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The non-judicious use of water at the farm level in traditional irrigation application methods is a present-day concern across the world that can be resolved by enhancing application efficiency through the adoption of advanced irrigation techniques. Sprinkler irrigation is a method that has high application efficiency, which can be further increased when coupled with automation toward precision irrigation. The objectives of this review are to summarize the main aspects of sprinkler and precision irrigation and their development, scope, and future prospects specifically in Asian countries. In this paper, a modified methodology, inspired by PRISMA guidelines, was used to explore the available literature to summarize the existing knowledge in the field. Regarding the technological aspects of the analyzed works, it became evident that sprinkler systems are an efficient method to not only irrigate crops (with 39% water saving) but also for the application of fertilizers with higher efficiency (>35%) and water productivity (>14.1%) compared with gravity irrigation systems. Moreover, this paper highlights the prominent features of precision irrigation for maximizing agricultural productivity. The use of sprinkler irrigation with precision applications using automation with a sensor-based mechanism for field data collection, data transformation, data analysis, and operation of IoT-based automatic solenoid valves can save 20–30% more irrigation water and increase crop yield by 20–27%. An analytical understanding and knowledge of the field were used to draw conclusions that are thought-provoking for scientists, researchers, and other stakeholders. [ABSTRACT FROM AUTHOR]

The article discusses the importance of crop tolerance to both abiotic and biotic stresses in agricultural research. Abiotic stresses include environmental factors like drought and extreme temperatures, while biotic stresses involve pests, diseases, and pathogens. Understanding and enhancing crop tolerance to these stresses is crucial for global food security and sustainable crop production. The article highlights various research papers from different countries that focus on topics such as abiotic stress (salinity, drought) and biotic stress (pests, diseases). The research papers explore different strategies, including breeding and genetic engineering, to enhance crop tolerance. [Extracted from the article]

Due to the changes in the agricultural sector, there is a demand among policymakers, administration, advisors and farmers for comparisons of the economic efficiency of organic versus conventional farms and their environmental impact. The authors of the paper hypothesised that in some conditions, organic farms can reach similar productive and economic results as conventional farms and, at the same time, achieve better environmental effects. The aim of the research was to compare the production, environmental and economic effects of selected organic and conventional farms from eastern Poland (mixed, crop production, animal production). The basis for the comparative analysis was the data from 12 farms obtained using the questionnaire method (direct interview) from the years 2020 to 2021. The yields of cereals in the tested organic farms were about one-third lower than the average obtained on conventional farms. Total organic crop production in cereal units per ha was 43% of conventional production. Balances of NPK indicated surpluses or deficiency, which suggested that in both systems nutrient management should be improved and optimised. The compared groups of organic farms generally had higher economic efficiency—both with and without subsidies—than conventional farms, despite the fact that the latter obtained significantly higher incomes. Gross farm income on conventional farms was higher than on organic farms by 28%, but conventional farms had higher direct costs by 332% than organic ones. As a result, the economic efficiency of agricultural production of organic farms was higher by 59% than conventional farms. The lower level of inputs incurred on organic farms was the main factor determining their high economic efficiency. Organic farms pursuing an intensive model of agricultural production (milk production), but also with specialisation in crop production, proved to be the most economically efficient. Despite diversified production, reducing the income risk, the mixed production, both organic and conventional, in the studied set of farms turned out to be the least profitable, indicating the need to support it. It is suggested that moderate specialisation increases the efficiency of management in organic farming. [ABSTRACT FROM AUTHOR]

Precision irrigation and fertilization in agriculture are vital for sustainable crop production, relying on accurate determination of the crop's nutritional status. However, there are challenges in optimizing traditional neural networks to achieve this accurately. This paper aims to propose a rapid identification method for crop water and nitrogen content using optimized neural networks. This method addresses the difficulty in optimizing the traditional backpropagation neural network (BPNN) structure. It uses 179 multi−spectral images of crops (such as maize) as samples for the neural network model. Particle swarm optimization (PSO) is applied to optimize the hidden layer nodes. Additionally, this paper proposes a double−hidden−layer network structure to improve the model's prediction accuracy. The proposed double−hidden−layer PSO−BPNN model showed a 9.87% improvement in prediction accuracy compared with the traditional BPNN model. The correlation coefficient R2 for predicted crop nitrogen and water content was 0.9045 and 0.8734, respectively. The experimental results demonstrate high training efficiency and accuracy. This method lays a strong foundation for developing precision irrigation and fertilization plans for modern agriculture and holds promising prospects. [ABSTRACT FROM AUTHOR]

