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Recent trends in the application of deep learning techniques for crop disease classification is gaining international attention among experts of various domains. Many of the pioneering works have been carried out using the leaf images in laboratory condition with several shortcomings for implementation in field condition. Some of the other key issues are the presence of multiple disorders and the similarity of symptoms which can be addressed by using local symptomatic regions of the disease symptoms. In addition, although many studies discussed on the AI-based smartphone application for disease classification, very few studies have actually implemented it. This study has explored the classification of five diseases in Solanum melongena (also commonly known as eggplant, aubergine or brinjal) with the creation of the dataset consisting of local symptomatic region, utilised one of the popular deep learning model VGG16 for classification and optimised it for deployment in a smartphone. The VGG16 model was trained with fine-tuned hyperparameter and evaluated with a test dataset which resulted in the accuracy of 94.3%. This study also analysed the feature parameters from several layers using Multi-class Support Vector Machine (MSVM) to understand the learning process as it approaches top layers. Further, the feature parameters of dominant channels that significantly influenced the classification process were identified and analysed. Finally, VGG16 model was customised and implemented in a smartphone. It was tested in a trial condition which resulted in the classification accuracy of 91.3%. Discussions on the possible reasons for misclassification and scope for improvement have been provided.

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.

In chloroplasts, thiol-dependent redox regulation is linked to light since the disulfide reductase activity of thioredoxins (Trxs) relies on photo-reduced ferredoxin (Fdx). Furthermore, chloroplasts harbor an NADPH-dependent Trx reductase (NTR) with a joint Trx domain, termed NTRC. The activity of these two redox systems is integrated by the redox balance of 2-Cys peroxiredoxin (Prx), which is controlled by NTRC. However, NTRC was proposed to participate in redox regulation of additional targets, prompting inquiry into whether the function of NTRC depends on its capacity to maintain the redox balance of 2-Cys Prxs or by direct redox interaction with chloroplast enzymes. To answer this, we studied the functional relationship of NTRC and 2-Cys Prxs by a comparative analysis of the triple Arabidopsis (Arabidopsis thaliana) mutant, ntrc-2cpab, which lacks NTRC and 2-Cys Prxs, and the double mutant 2cpab, which lacks 2-Cys Prxs. These mutants exhibit almost indistinguishable phenotypes: in growth rate, photosynthesis performance, and redox regulation of chloroplast enzymes in response to light and darkness. These results suggest that the most relevant function of NTRC is in controlling the redox balance of 2-Cys Prxs. A comparative transcriptomics analysis confirmed the phenotypic similarity of the two mutants and suggested that the NTRC-2-Cys Prxs system participates in cytosolic protein quality control. We propose that NTRC and 2-Cys Prxs constitute a redox relay, exclusive to photosynthetic organisms that fine-tunes the redox state of chloroplast enzymes in response to light and affects transduction pathways towards the cytosol

Photosynthesis in leaves generates fixed-carbon resources and essential metabolites that support sink tissues, such as roots. Two of these metabolites, sucrose and auxin, promote growth in root systems, but the explicit connection between photosynthetic activity and control of root architecture has not been explored. Through a mutant screen to identify pathways regulating root system architecture, we identified a mutation in the Arabidopsis thaliana CYCLOPHILIN 38 (CYP38) gene, which causes accumulation of pre-emergent stage lateral roots. CYP38 was previously reported to stabilize photosystem II (PSII) in chloroplasts. CYP38 expression is enriched in shoots, and grafting experiments show that the gene acts non-cell-autonomously to promote lateral root emergence. Growth of wild-type plants under low-light conditions phenocopies the cyp38 lateral root emergence defect, as does the inhibition of PSII-dependent electron transport or Nicotinamide adenine dinucleotide phosphate (NADPH) production. Importantly, these perturbations to photosynthetic activity rapidly suppress lateral root emergence, which is separate from their effects on shoot size. Supplementary exogenous sucrose largely rescued primary root (PR) growth in cyp38, but not lateral root growth. Auxin (indole-3-acetic acid (IAA)) biosynthesis from tryptophan is dependent on reductant generated during photosynthesis. Consistently, we found that wild-type seedlings grown under low light and cyp38 mutants have highly diminished levels of IAA in root tissues. IAA treatment rescued the cyp38 lateral root defect, revealing that photosynthesis promotes lateral root emergence partly through IAA biosynthesis. These data directly confirm the importance of CYP38-dependent photosynthetic activity in supporting root growth, and define the specific contributions of two metabolites in refining root architecture under light-limited conditions

