design with three replicates in strip-plot with space for corn season in 2013, and for soybean season in 2014. Data collected included soil resistance or cone index (CI), soil bulk density, soil moisture content, plant emergence, plant height and grain yield. The results showed that CI values followed a similar pattern for different soil transects up to 37.5 cm depth and then increased sharply. An average CI of 1.19 MPa was noted over the whole profile at 0–45 cm depth for the study area and not significantly different between MT and LT rows for both years. Moderate compaction resulted in early emergence of corn plants in MT rows by 175% compared to LT rows. The plant height didn’t show any significant difference between MT and LT rows for both years. The yield data showed significant difference between the soil transects, but no difference was observed between MT and LT rows in both 2013 and 2014 season. The interactions between soil transects and traffic conditions were not significantly different for all soil and plant related dependent variables. The freeze-thaw cycle occurred during winter from 2013 to 2014 and 2014 to 2015 alleviated soil resistance over the whole soil profile at 0–45 cm depth. Results show that different crops grown in a no till field are not very much influenced by wheel traffic. The study also suggests that moderate compaction occurred after harvest in a no till field could be alleviated by the effect of freeze thaw cycle.

Soybean aphids affects crop growth and causes significant yield losses in soybean production.Manual scouting and counting of soybean aphids is laborious and time consuming.Algorithm developed based on image processing techniques is useful to detect and count aphids.Different image resolutions and illumination conditions affect accuracy of the algorithm. Soybean aphid (Aphis glycines) is one of the most important insect pests of soybeans in North America. Insecticide application is performed if the aphids count exceeds the economic threshold of 250 per plant. Precise estimates of aphid densities are needed for field conditions to maximize insecticide application efficiency. The current method of identifying and counting aphids on a plant is a labor-intensive and time consuming process. The objective of this study was to use image processing technique to detect and count different sized soybean aphids on a soybean leaf. The trials were conducted with soybean plants grown in a greenhouse. Three sets of data were collected on different dates using replicate plants from 4 soybean varieties infested with a range of aphid densities. Images of infested soybean trifoliate leaves were captured with different cameras under 2 different illumination conditions with different cameras used across the different data sets. The images captured were processed in MATLAB R2014a software using the Image Processing Toolbox to identify and count aphids. In order to evaluate the accuracy of the algorithm, the aphids counted with the sensing system were compared to a count generated manually by a trained expert. The algorithm counting with SONY camera images correlated (r2=0.96) very well with manual counts. The misclassification percentage was low for most cameras with different resolutions under high illumination conditions. The results also showed that images captured with an inexpensive regular digital camera gave satisfactory results under high illumination conditions.

Soybean aphids are serious pests, causing negative yield impacts in the crop. Assessing their population is essential for making appropriate pesticide application decisions. Manual identification and counting, which is commonly performed to determine the economic threshold level, is time-consuming, laborious, and causes visual fatigue. In this study, an automatic image processing method was developed to identify and count aphids as well as exoskeletons and leaf spots on soybean leaves based on shape analysis. Aphid-infested soybean trifoliates were obtained at three infestation rates (low, medium, and high). Images of the front sides of the leaves were captured in the laboratory with three cameras (digital single-lens reflex or DSLR, consumer-grade digital, and smartphone) under two lighting conditions (direct and indirect). The shape parameters considered were area, perimeter, convex area, eccentricity, aspect ratio, solidity, hollowness, and roundness. Among the shape parameters tested, hollowness was the best in identifying aphids and was therefore used for developing the object classification algorithm. Of the three cameras tested, images from the consumer-grade digital camera produced the best identification accuracy (>82.4%), followed by the DSLR camera (>81.2%) and smartphone camera (>37.9%). Statistical analysis revealed that the accuracies did not differ significantly under different lighting conditions (p = 0.43), but the accuracies differed for the smartphone camera compared to the DSLR and consumer-grade digital cameras (p = 8.87 × 10-10). The results of automatic and manual counting were very well correlated (r = 0.92). The automatic image processing method achieved more rapid counting (