Avocado is one of the most commercialized and profitable fruits in the international market. Its cultivation and production are centered in countries characterized by tropical and subtropical climatic conditions, many of them with emerging economies. Moreover, the use of technology is key to agricultural production improvement strategies. Using avocado crop data to forecast the potential impacts of biotic and abiotic factors, combined with smart farming technologies, growers can apply measures during a single production phase to reduce the risks caused by pests and weather variations. Therefore, this paper aims to distinguish the most relevant variables related to agroclimatic and phytosanitary events in avocado crops, their incidence on production and risk management, as well as the emerging technologies used for the identification and analysis of pests and diseases in avocados. A scientific literature search was performed, and the first search found 608 studies, and once the screening process was applied, 37 papers were included in this review. In the results, three research questions were answered that described the pests and diseases with high impact on avocado production, along with the data sources and the principal enabling technologies used in the identification of agroclimatic and phytosanitary events in avocados. Some challenges and trends in the parameterization of the technology in field conditions for data collection are also highlighted. [ABSTRACT FROM AUTHOR]

Due to the massive surge in the world population, the agriculture cycle expansion is necessary to accommodate the anticipated demand. However, this expansion is challenged by weed invasion, a detrimental factor for agricultural production and quality. Therefore, an accurate, automatic, low-cost, environment-friendly, and real-time weed detection technique is required to control weeds on fields. Furthermore, automating the weed classification process according to growth stages is crucial for using appropriate weed controlling techniques, which represents a gap of research. The main focus of the undertaken research described in this paper is on providing a feasibility study for the agriculture community using recent deep-learning models to address this gap of research on classification of weed growth stages. For this paper we used a drone to collect a dataset of four weed (Consolida regalis) growth stages. In addition, we developed and trained one-stage and two-stage models YOLOv5, RetinaNet (with Resnet-101-FPN, Resnet-50-FPN backbones) and Faster R-CNN (with Resnet-101-DC5, Resnet-101-FPN, Resnet-50-FPN backbones), respectively. The results show that the generated Yolov5-small model succeeds in detecting weeds and classifying weed growth stages in real time with the highest recall of 0.794. RetinaNet with ResNet-101-FPN backbone shows accurate results in the testing phase (average precision of 87.457). Although Yolov5-large showed the highest precision in classifying almost all weed growth stages, Yolov5-large could not detect all objects in tested images. Overall, RetinaNet with ResNet-101-FPN backbones shows accurate and high precision, whereas Yolov5-small shows the shortest inference time in real time for detecting a weed and classifying its growth stages. [ABSTRACT FROM AUTHOR]

The potential benefits of applying information and communication technology (ICT) in precision agriculture to enhance sustainable agricultural growth were discussed in this review article. The current technologies, such as the Internet of Things (IoT) and artificial intelligence (AI), as well as their applications, must be integrated into the agricultural sector to ensure long-term agricultural productivity. These technologies have the potential to improve global food security by reducing crop output gaps, decreasing food waste, and minimizing resource use inefficiencies. The importance of collecting and analyzing big data from multiple sources, particularly in situ and on-the-go sensors, is also highlighted as an important component of achieving predictive decision making capabilities in precision agriculture and forecasting yields using advanced yield prediction models developed through machine learning. Finally, we cover the replacement of wired-based, complicated systems in infield monitoring with wireless sensor networks (WSN), particularly in the agricultural sector, and emphasize the necessity of knowing the radio frequency (RF) contributing aspects that influence signal intensity, interference, system model, bandwidth, and transmission range when creating a successful Agricultural Internet of Thing Ag-IoT system. The relevance of communication protocols and interfaces for presenting agricultural data acquired from sensors in various formats is also emphasized in the paper, as is the function of 4G, 3G, and 5G technologies in IoT-based smart farming. Overall, these research sheds light on the significance of wireless sensor networks and big data in the future of precision crop production [ABSTRACT FROM AUTHOR]