Article number 684328 Smart farming employs intelligent systems for every domain of agriculture to obtain sustainable economic growth with the available resources using advanced technologies. Deep Learning (DL) is a sophisticated artificial neural network architecture that provides state-of-the-art results in smart farming applications. One of the main tasks in this domain is yield estimation. Manual yield estimation undergoes many hurdles such as labor-intensive, time-consuming, imprecise results, etc. These issues motivate the development of an intelligent fruit yield estimation system that offers more benefits to the farmers in deciding harvesting, marketing, etc. Semantic segmentation combined with DL adds promising results in fruit detection and localization by performing pixel-based prediction. This paper reviews the different literature employing various techniques for fruit yield estimation using DL-based semantic segmentation architectures. It also discusses the challenging issues that occur during intelligent fruit yield estimation such as sampling, collection, annotation and data augmentation, fruit detection, and counting. Results show that the fruit yield estimation employing DL-based semantic segmentation techniques yields better performance than earlier techniques because of human cognition incorporated into the architecture. Future directions like customization of DL architecture for smart-phone applications to predict the yield, development of more comprehensive model encompassing challenging situations like occlusion, overlapping and illumination variation, etc., were also discussed. Ministerio de Economía y Competitividad ( España) CEI-15-AGR278, US-1263678

Nº Artículo: 1117 The aim of this study was to compare three different soil-sampling methods used in Precision Agriculture and their environmental impact on agricultural production. The sampling methods used were: zone management by elevation, grid sampling (GS) and sampling oriented by apparent soil electrical conductivity (OS). Three different fields were tested. When the recommendations were compared, a significant difference among the suggested doses was observed. This indicated the need to improve the soil-sampling techniques, since there were doubts about input deficits or overdoses, regardless of the technology studied. The GS method was the most environmentally viable alternative for phosphorus (P) compared to other methods and the OS presented as the better option for potassium (K) and nitrogen (N). However, the use of soil sensors appeared to be a viable technology that needs further improvement in order to improve productivity and, hence, economic and environmental benefits.

Nowadays, a precise management of agricultural inputs based on spatial variability has become possible due to the availability of new technologies, including Global Navigation Satellite System (GNSS), unmanned aerial vehicles in agriculture (UAVs), high-resolution remote sensing, sensors to measure soil and plant characteristics, and controllers to guide agricultural machinery and apply precise amount of inputs. The benefits of precision agriculture are as follows: • The return on the investment from crop production may be increased by improvements in yield and/or a reduction in inputs; • The risk of environmental pollution from over application of agrochemicals that are associated with traditional agriculture can be reduced; • Greater product quality assurance achieved from precise targeting and recording of field applications that improve traceability. In the last decade, the number of fully operational and commercially available GNSS has increased. These systems provide accurate, continuous, geographic position and speed information in three dimensions and under all weather conditions to users 24 h a day. In the last two decades, the ability of Differential Global Positioning System to provide real-time, submeter- or even decimeter-level accuracy has revolutionized the agricultural industry. Applications of GNSS for agricultural purposes have thrived in recent years, and the literature is rich with numerous interesting examples. In this chapter, we limit our attention to five specific applications with which the authors are very closely involved: (1) GNSS in crop protection, (2) in variable rate application (VRA), (3) monitoring soil, plants, and production, (4) agricultural UAVs, and (5) ground platforms and autonomous tractors. Not all the operations in precision agriculture need the same level of accuracy. Most of them, such as yield monitoring or VRAs, can be accomplished satisfactorily with DGNSS that has submeter accuracy. Only some operations, however, demand high (~10 mm range) geopositioning accuracy and precision, such as a plant-specific operation, precision guidance, and controlled traffic farming. This level of accuracy could be achieved with the use of real-time kinematic Global Positioning System.