Background Active optical crop sensors have been gaining importance to determine in-season nitrogen (N) fertilization requirements for on-the-go variable rate applications. Although most of these active in-field crop sensors have been evaluated in maize (Zea mays L.) and wheat (Triticum aestivum L. emend. Thell.), these sensors have not been evaluated in soybean [Glycine max (L.) Merr.] production systems in North Dakota, USA. Recent research from both South Dakota and North Dakota, USA indicate that in-season N application in soybean can increase soybean yield under certain conditions. Results The study revealed that OptRx™ sensor reading did not show any significant differences from early to midway through the growing season. The NDRE (normalized difference red edge) index data collected towards the end of the growing season showed significantly higher values for some of the N treatments as compared to others in both years. The NDRE values were strongly correlated to grain yield for both years under tiled (r = 0.923) and non-tiled (r = 0.901) drainage conditions. Certain soybean varieties displayed significantly higher NDRE values over both years. The three varieties tested across years, under both tiled and non-tiled conditions, showed a significant linear relationship between late August NDRE values and yield (R2 = 0.85 for tiled and R2 = 0.81 for non-tiled). Conclusion In this research, the study results show that the OptRx™ sensor has the potential to work for soybean as well, though later in the crop growing season. Further investigation is needed to confirm the use of OptRx™ sensor for variable rate in-season N applications in soybeans. © 2019 Society of Chemical Industry.

Temperature is one of the most important plant growth parameters that should be controlled in the greenhouses. The present study was aimed to assess the thermal behavior of a greenhouse with and without the fan and pad (FP) evaporative cooling system. A method was developed to approximate the greenhouse temperature based on the mass and energy balance equations. For this purpose, both of the fan and pad evaporative cooling system, and the greenhouse were studied. The results of the theoretical analysis were compared with those achieved by the experiments. Maximum deviations of 5.32, 5.56 and 4.53oC were observed between the theoretical and experimental temperatures of the inside air, the floor and the cover of the greenhouse without the cooling system, respectively. Whereas, the mean absolute error values associated with the predicted temperatures of the greenhouse with the FP system were ranged between 1.50 and 25.67%. Based on the obtained values for the correlation coefficient, root mean square error and mean absolute magnitude error, it was concluded that the models satisfactorily predicted the temperature of the greenhouse components. An air circulation system inside the greenhouse can be proposed to maintain the lumped condition even at the high temperatures, and lead to smaller errors. The results indicated that the inside air, the floor and the cover temperature of the greenhouse reduced by respectively 20.6, 13.0 and 20.6 oC when using the FP system with the air velocity of 4.4 ms-1 and the pad thickness of 6 cm.

Sentinel 2 is the newest satellite that has a high spatial resolution and a 10 day equator with one satellite, and 5 day equator with 2 satellites. Sentinel is one of the data providers in Landsat and Modis satellite group and working in a time interval between these two satellites. The identification and exploration of vegetation indices on this satellite seems to be necessary. The aim of this study was to compare sentinel and Landsat satellite imagery in date palm identification. In the study area, three main types of land cover (alfalfa, date palm, and soil) were identified to compare in the images of these two satellites. To evaluate these aerial imageries based on previous literatures, four supervised classification methods of Neural Network (NN), Maximum Likelihood Classifier (MLC), Support Vector Machines (SVM) and Mahalanobis Distance Classifier (MDC) were used. The classification results showed that the overall accuracy of the classification in sentinel images by using NN, MLC, SVM and MDC were 99.57% (Kapa 0.99), 98.47% (Kappa 0.97), 99.63% (Kappa 0.99) and 98.37% (Kappa 0.97) respectively. For Landsat images by using NN, MLC, SVM and MDC the overall accuracy and kappa were 98.86% (Kappa 0.98), 93.37% (Kappa 0.87), 99.14% (Kappa 0.98) and 97.33% (Kappa 0.94), respectively. The results of satellite imagery comparison showed that Sentinel images with an average overall accuracy of 99.63% and Landsat with 99.14% can identify the areas under date palm cultivation. Both satellites were also able to classify other land covers satisfactorily .

Yield monitoring systems for grain and some non-grain crops have been developed and are commercially available. However, no commercial method has been developed for on-the-go measurement of quality indices within the field and scientific studies are still in progress. Most of these efforts are based on NIR and RS techniques. These techniques have some limitations that make them impractical for in-field usage, besides, using these techniques and the required devices are not accessible in most low income and developing countries. Previous research showed good correlation between shear force needed to cut forage crop stems and their nutritional quality indices. In this study, we have tried to use this technique in field conditions to estimate quality indices during baling operation. To achieve this, a rectangular baler (John Deere - 349) was equipped with an appropriate load cell to measure the shear force needed to cut alfalfa stems on its stationary knife blade while each new charge of hay is introduced to the baling chamber. To account for the effect of hay slice thickness and density on shear force, an additional mass flow rate sensor was also installed on this machine. Different levels of crop quality treatments were selected to evaluate the suggested method. Hay samples were also collected from the test fields and their quality indices were measured using standard laboratory test methods. Primary results showed correlation between shear force and quality indices if the effects of mass flow rate and moisture content are canceled. Further investigations are needed to prove the creditability of using this real-time method in field conditions.