As essential environmental parameters in the greenhouse, appropriate light and CO2 will improve agricultural productivity and quality. Although many related studies have been carried out on the intelligent regulation of these environmental factors, the regulation of light and CO2 is usually controlled separately, and energy consumption is rarely considered. This paper proposed a coordinated control strategy for greenhouse light and CO2 based on the multi-objective optimization model. Firstly, the experiments on the net photosynthetic rate of blueberry under different temperatures, photon flux density, and CO2 concentration nesting were carried out to establish a blueberry net photosynthetic rate prediction model based on Support Vector Regression (SVR). Secondly, a model for calculating the energy cost of both light and CO2 was constructed. Thirdly, taking the maximum net photosynthetic rate and the minimum energy cost as the objective functions, the Non-dominated Sorting Genetic Algorithm (NSGA-II) was leveraged to obtain the Pareto optimal solutions of the target regulation values of light and CO2 concentration in different temperature ranges. Then, the optimal values were selected based on two different strategies. Finally, the multi-objective optimal control strategy proposed in this paper was compared with both the classical threshold control strategy and the Gaussian curvature maximization control strategy. The results indicated that the strategy which prioritized energy saving could reduce the energy cost by about 22.33% and 19.08%, respectively, under the premise that the net photosynthetic rate was consistent. Meanwhile, the strategy that prioritized production efficiency could increase the net photosynthetic rate by about 8.40% and 4.42%, respectively, with the same energy cost. In conclusion, the proposed multi-objective optimization control can improve the greenhouse climate control performance and reduce cost compared with other mentioned strategies. [ABSTRACT FROM AUTHOR]

A tomato automatic detection method based on an improved YOLOv8s model is proposed to address the low automation level in tomato harvesting in agriculture. The proposed method provides technical support for the automatic harvesting and classification of tomatoes in agricultural production activities. The proposed method has three key components. Firstly, the depthwise separable convolution (DSConv) technique replaces the ordinary convolution, which reduces the computational complexity by generating a large number of feature maps with a small amount of calculation. Secondly, the dual-path attention gate module (DPAG) is designed to improve the model's detection precision in complex environments by enhancing the network's ability to distinguish between tomatoes and the background. Thirdly, the feature enhancement module (FEM) is added to highlight the target details, prevent the loss of effective features, and improve detection precision. We built, trained, and tested the tomato dataset, which included 3098 images and 3 classes. The proposed algorithm's performance was evaluated by comparison with the SSD, faster R-CNN, YOLOv4, YOLOv5, and YOLOv7 algorithms. Precision, recall rate, and mAP (mean average precision) were used for evaluation. The test results show that the improved YOLOv8s network has a lower loss and 93.4% mAP on this dataset. This improvement is a 1.5% increase compared to before the improvement. The precision increased by 2%, and the recall rate increased by 0.8%. Moreover, the proposed algorithm significantly reduced the model size from 22 M to 16 M, while achieving a detection speed of 138.8 FPS, which satisfies the real-time detection requirement. The proposed method strikes a balance between model size and detection precision, enabling it to meet agriculture's tomato detection requirements. The research model in this paper will provide technical support for a tomato picking robot to ensure the fast and accurate operation of the picking robot. [ABSTRACT FROM AUTHOR]