Regulation of enzyme activity based on thiol-disulfide exchange is a regulatory mechanism in which the protein disulfide reductase activity of thioredoxins (TRXs) plays a central role. Plant chloroplasts are equipped with a complex set of up to 20 TRXs and TRX-like proteins, the activity of which is supported by reducing power provided by photosynthetically reduced ferredoxin (FDX) with the participation of a FDX-dependent TRX reductase (FTR). Therefore, the FDX–FTR–TRXs pathway allows the regulation of redox-sensitive chloroplast enzymes in response to light. In addition, chloroplasts contain an NADPH-dependent redox system, termed NTRC, which allows the use of NADPH in the redox network of these organelles. Genetic approaches using mutants of Arabidopsis (Arabidopsis thaliana) in combination with biochemical and physiological studies have shown that both redox systems, NTRC and FDX-FTR-TRXs, participate in fine-tuning chloroplast performance in response to changes in light intensity. Moreover, these studies revealed the participation of 2-Cys peroxiredoxin (2-Cys PRX), a thiol-dependent peroxidase, in the control of the reducing activity of chloroplast TRXs as well as in the rapid oxidation of stromal enzymes upon darkness. In this review, we provide an update on recent findings regarding the redox regulatory network of plant chloroplasts, focusing on the functional relationship of 2-Cys PRXs with NTRC and the FDX–FTR–TRXs redox systems for fine-tuning chloroplast performance in response to changes in light intensity and darkness. Finally, we consider redox regulation as an additional layer of control of the signaling function of the chloroplast.

The potential of hyperspectral measurements for early disease detection has been investigated by many experts over the last 5 years. One of the difficulties is obtaining enough data for training and building a hyperspectral training library. When the goal is to detect disease at a previsible stage, before the pathogen has manifested either its first symptoms or in the area surrounding the existing symptoms, it is impossible to objectively delineate the regions of interest containing the previsible pathogen growth from the areas without the pathogen growth. To overcome this, we propose an image labelling and segmentation algorithm that is able to (a) more objectively label the visible symptoms for the construction of a training library and (b) extend this labelling to the pre-visible symptoms. This algorithm is used to create hyperspectral training libraries for late blight disease (Phytophthora infestans) in potatoes and two types of leaf rust (Puccinia triticina and Puccinia striiformis) in wheat. The model training accuracies were compared between the automatic labelling algorithm and the classic visual delineation of regions of interest using a logistic regression machine learning approach. The modelling accuracies of the automatically labelled datasets were higher than those of the manually labelled ones for both potatoes and wheat, at 98.80% for P. infestans in potato, 97.69% for P. striiformis in soft wheat, and 96.66% for P. triticina in durum wheat.

Thioredoxins (Trxs) are small, ubiquitous enzymes that catalyze disulphide–dithiol interchange in target enzymes. The large set of chloroplast Trxs, including f, m, x and y subtypes, use reducing equivalents fueled by photoreduced ferredoxin (Fdx) for fine-tuning photosynthetic performance and metabolism through the control of the activity of redox-sensitive proteins. Although biochemical analyses suggested functional diversity of chloroplast Trxs, genetic studies have established that deficiency in a particular Trx subtype has subtle phenotypic effects, leading to the proposal that the Trx isoforms are functionally redundant. In addition, chloroplasts contain an NADPH-dependent Trx reductase with a joint Trx domain, termed NTRC. Interestingly, Arabidopsis mutants combining the deficiencies of x- or f-type Trxs and NTRC display very severe growth inhibition phenotypes, which are partially rescued by decreased levels of 2-Cys peroxiredoxins (Prxs). These findings indicate that the reducing capacity of Trxs f and x is modulated by the redox balance of 2-Cys Prxs, which is controlled by NTRC. In this study, we explored whether NTRC acts as a master regulator of the pool of chloroplast Trxs by analyzing its functional relationship with Trxs y. While Trx y interacts with 2-Cys Prxs in vitro and in planta, the analysis of Arabidopsis mutants devoid of NTRC and Trxs y suggests that Trxs y have only a minor effect, if any, on the redox state of 2-Cys Prxs.

Photosynthesis in leaves generates the fixed-carbon resources and essential metabolites that support sink tissues, such as roots [1]. One of these products, sucrose, is known to promote primary root growth, but it is not clear what other molecules may be involved and whether other stages of root system development are affected by photosynthate levels [2]. Through a mutant screen to identify pathways regulating root system architecture, we identified a mutation in the CYCLOPHILIN 38 (CYP38) gene, which causes an accumulation of pre-emergent stage lateral roots, with a minor effect on primary root growth. CYP38 was previously reported to maintain the stability of Photosystem II (PSII) in chloroplasts [3]. CYP38 expression is enriched in the shoot and grafting experiments show that the gene acts non-cell autonomously to promote lateral root emergence. Growth of wild-type plants under low light conditions phenocopied the cyp38 lateral root emergence phenotype as did the inhibition of PSII-dependent electron transport or NADPH production. Importantly, the cyp38 root phenotype is not rescued by exogenous sucrose, suggesting the involvement of another metabolite. Auxin (IAA) is an essential hormone promoting root growth and its biosynthesis from tryptophan is dependent on reductant generated during photosynthesis [4,5]. Both WT seedlings grown under low light and cyp38 mutants have highly diminished levels of IAA in root tissues. The cyp38 lateral root defect is rescued by IAA treatment, revealing that photosynthesis promotes lateral root emergence partly through IAA biosynthesis. Metabolomic profiling shows that the accumulation of several defense-related metabolites are also photosynthesis-dependent, suggesting that the regulation of a number of energy-intensive pathways are down-regulated when light becomes limiting.

