Drying is an energy-intensive step in walnut production, accounting for 19% of processing costs and contributing to greenhouse gas emissions. This study surveyed 19 California walnut drying facilities to update energy consumption estimates and identify factors influencing energy use. The variables analyzed included production volume, implementation of energy conservation strategies, the type of fuel used for drying, and ambient air conditions (temperature and relative humidity). The results show that the average energy consumption for walnut drying in California was 1.156 million British thermal units per short ton of in-shell dried walnuts, 11.1% lower than 2009 estimates. Natural gas facilities used 41.5% less energy than those using propane. The type of fuel used for drying was the only factor that significantly affected energy consumption; energy conservation strategies, including in-bin moisture meters and air recirculation, did not have a significant impact. This suggests the need for improved application of energy conservation strategies through better automation and operator training.

This paper presented an a pioneering method for real-time robotic weed control in vegetable fields characterized by high weed densities, particularly in challenging scenarios where the foliage of crop plants was intricately intertwines with weeds, causing significant occlusion. This issue was particularly pronounced in organic farming environments where conventional classification algorithms often fell short. Our proposed method, termed crop signalling, involved the pre-transplantation treatment of celery crop plants with Rhodamine B (Rh-B), a fluorescent compound with unique optical properties that generated distinct signals readable by machines to effectively discern crop plants from weeds. A custom illumination system was specifically designed to excite the fluorescence properties of the Rh-B dye, facilitating visualization. Concurrently, a dedicated machine vision algorithm was developed not only to differentiate crop plants from weeds but also to accurately determine the stem locations of crop plants as they enter the soil, facilitating targeted weed-knife control. Experimental results showcased exceptional performance, achieving a 100% accuracy rate in detecting and distinguishing crop plants from weeds in densely populated fields, with no instances of false positives. The algorithm demonstrated a high precision rate of 99.66% in identifying celery plant stem locations across 313 images. This research represented a significant advancement in spectral fluorescence imaging for precision weed and crop classification, offering promising prospects for the application of robotic weed control in celery cultivation.

Drying is an energy-intensive step in walnut production, accounting for 19% of processing costs and contributing to greenhouse gas emissions. This study surveyed 19 California walnut drying facilities to update energy consumption estimates and identify factors influencing energy use. The variables analyzed included production volume, implementation of energy conservation strategies, the type of fuel used for drying, and ambient air conditions (temperature and relative humidity). The results show that the average energy consumption for walnut drying in California was 1.156 million British thermal units per short ton of in-shell dried walnuts, 11.1% lower than 2009 estimates. Natural gas facilities used 41.5% less energy than those using propane. The type of fuel used for drying was the only factor that significantly affected energy consumption; energy conservation strategies, including in-bin moisture meters and air recirculation, did not have a significant impact. This suggests the need for improved application of energy conservation strategies through better automation and operator training.

Commercial vegetable crop transplanters currently use several unsynchronized planting units mounted to a common transport frame.The objective of this work was to assess the performance of a new transplanting technology to improve the plant placement accuracy and spatiotemporal planting synchronization across adjacent rows, thus producing a grid-like planting pattern using adjacent vegetable crop transplanters.The feasibility of synchronization of adjacent transplanting units for vegetable crops was demonstrated using tomato as the target crop.A colour, digital, high-speed computer vision analysis of the motion and dynamics of the plant trajectories of transplanted tomatoes was conducted.The high-speed video analysis led to the design and testing of an improved plant support mechanism to enhance the control and precision of the transplanting of vegetable crops.The absolute deviation values of the final location in the soil were reduced by approximately 25% for both the right planter and left planter compared to those in previous years.These results serve as the fundamental basis for a mechatronic system that Con formato: Normal can precisely transplant vegetable crops in a grid-like pattern across rows as a critical first step in a systematic approach to fully automated individual plant care.

Wet basis moisture content (MCwb) is an important quality parameter of green coffee as it affects the coffee's physical, chemical, and sensory characteristics. Accurate estimation of green coffee MCwb after dry hulling, longterm storage, and transportation is imperative to prevent quantitative and qualitative losses. Thus, this study aimed to design, develop, calibrate and validate a prototype inline system capable of accurately measuring the MCwb of green coffee beans, using a commercially available time-domain reflectometry (TDR) probe. The TDR probe was calibrated and validated with green coffee within a MCwb range of 9-21%. A calibration linear regression model correlating the TDR probe output (dielectric constant) to reference MCwb measurements obtained by a halogen moisture analyzer, yielded a high coefficient of correlation (R2 = 0.99). Model validation yielded a high R2, and a low Root Mean Squared Error equal to 0.93, and 0.9% MCwb, subsequently. Results indicate that the TDR inline green coffee moisture estimation system has the potential to be applied in real-time, industrial-scale operations.