In order to clarify the temporal and spatial changes in the internal photothermal environment in an open-field agrivoltaic system (OAVS), this paper took the OAVS in eastern China as the research object and divided the internal area into the southern area, middle area and northern area, according to the spatial structure. Further, a photothermal environment test was conducted in the above three areas in the summer and winter. The results showed that the summer average daylight rate (Rm-avg) in the middle area was 66.6%, while the Rm-avg in the other two areas was about 20%, with no significant difference. In the winter, the light environment in the southern area was slightly better, and the Rm-avg in the above three areas was 26.4%, 24.7% and 19.7%, respectively. On the whole, the relationship between the thermal environmental factors and the solar radiation intensity was consistent. Further, a 3D model of an OAVS was established using Autodesk Ecotect Analysis 2011, and the internal light environment was simulated. Compared with the measured values, the relative error was less than 10%, which verified the reliability of the OAVS model. Then, the model was used to reveal the temporal and spatial changes in the light environment of the OAVS. The simulation results showed that the daylighting rate in the summer from the ground to the height of the fig canopy inside the system was 20.7% to 61.5%. In the winter, the daylighting rate from the ground to the height of the fig canopy inside the system was 17.7% to 36.4%. The effectiveness of the OAVS in reducing the level of solar radiation intensity depended on the time of day and the angle of the sun. At the spatial scale, due to the strong consumption of light by photovoltaic panels, there was a strong horizontal and vertical light environment gradient inside the system. In conclusion, the photothermal environment research of an OAVS based on Autodesk Ecotect Analysis 2011 can not only provide a basis for agricultural production and structural design such as span, height and the laying density of PV panels, but also expand its application to regions with different latitudes and longitudes and specific climates. [ABSTRACT FROM AUTHOR]

Path-tracking control algorithms in agriculture typically focus on how to improve the trajectory-tracking performance of autonomous agricultural machinery, and the agricultural productivity is optimized in a two-layer way. The upper operational layer optimizes an optimal tracking trajectory with the best agricultural productivity, and the lower control layer—such as Nonlinear Model Predictive Control (NMPC)—receives this optimized tracking trajectory first, and then steers the vehicle to track this trajectory with high accuracy. However, this two-layer structure cannot improve the agricultural productivity at the control layer online, which makes the agricultural operation sub-optimal. In this paper, we focus on agricultural machinery operational efficiency, to represent agricultural productivity; in order to realize optimizing control to further improve agricultural machinery operational efficiency, a new path-tracking control algorithm, named Efficiency-oriented Model Predictive Control (EfiMPC), is proposed. EfiMPC is intrinsically a nested structure, which can consider the global performance of the whole system defined in the operational layer—like the agricultural machinery operational efficiency considered in this paper—in the control layer online; thus, the agricultural machinery operational efficiency can be improved during the farming operation. An unreachable tracking point, denoted as the pseudo-point, has been proposed, to indicate the agricultural machinery operational efficiency objective in a receding horizon fashion; EfiMPC can utilize this pseudo-point to realize the optimizing control online. A simulation case study was used to test the superiority of the proposed EfiMPC algorithm, and the results show that, compared with the traditional NMPC algorithm, the agricultural machinery operational efficiency realized by EfiMPC was improved by 8.56%; thus, the effectiveness of the EfiMPC has been demonstrated. [ABSTRACT FROM AUTHOR]

The growth of the Indonesian population has led to an increase in the demand for rice, which the country has yet to satisfy. Indonesia needs a comprehensive strategy that integrates meaningful efforts to increase its agricultural production. This study aims to review the examined trends in rice yield in Indonesia for 70 years after Indonesia's independence (1945–2016) followed by the identification of the application technology and factors that contribute to increasing rice yields to forecast sustainable food security scenarios up to 2030. This article reviews the results of research on rice production technology in Indonesia from 1945 to 2016, and the outlook for 2030. This paper examines the main points of the Indonesian transformation of rice technology: improvement of rice varieties, integrated crop management, innovations in agricultural machinery, and the Integrated Cropping Calendar Information System (ICCIS). We found that transformation has helped Indonesia increased its rice yields from 3 t ha−1 prior to 1961 to 4.6 t ha−1 in 1985, stagnated in 1990, and increased again in 2017 to 5.46 t ha−1. The increase in yield was sustained by an increase in the harvested area owing to cropping index (CI) innovation. Food security and sustainable development remain the primary goals of Indonesia's agricultural sector. The application of appropriate technologies and institutional innovations can assist Indonesia in achieving its food security. Therefore, the transformation of technological innovations will continue to be an essential driver of future agricultural growth, including greater use of crop varieties, machinery, and land/institutional reforms. [ABSTRACT FROM AUTHOR]