Farmers require accurate yield estimates, since they are key to predicting the volume of stock needed at supermarkets and to organizing harvesting operations. In many cases, the yield is visually estimated by the crop producer, but this approach is not accurate or time efficient. This study presents a rapid sensing and yield estimation scheme using offthe-shelf aerial imagery and deep learning. A Region-Convolutional Neural Network was trained to detect and count the number of apple fruit on individual trees located on the orthomosaic built from images taken by the unmanned aerial vehicle (UAV). The results obtained with the proposed approach were compared with apple counts made in situ by an agrotechnician, and an R2 value of 0.86 was acquired (MAE: 10.35 and RMSE: 13.56). As only parts of the tree fruits were visible in the top-view images, linear regression was used to estimate the number of total apples on each tree. An R2 value of 0.80 (MAE: 128.56 and RMSE: 130.56) was obtained. With the number of fruits detected and tree coordinates two shapefile using Python script in Google Colab were generated. With the previous information two yield maps were displayed: one with information per tree and another with information per tree row. We are confident that these results will help to maximize the crop producers' outputs via optimized orchard management.

Photosynthesis in leaves generates the fixed-carbon resources and essential metabolites that support sink tissues, such as roots. One of these products, sucrose, is known to promote primary root growth, but it is not clear what other molecules may be involved and whether other stages of root system development are affected by photosynthate levels. Through a mutant screen to identify pathways regulating root system architecture, we identified a mutation in the CYCLOPHILIN 38 (CYP38) gene, which causes an accumulation of pre-emergent stage lateral roots, with a minor effect on primary root growth. CYP38 was previously reported to maintain the stability of Photosystem II (PSII) in chloroplasts. CYP38 expression is enriched in the shoot and grafting experiments show that the gene acts non-cell autonomously to promote lateral root emergence. Growth of wild-type plants under low light conditions phenocopied the cyp38 lateral root emergence phenotype as did the inhibition of PSII-dependent electron transport or NADPH production. Importantly, the cyp38 root phenotype is not rescued by exogenous sucrose, suggesting the involvement of another metabolite. Auxin (IAA) is an essential hormone promoting root growth and its biosynthesis from tryptophan is dependent on reductant generated during photosynthesis. Both WT seedlings grown under low light and cyp38 mutants have highly diminished levels of IAA in root tissues. The cyp38 lateral root defect is rescued by IAA treatment, revealing that photosynthesis promotes lateral root emergence partly through IAA biosynthesis. Metabolomic profiling shows that the accumulation of several defense-related metabolites are also photosynthesis-dependent, suggesting that the regulation of a number of energy-intensive pathways are down-regulated when light becomes limiting.

Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model.

Thioredoxins (Trxs) are small, ubiquitous enzymes that catalyze disulphide–dithiol interchange in target enzymes. The large set of chloroplast Trxs, including f, m, x and y subtypes, use reducing equivalents fueled by photoreduced ferredoxin (Fdx) for fine-tuning photosynthetic performance and metabolism through the control of the activity of redox-sensitive proteins. Although biochemical analyses suggested functional diversity of chloroplast Trxs, genetic studies have established that deficiency in a particular Trx subtype has subtle phenotypic e ects, leading to the proposal that the Trx isoforms are functionally redundant. In addition, chloroplasts contain an NADPH-dependent Trx reductase with a joint Trx domain, termed NTRC. Interestingly, Arabidopsis mutants combining the deficiencies of x- or f-type Trxs and NTRC display very severe growth inhibition phenotypes, which are partially rescued by decreased levels of 2-Cys peroxiredoxins (Prxs). These findings indicate that the reducing capacity of Trxs f and x is modulated by the redox balance of 2-Cys Prxs, which is controlled by NTRC. In this study, we explored whether NTRC acts as a master regulator of the pool of chloroplast Trxs by analyzing its functional relationship with Trxs y. While Trx y interacts with 2-Cys Prxs in vitro and in planta, the analysis of Arabidopsis mutants devoid of NTRC and Trxs y suggests that Trxs y have only a minor e ect, if any, on the redox state of 2-Cys Prxs.