Near-infrared (NIR) spectroscopy has been used to non-destructively and rapidly evaluate the quality of fresh agricultural produce. In this study, two commercially available portable spectrometers (F-750: Felix Instruments, WA, USA; and SCiO: Consumer Physics, Tel Aviv, Israel) were evaluated in the wavelength range between 740 and 1070 nm to non-invasively predict quality attributes, including the dry matter (DM), and total soluble solids (TSS) content of three fresh table grape cultivars (‘Autumn Royal’, ‘Timpson’, and ‘Sweet Scarlet’) and one peach cultivar (‘Cassie’). Prediction models were developed using partial least-square regression (PLSR) to correlate the NIR absorbance spectra with the invasive quality measurements. In regard to grapes, the best DM prediction models yielded an R2 of 0.83 and 0.81, a ratio of standard error of performance to standard deviation (RPD) of 2.35 and 2.29, and a root mean square error of prediction (RMSEP) of 1.40 and 1.44; and the best TSS prediction models generated an R2 of 0.97 and 0.95, an RPD of 5.95 and 4.48, and an RMSEP of 0.53 and 0.70 for the F-750 and SCiO spectrometers, respectively. Overall, PLSR prediction models using both spectrometers were promising to predict table grape quality attributes. Regarding peach, the PLSR prediction models did not perform as well as in grapes, as DM prediction models resulted in an R2 of 0.81 and 0.67, an RPD of 2.24 and 1.74, and an RMSEP of 1.28 and 1.66; and TSS resulted in an R2 of 0.62 and 0.55, an RPD of 1.55 and 1.48, and an RMSEP of 1.19 and 1.25 for the F-750 and SCiO spectrometers, respectively. Overall, the F-750 spectrometer prediction models performed better than those generated by using the SCiO spectrometer data.

The advent of automated technology in agriculture employing robots allows researchers and engineers to automate many of the tasks in a semi-structured, natural farming envi-ronment where these tasks need to be performed. Here we propose a fast-intelligent weed control system using a crop signalling concept with machine vision and a precision micro-jet sprayer to target in-row weeds for precision herbicide application. Crop signalling is a novel technology invented to read crop plants by machine to simplify the task of differ-entiating vegetable crops from weeds for selective weed control in real-time. In-row weed control in vegetable crops like lettuce requires a very precise herbicide spray resolution with a fast response time. A novel, accurate, high-speed, centimetre precision spray tar-geting actuator system was designed and experimentally validated in synchronization with a machine vision system to spray detected weeds located between lettuce plants. The system processed an image, representing a 120 mm x 180 mm region of row-crop in 80 ms, which allowed the micro-jet sprayer to successfully function at a travel speed of 3.2 km h-1 and selectively deliver herbicide to the weed targets. The analysis of the overall perfor-mance of the system to kill weeds in indoor experimental trials is discussed and presented. Findings indicate that 98% weeds were correctly sprayed which indicates the efficacy and robustness of the proposed systems.(c) 2023 The Author(s). Published by Elsevier Ltd on behalf of IAgrE. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

The cover image is based on the Research Article A strawberry harvest-aiding system with crop-transport collaborative robots: Design, development, and field evaluation by Chen Peng et al., https://doi.org/10.1002/rob.22106.

Mechanizing the manual harvesting of fresh market fruits constitutes one of the biggest challenges to the sustainability of the fruit industry. During manual harvesting of some fresh‐market crops like strawberries and table grapes, pickers spend significant amounts of time walking to carry full trays to a collection station at the edge of the field. A step toward increasing harvest automation for such crops is to deploy harvest‐aid collaborative robots (co‐bots) that transport empty and full trays, thus increasing harvest efficiency by reducing pickers' non‐productive walking times. This study presents the development of a co‐robotic harvest‐aid system and its evaluation during commercial strawberry harvesting. At the heart of the system lies a predictive stochastic scheduling algorithm that minimizes the expected non‐picking time, thus maximizing the harvest efficiency. During the evaluation experiments, the co‐robots improved the mean harvesting efficiency by around 10% and reduced the mean non‐productive time by 60%, when the robot‐to‐picker ratio was 1:3. The concepts developed in this study can be applied to robotic harvest‐aids for other manually harvested crops that involve walking for crop transportation.