Climate change is a very serious threat to the agricultural sector and potentially brings new problems to the sustainability of agricultural production systems. This paper aims to know how to improve crop yield by changing the nitrogen application ratio and sowing time under future climate change conditions based on the CERES-Rice model. The CERES-Rice model was calibrated and validated with a three-year field experiment (2018–2020), which was coupled with four N rates (50, 100, 150, and 200 kg/ha) and three different N ratios (B:T:S = 3:1:0; B:T:S = 5:3:2; B:T:S = 6:3:1). The results showed that the CERES-Rice model had better simulation effect on the phenophase (n-RMSE < 15%, d > 0.9 and R2 = 0.978) and yield (n-RMSE < 10%, d > 0.9 and R2 = 0.910) of double-harvest rice. The calibrated model was used to evaluate the growth period and yield of double-harvest rice under the RCP4.5 climate scenario and the results revealed that future yields of double-harvest rice in Nanchang are lower than those in experimental years, especially for early rice. Adjusting the nitrogen application ratio and sowing time can improve the yield of double-harvest rice to a certain extent, and the nitrogen application ratio of 5:3:2 has the best effect. In 2021–2035, the best yield of double-harvest rice can be obtained when the sowing date of early rice is about 15 days earlier and the sowing date of late rice is about 10 days earlier than the experiment year. From 2035 to 2050, the sowing date of early rice and late rice will be advanced by about 10 days, and the total yield of double-harvest rice will be higher. In 2050–2070, the total yield of double-harvest rice may reach the best when the sowing date is delayed by 10–15 days. Therefore, reasonably changing the sowing date of double-harvest rice and the nitrogen application regime of early rice can be used as a possible adaptive strategy to cope with the yield reduction in double-harvest rice in future climate scenarios. [ABSTRACT FROM AUTHOR]

To date, effectively controlling resistant weeds has been a great challenge in modern agricultural production. Developing new modes of action of herbicides would be an efficient, convenient, and timely means of controlling resistant weeds. In particular, new modes of herbicide action do not appear to have evolutionary resistance or cross-resistance with existing herbicides. However, a few successful herbicides with new modes of action (MoAs) have been marketed in the past 20 years. In this paper, we analyzed limiting factors for the slow development of novel herbicide MoAs. We then summarized the positive herbicide targets for the herbicides that have been discovered in recent years, such as Solanyl Diphosphate Synthase (SPS), Fatty Acid Thioesterase (FAT), Plastid Peptide Deformylase (PDEF), and Dihydroxy-Acid Dehydratase (DHAD). Some commercial herbicide varieties have been obtained based on novel herbicide targets, such as Homogentisate Solanesyltransferase (HST) and Dihydroorotate Dehydrogenase (DHODH). This provides a new reference and idea for herbicide molecular design in the future. In addition, some practical and efficient methods were mentioned for the rational design, discovery, and development of targeted herbicides development. In order to overcome the adverse conditions of compound druggability, prodrug strategies are also used in herbicide development, which can optimize the administration, permeability, absorption, and distribution of the original drug molecule or a candidate compound and may provide more possibilities for the development of new herbicides. The development of new herbicides is fascinating, the challenges and rewards are great, and the path to success is becoming more apparent. [ABSTRACT FROM AUTHOR]