Satellite imagery is a simple and scalable support tool for crop monitoring which allows advanced remote management of a large number of hectares. It offers to agricultural technicians’ free information a reasonable resolution. We will analyze advantages and disadvantages as well as the main application and sources of satellite images.

Currently, pest management practices require modern equipment and the use of complex information, such as regulations and guidelines. The complexity of regulations is the root cause of the emergence of automated solutions for compliance assessment by translating regulations into sets of machine-processable rules that can be run by specialized modules of farm management information systems (FMIS). However, the manual translation of rules is prohibitively costly, and therefore, this translation should be carried out with the support of artificial intelligence techniques. In this paper, we use the official Spanish phytosanitary products registry to empirically evaluate the performance of four popular machine learning algorithms in the task of correctly classifying pesticide regulations as prohibitions or obligations. Moreover, we also evaluate how to improve the performance of the algorithms in the preprocessing of the texts with natural language processing techniques. Finally, due to the specific characteristics of the texts found in pesticide regulations, resampling techniques are also evaluated. Experiments show that the combination of the machine learning algorithm Logic regression, the natural language technique part-of-speech tagging and the resampling technique Tomek links is the best performing approach, with an F1 score of 68.8%, a precision of 84.46% and a recall of 60%. The experimental results are promising, and they show that this approach can be applied to develop a computer-aided tool for transforming textual pesticide regulations into machine-processable rules. To the best of our knowledge, this is the first study that evaluates the use of artificial intelligence methods for the automatic translation of agricultural regulations into machine-processable representations.

The use of reliable information and data that are rapidly and easily acquired is essential for farm water management and appropriate irrigation strategies. Over the past decade, new advances have been made in irrigation technology, such as platforms that continuously transmit data between irrigation controllers and field sensors, mobile apps, and equipment for variable rate irrigation. In this study, images captured with a thermal imaging camera mounted on an unmanned aerial vehicle (UAV) were used to evaluate the water status of sugar beet plants in a plot with large spatial variability in terms of soil properties. The results were compared with those of soil moisture measurements. No direct relationship was observed between the water status of the soil and that of the crops. However, the fresh root mass and sugar content tended to decrease when higher levels of water stress were detected in the crop using thermal imaging, with coefficients of determination of 0.28 and 0.94 for fresh root mass and sugar content, respectively. Differences were observed between different soil types, and therefore different irrigation strategies are needed in highly heterogeneous plots.

Canopy temperature has been proposed as a relevant variable for crop water stress monitoring. Since crop temperature is highly influenced by the prevailing climatic conditions, it is usually normalized with indices such as the crop water stress index (CWSI). The index requires the use of two baselines that relate canopy temperature under maximum stress and non-water stress conditions with vapor pressure deficit (VPD). These reference baselines are specific to each crop and climatic region. In maize, they have been extensively studied for certain climatic regions but very little is known on their suitability to be used under Mediterranean-type conditions nor their temporal stability, both diurnally and between seasons. Thus, the objective of this work was to determine the reference baselines for maize grown under Mediterranean conditions, as well as its diurnal and long-term stability. An experiment was conducted for 3 years in a maize breeding field, under well-watered and water-stressed irrigation treatments. The determined reference baselines for computing CWSI in maize have shown to be stable in the long term but markedly influenced by the meteorological variations between 10–17 h UTC (Coordinated Universal Time). These results indicate that several reference baselines should be used for CWSI computing throughout the abovementioned time interval. The CWSI values calculated for well-watered and water-stressed maize breeding plots using the reference baselines derived in this study were successfully correlated with other physiological indicators of plant water stress.