Commercial vegetable crop transplanters currently use several unsynchronised planting units mounted to a common transport frame. The objective of this work was to assess the performance of a new transplanting technology to improve the plant placement accuracy and spatiotemporal planting synchronization across adjacent rows, thus producing a grid like planting pattern using adjacent vegetable crop transplanters. The feasibility of synchronisation of adjacent transplanting units for vegetable crops was demonstrated using tomato as the target crop. A colour, digital, high-speed computer vision analysis of the motion and dynamics of the plant trajectories of transplanted tomatoes was conducted. The high-speed video analysis led to the design and testing of an improved plant support mechanism to enhance the control and precision of the transplanting of vegetable crops. The absolute deviation values of the final location in the soil were reduced by approximately 25% for both the right planter and left planter compared to those in previous years. These results serve as the fundamental basis for a mechatronic system that can precisely transplant vegetable crops in a grid-like pattern across rows as a critical first step in a systematic approach to fully automated individual plant care. (c) 2021 IAgrE. Published by Elsevier Ltd. All rights reserved.

Abstract. Conventional cultivation works to control weeds between the rows, but it ignores the weeds in crop rows which are most competitive with crops. This article introduces the latest advances in a new systematic method for automatic identification of celery for weed control. The paper describes the development of a novel root treatment developed to create a machine-readable vegetable crop plant and matching fluorescence macroscope that can work together to recognize vegetable crop plants growing in fields with high weed densities and high levels of leaf obscuration. Rhodamine B (Rh–B) is an efficient systemic compound to label crop plants due to its membrane permeability and unique fluorescent properties. Rh–B solution at 60 ppm was applied to the celery roots prior to transplantation to evaluate Rh–B persistence in plants under sunlight. Systemic Rh-B absorbed via the roots moved throughout the plants. After exposed to full sunlight for 4 weeks, the Rh–B was still detectable in stems and leaves of plants. Celery was tolerant to the root treatment. The systemic Rh-B allowed for the rapid identification of plants, thereby facilitating the automatic differentiation of weeds and crops by a robotic fluorescence macroscope.

•A color vision system (CVS) was designed to predict walnut kernel color.•The CVS removes bias, adds reliability and speed to the color evaluation process.•The CVS depicts walnut color with less variability than a colorimeter measurement.•The CVS can be used during commercial and breeding walnut kernel color grading.•Color predictions models per cultivar are more reliable than a full generic model.

Vegetable crop productivity is susceptible to damage from weed competition, with early season weeds a control priority to prevent significant yield loss. There is an urgent need for a reliable robotic sensing system that can work well in a variety of crops to achieve universal weed/crop differentiation, which would facilitate further development in robotic technologies for farming and bring economic benefits to vegetable production. The aim of this study was to develop a novel technique to create a machine-readable crop plant using a systemic crop signalling compound applied to seeds or transplants. The protocols for the crop signalling method and its detection are described. Rhodamine B (Rh-B) was selected as the signalling compound in this study, because it could be used as a fluorescent tracer, had a unique optical appearance in plants, and had the necessary properties to allow systemic behaviour in vegetable seedlings. The Rh-B tracer was applied to snap bean and the systemic behaviour analysed with a fluorescent macroscope. The uptake of Rh-B varied among treatment methods. The Rh-B uptake through the seed coat of snap beans was found mainly in the seedling hypocotyls. The results for root uptake showed that Rh-B could be more readily transported to the whole plant through the root system as compared to the application to seeds. The midvein and secondary veins of bean leaves showed stronger Rh-B fluorescence than other regions of the leaf. Higher concentrations of Rh-B resulted in greater absorption by the plant. Although the crop signalling compound could follow both seed and root pathways for plant uptake, the uptake based on the root pathway had greater capacity than that of the seed pathway. The use of Rh-B provided a systemic crop signalling compound was discussed on further research and field tested to have application to enhance weed/crop differentiation by automated weeders in vegetable crops. The systemic crop signalling system successfully created a machine-readable signal on vegetable crops, and appeared to be non-destructive, cost effective, efficient and accurate for performing automatic plant care tasks. (C) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.

Robotic weed control for vegetables is necessary to increase crop productivity, avoid intensive hand weeding as labour shortages in developed countries such as United States has led to a surge in food production costs. However, development of a reliable, intelligent robotic system for weed control in real-time for vegetables still remains a challenging task. The main issue arises while distinguishing crops from weeds in real-time. In this paper, a novel technique to crop signalling to distinguish crops from in-row weeds in complex natural scenarios, such as high weed densities commonly found on organic farms, in real-time using machine vision is presented. Crop signalling is a simple and low-cost technique in which a signalling compound is produced by or applied to the crop and where the signalling compound is machine readable and helps to create visual features that uniquely distinguish the crops from weeds. The crop and weed mapping algorithm presented here were specially designed and developed for a vision-based weeding robot equipped with a micro-jet herbicide-spraying system for weed control in a lettuce field. The proposed technique involves weed/crop mapping and decision making. Experimental results show that the crop detection accuracy was 99.75%, and 98.11% of sprayable weeds were detected. The proposed technique is highly accurate, reliable and more robust than other sensor-based techniques presented in the literature. (C) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.