Estimating foliar damage is essential in agricultural processes to provide proper crop management, such as monitoring the defoliation level to take preventive actions. Furthermore, it is helpful to avoid the reduction of plant energy production, nutrition decrement, and consequently, the reduction of the final production of the crop and economic losses. In this sense, numerous proposals support the defoliation estimate task, ranging from traditional methodologies to computational solutions. However, subjectivity characteristics, reproducibility limitations, and imprecise results persist. Then, these circumstances justify the search for new solutions, especially in defoliation assessments. The main goal of this paper consists of developing an automatic method to estimate the percentage of damaged leaf areas consumed by insects. As a novelty, our method provides high precision in calculating defoliation severity caused by insect predation on the leaves of various plant species and works effectively to estimate leaf loss in leaves with border damage. We describe our method and evaluate its performance concerning 12 different plant species. Our experimental results demonstrate high accuracy in the determination of leaf area loss with a correlation coefficient superior to 0.84 for apple, blueberry, cherry, corn, grape, bell pepper, potato, raspberry, soybean, and strawberry leaves, and mean absolute error (MAE) less than 4% in defoliation levels up to 54% in soybean, strawberry, potato, and corn leaves. In addition, the method maintains a mean error of less than 50%, even for severe defoliation levels up to 99%. [ABSTRACT FROM AUTHOR]

Wheat is one of the most important crops in the world and was considered the major grain crop grown in Egypt. Nowadays, Egypt is the largest wheat importer in the world and consumes an extensive amount of it. To decrease the gap between production and consumption and increase the yield, we need to control wheat diseases, especially stripe rust, due to its major damage to wheat. Further, we need to advise farmers as early as we can to control and treat them. The paper proposed an interactive intelligent system to monitor, predict and give the correct advice at the right time to farmers. This system is called MISSR (Mentoring Interactive System for Stripe Rust). The system is considered an important means to effectively prevent risks in agricultural production. It also plays an important role in guiding farmers and decision-makers to plan and implement suitable practices to increase yield and mitigate stripe-rust disease. On the other hand, it can acquire relevant and timely information in the areas where this information or data is unavailable. To build this model for the wheat crop in Egypt, we used wheat experts' knowledge and climate data API. MISSR is available as a mobile application to provide access for more farmers and increase its availability. [ABSTRACT FROM AUTHOR]

Cereal grains and tubers are among the highly consumed staple foods globally; however, due to unfavorable weather conditions and the competition for natural resources, the major staple cereal crops, such as rice, are under production threat. On the other hand, the overuse of chemical fertilizers and pesticides to increase crop yield is deteriorating the growing environment for plants and animals, including humans. As such, sustainable management practices are the key method that can be employed to increase crop production without harming the environment. Plant growth-promoting bacteria (PGPB), such as the purple non-sulfur bacteria (PNSB), have recently gained much attention in crop production due to their ability to accumulate higher-value compounds that are highly beneficial to crops. Some of the major benefits PNSB holds are that it can fix atmospheric nitrogen, solubilize phosphate, remediate heavy metals, suppress methane emissions from waterlogged paddy fields, and assist in carbon sequestration. These benefits allow PNSB to be an important bacterium for improving plant growth and yield much more sustainably while benefiting the environment. This review article discusses the beneficial effects of PNSB on rice crop plants through careful screening of previous work in this area. The review also identifies the research gaps and suggests future research pathways to make PNSB an important bacteria for sustainable rice crop production. The review paper aims for the United Nation's sustainable development goal number two, "Zero Hunger," target 2.4, indicator 2.4.1, "Proportion of agricultural area under productive and sustainable agriculture". [ABSTRACT FROM AUTHOR]

The increasing global demand for food has forced farmers to produce higher crop yields in order to keep up with population growth, while maintaining sustainable production for the environment. As knowledge about natural cropland suitability is mandatory to achieve this, the aim of this paper is to provide a review of methods for suitability prediction according to abiotic environmental criteria. The conventional method for calculating cropland suitability in previous studies was a geographic information system (GIS)-based multicriteria analysis, dominantly in combination with the analytic hierarchy process (AHP). Although this is a flexible and widely accepted method, it has significant fundamental drawbacks, such as a lack of accuracy assessment, high subjectivity, computational inefficiency, and an unsystematic approach to selecting environmental criteria. To improve these drawbacks, methods for determining cropland suitability based on machine learning have been developed in recent studies. These novel methods contribute to an important paradigm shift when determining cropland suitability, being objective, automated, computationally efficient, and viable for widespread global use due to the availability of open data sources on a global scale. Nevertheless, both approaches produce invaluable complimentary benefits to cropland management planning, with novel methods being more appropriate for major crops and conventional methods more appropriate for less frequent crops. [ABSTRACT FROM AUTHOR]