12 páginas.-- 10 figuras.-- 3 tablas.-- 43 referencias, Characterization of the spatio-temporal variability of tree water status is a prerequisite to conducting precise irrigation management in fruit tree orchards. This study assessed the suitability of a crop water stress index (CWSI) derived from high-resolution aerial thermal imagery for estimating tree water status variability in super high density (SHD) olive orchards. The experiment was conducted at a commercial SHD olive orchard near Seville (southwestern Spain), with drip irrigated trees under three irrigation treatments (four plots per treatment in a randomized block design): a full irrigation treatment to replace the crop water needs (ETc) and two regulated deficit irrigation treatments to replace ca. 45% of ETc. Meteorological variables, soil moisture content, leaf water potential, stem water potential and leaf gas exchange measurements were performed along the irrigation season. Infrared temperature sensors (IRTs) installed approximately 1 m above the canopies were used to derive the required Non-Water-Stressed Baselines (NWSBs) for CWSI calculation. NWSBs were not common during the growing season, although the seasonal effect could be partly explained with solar angle variations. A thermal camera installed on a mini Remotely Piloted Aircraft System (RPAS) allowed for the recording of high-resolution thermal images on 5 representative dates during the irrigation season. The CWSI values derived from aerial thermal imagery were sensitive to the imposed variations in tree water status within the SHD olive orchard. Among the recorded variables, maximum stomatal conductance showed the tightest correlation with CWSI. We concluded that high-resolution thermal imagery captured from a mini RPAS is a suitable tool for defining tree water status variability within SHD olive orchards, This work was funded by the Spanish Ministry of Economy and Competitiveness (research project AGL2012-34544, predoctoral contract grant BES-2013-065380) and by the Junta de Andalucía (research project P12-AGR-1227).

New Super-High-Density (SHD) olive orchards designed for mechanical harvesting are increasing very rapidly in Spain. Most studies have focused in effectively removing the olive fruit, however the machine needs to put significant amount of energy on the canopy that could result in structural damage or extra stress on the trees. During harvest, a series of 3-axis accelerometers were installed on the tree structure in order to register vibration patterns. A LiDAR (Light Detection and Ranging) and a camera sensing device were also mounted on a tractor. Before and after harvest measurements showed significant differences in the LiDAR and image data. A fast estimate of the damage produced by an over-the-row harvester with contactless sensing could be useful information for adjusting the machine parameters in each olive grove automatically in the future.

Light is probably the most important environmental stimulus for plant development. As sessile organisms, plants have developed regulatory mechanisms that allow the rapid adaptation of their metabolism to changes in light availability. Redox regulation based on disulfide-dithiol exchange constitutes a rapid and reversible post-translational modification, which affects protein conformation and activity. This regulatory mechanism was initially discovered in chloroplasts when it was identified that enzymes of the Calvin-Benson cycle (CBC) are reduced and active during the day and become rapidly inactivated by oxidation in the dark. At present, the large number of redox-sensitive proteins identified in chloroplasts extend redox regulation far beyond the CBC. The classic pathway of redox regulation in chloroplasts establishes that ferredoxin (Fdx) reduced by the photosynthetic electron transport chain fuels reducing equivalents to the large set of thioredoxins (Trxs) of this organelle via the activity of a Fdx-dependent Trx reductase (FTR), hence linking redox regulation to light. In addition, chloroplasts harbor an NADPH-dependent Trx reductase with a joint Trx domain, termed NTRC. The presence in chloroplasts of this NADPH-dependent redox system raises the question of the functional relationship between NTRC and the Fdx-FTR-Trx pathways. Here, we update the current knowledge of these two redox systems focusing on recent evidence showing their functional interrelationship through the action of the thioldependent peroxidase, 2-Cys peroxiredoxin (2-Cys Prx). The relevant role of 2-Cys Prxs in chloroplast redox homeostasis suggests that hydrogen peroxide may exert a key function to control the redox state of stromal enzymes. Indeed, recent reports have shown the participation of 2-Cys Prxs in enzyme oxidation in the dark, thus providing an explanation for the long-lasting question of photosynthesis deactivation during the light-dark transition.

González-de-Santos, Pablo et al., Feeding the growing global population requires an annual increase in food production. This requirement suggests an increase in the use of pesticides, which represents an unsustainable chemical load for the environment. To reduce pesticide input and preserve the environment while maintaining the necessary level of food production, the efficiency of relevant processes must be drastically improved. Within this context, this research strived to design, develop, test and assess a new generation of automatic and robotic systems for effective weed and pest control aimed at diminishing the use of agricultural chemical inputs, increasing crop quality and improving the health and safety of production operators. To achieve this overall objective, a fleet of heterogeneous ground and aerial robots was developed and equipped with innovative sensors, enhanced end-effectors and improved decision control algorithms to cover a large variety of agricultural situations. This article describes the scientific and technical objectives, challenges and outcomes achieved in three common crops., The research leading to these results received funding from the European Union’s Seventh Framework Programme [FP7/2007-2013] under Grant Agreement nº 245986. Support for publishing this article has been provided by CSIC.