A novel technique for enabling robotic weed control in a commercial processing tomato field having densely populated weeds is described. It is necessary to accurately locate the stem emerging points (SEPs) of crop plants for the successful application of a mechanical weeding actuator to remove weeds during automated weeding. However, it is a difficult and challenging task to locate the SEPs in complex natural scenarios such as when the main stem is occluded by weeds or crop foliage, the crop plants are lying on the soil surface, there are non-uniform planting bed conditions, or there is leaf damage due to insects etc. To overcome these challenges a novel crop signalling concept has been proposed to mark the crop plants at planting to make them machine-readable. Plants lacking this crop signal were classified as weeds and removed by the robotic weed knife actuator. A machine-vision algorithm was developed to analyse the seven views of the crop plants taken by camera with help of a specially designed imaging chamber and locate the SEPs of tomato plants, which was passed to the robotic weed knife control algorithm to remove weeds. The algorithm was successfully detected and located the main stems of tomato plants in outdoor environment with success rate of 99.19% while traveling at a speed of 3.2 km h(-1) with a processing time for all views of 30 ms f(-1) (C) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.

Automated weed management tools in vegetable crops are needed to reduce or eliminate hand-weeding because of labour shortages and cost. Distinguishing crop plants from weeds in complex natural scenes of crop-weed mixtures remains a challenge for weed management automation. This paper presents a novel solution to the weed control problem by employing crop signalling technology: a novel systems approach that creates a machine-readable crop plant. A robot-vision-based weed-knife control system with a novel three-dimensional geometric detection algorithm was developed to automate weed control for tomato and lettuce crops. The system successfully detected the crop signal from occluded crop plants while traveling at speeds up to of 3.2 km h(-1). The in-field experiments show that the system is able to reduce the number of weed plants by 83% in the seedling area. Crop detection accuracy was measured at 97.8% (precision 0.998 and recall 0.952) with a detection time of 30 ms f(-1). This paper also shows that the crop signalling system has the advantage that prior knowledge of visual features of each crop and weed species is not required and poor visual appearance of the crop plants or weeds does not affect system performance. (C) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.

Increasing weed control costs and limited herbicide options threaten vegetable crop profitability. Traditional interrow mechanical cultivation is very effective at removing weeds between crop rows. However, weed control within the crop rows is necessary to establish the crop and prevent yield loss. Currently, many vegetable crops require hand weeding to remove weeds within the row that remain after traditional cultivation and herbicide use. Intelligent cultivators have come into commercial use to remove intrarow weeds and reduce cost of hand weeding. Intelligent cultivators currently on the market such as the Robovator, use pattern recognition to detect the crop row. These cultivators do not differentiate crops and weeds and do not work well among high weed populations. One approach to differentiate weeds is to place a machine-detectable mark or signal on the crop (i.e., the crop has the mark and the weed does not), thereby facilitating weed/crop differentiation. Lettuce and tomato plants were marked with labels and topical markers, then cultivated with an intelligent cultivator programmed to identify the markers. Results from field trials in marked tomato and lettuce found that the intelligent cultivator removed 90% more weeds from tomato and 66% more weeds from lettuce than standard cultivators without reducing yields. Accurate crop and weed differentiation described here resulted in a 45% to 48% reduction in hand-weeding time per hectare.

Our engineering group at UC Davis has modified a tractor to develop a ground-based system to collect continuous Multiview data. One of the goals of this project was to use this data to estimate the volume of plants. Using 3D reconstruction models called point clouds, volume of the desired crops can be estimated.. We have developed a novel scalable system to organize the data by plant variety and extract volume estimation of the plants. This volume has larger implications when used to determine plant health, where the plant is in the life cycle or estimate fruit yield of the plant. Our high throughput phenotyping system could make data collection for plant varieties much more efficient for plant breeders and researchers.

By the year 2050 the world population will increase to 9.7 billion people. Food production must increase by at least 70% in order to feed this population. One way to increase food production is to create crop cultivars that can produce high quality and high yielding crops without needing to increase the amount of resources required. Plant breeders are able to create new crop cultivars using high-throughput genotyping techniques, however the current bottleneck in plant breeding is in-field phenotyping. This study focuses on designing a high-throughput in-field proximal phenotyping system capable of collecting non-contact, high-resolution, multi-sensor, multi-view, phenomic data of vegetable plants.

Vegetable crops are very susceptible to damage from weed competition, with early season weeds emerging within 3 weeks after crop emergence a control priority to prevent significant yield loss. One strategy for rapid weed crop differentiation is to place a machine detectable marker on the crop, i.e. Crop signaling. In this study, Rhodamine B (Rh-B) was used as a systemic crop signaling compound in tomato plants due to its unique fluorescent properties. The photostability of Rh-B was assessed under full sun treatment. The results showed that Rh-B was photostable in tomato plants for 27 days after transplanting.