Pathogen infection has greatly reduced crop production. As the symptoms of diseases usually appear when the plants are infected severely, rapid identification approaches are required to monitor plant diseases at early the infection stage and optimize control strategies. Hyperspectral imaging, as a fast and nondestructive sensing technology, has achieved remarkable results in plant disease identification. Various models have been developed for disease identification in different plants such as arable crops, vegetables, fruit trees, etc. In these models, important algorithms, such as the vegetation index and machine learning classification and methods have played significant roles in the detection and early warning of disease. In this paper, the principle of hyperspectral imaging technology and common spectral characteristics of plant disease symptoms are discussed. We reviewed the impact mechanism of pathogen infection on the photo response and spectrum features of the plants, the data processing tools and algorithms of the hyperspectral information of pathogen-infected plants, and the application prospect of hyperspectral imaging technology for the identification of plant diseases. [ABSTRACT FROM AUTHOR]

This paper reviews changes to lifecycle temporality in Southwest Asian plant and animal domestication, exploring their relationship to long-term processes associated with ancient and contemporary globalization. We survey changes under domestication to the timing of seed dispersal, germination, vegetative growth, flowering and maturation in wheat and barley and to birth, reproduction, lactation, wool production and death in sheep, goats and cattle. Changes in biological temporality among domesticates are ultimately related to globally increasing production intensity, geographic diffusion, and agricultural diversity associated with cultivar/breed evolution. Recently, however, increasing crop production intensity and geographic diffusion are accompanied by declining agricultural diversity worldwide. Similar processes are apparent in contemporary economic and cultural globalization, suggesting that long-term agricultural developments might be viewed as a subset of globalization. Moreover, the origin of certain features of contemporary globalization may be traced back to the beginnings of plant and animal domestication. Thus, while biologists since Darwin considered domestication as a model for the study of evolution, we suggest that domestication may also offer a model for the study of globalization. [ABSTRACT FROM AUTHOR]

Drought has a significant impact on agricultural production, but the evolution of drought in the North China Plain (NCP) and its impact on winter wheat yield remain unclear. In this paper, we used the Standardized Precipitation Evapotranspiration Index (SPEI) and combined with the Standardized Yield Residual Series (SYRS) and the Vegetation Condition Index (VCI) to study the spatiotemporal distribution of drought at different time scales and its impact on winter wheat yield. We found that: (1) The southern NCP was dominated by mild drought at the 1-month scale. In the northern NCP, mild drought was observed at the short-time scale and moderate drought at the medium- and long-time scales; (2) the frequency of mild drought was high and the frequency of moderate drought was a step lower. Moreover, drought risks were increasing in the north-central, eastern, and northeastern NCP, while the southern, west-central, southeastern, and northwestern NCP were becoming wetter (at the 6- and 9-month scales); (3) the maximum correlation coefficients were concentrated in the seedling to the greening stages. (4) Drought affected winter wheat with a lag of approximately 3 months, and the VCI was the most sensitive to the short-term SPEI. [ABSTRACT FROM AUTHOR]

Hoppers for unloading bulk materials are an indispensable feature of many technological machines and not only those employed in agricultural production. One of the problems in the operation of hoppers is the appearance of dynamic arches which make the outflow of grain uneven. Experimental studies have previously shown that the formation of dynamic arches creates an uneven outflow of grain along the vertical zones of outlet hoppers. This can lead to the processing mode violation of bulk material and loss of machine productivity. This paper theoretically shows that the use of arch-breaking devices with an element oscillating in the grain layer requires systems for automatic adjustment of oscillation frequency. This complicates the design and makes it more expensive. It is proposed to amend the type of material outflow as it moves along the hopper's height to solve this problem. The possibility of employing this approach has been tested on the use of a bulk material flow separator in the outlet hopper. The bulk material flow separator is made in a plate form and is installed rigidly between two opposite walls of the outlet hopper. Thus, the volume of the hopper is divided into two vertical equal parts. With the help of experimental studies, it was determined that the lower side of the bulk material flow separator should be at a distance of 0.3 of the total hopper height from the discharge opening, while the upper side of the separator should be at a distance of 0.25 of the hopper's height from the top edge of the hopper. The experimental verification of flow separator use confirmed its effectiveness. [ABSTRACT FROM AUTHOR]

Recently, large-scale agriculture has led to increasing crop production. To increase crop productivity in large-scale cropping systems, attempts have been made to make nano-fertilizers and deliver them to the crops by extension of nanotechnology. Hence, nano-fertilizers might be defined as nanoparticles that may directly assist in supplying essential nutrients for crop productivity. Seed germination is the first and the most susceptible stage in the plant's growing phases, so could be considered as an index to evaluate the effect of newly developed materials such as nanoparticles (NPs), providing useful information for researchers. In our experiments, germination tests have been carried out in Petri dishes containing wet filter paper and nano-primed seeds. We had biosynthesized seven nanoparticles in our previous studies including calcinated and non-calcinated zinc oxide, zinc, magnesium oxide, silver, copper, and iron nanoparticles. The effect of these biogenic nanoparticles and their counterpart metallic salts including zinc acetate, magnesium sulfate, silver nitrate, copper sulfate, and iron (III) chloride was studied on two popularly grown plants, wheat and flax, in laboratory conditions to obtain preliminary information for future field experiments. Germination percentage, shoot length, root length, seedlings length, root–shoot ratio, seedling vigor index (SVI), shoot length stress tolerance index (SLSI), and root length stress tolerance index (RLSI) were calculated on the second and seventh days of the experiment. According to the results, the response of the plants to metal containing nanoparticles and metal salts mainly depend on the type of the metal, plant species, concentration of the NP suspension or salt solution, condition of the exposure, and the stage of growth. [ABSTRACT FROM AUTHOR]

Rapid and accurate grape leaf disease diagnosis is of great significance to its yield and quality of grape. In this paper, aiming at the identification of grape leaf diseases, a fast and accurate detection method based on fused deep features, extracted from a convolutional neural network (CNN), plus a support vector machine (SVM) is proposed. In the research, based on an open dataset, three types of state-of-the-art CNN networks, three kinds of deep feature fusion methods, seven species of deep feature layers, and a multi-class SVM classifier were studied. Firstly, images were resized to meet the input requirements of the CNN network; then, the deep features of the input images were extracted via the specific deep feature layer of the CNN network. Two kinds of deep features from different networks were then fused using different fusion methods to increase the effective classification feature information. Finally, a multi-class SVM classifier was trained with the fused deep features. The experimental results on the open dataset show that the fused deep features with any kind of fusion method can obtain a better classification performance than using a single type of deep feature. The direct concatenation of the Fc1000 deep feature extracted from ResNet50 and ResNet101 can achieve the best classification result compared with the other two fusion methods, and its F1 score is 99.81%. Furthermore, the SVM classifier trained using the proposed method can achieve a classification performance comparable to that of using the CNN model directly, but the training time is less than 1 s, which has an advantage over spending tens of minutes training a CNN model. The experimental results indicate that the method proposed in this paper can achieve fast and accurate identification of grape leaf diseases and meet the needs of actual agricultural production. [ABSTRACT FROM AUTHOR]

Considering an expanding research and development pipeline of and growing practical experiences with genetically modified (GM) crops, governments in Sub-Sahara Africa have in recent years authorized various GM crop events for general, unconfined environmental release, and in a few cases, subsequent commercial variety registration and cultivation. These decisions are consistent with more business-oriented agricultural development policies aimed at turning agriculture into a leading driver of economic growth and wealth creation. Such policy reforms are also motivated by continent-wide initiatives described in this review paper, which emphasize seed sector reform, agricultural productivity growth, intra-regional trade, and agri-business development. While such policy developments spur a more proactive approach to introducing agricultural biotechnology, it becomes apparent that authorizing GM crops for general environmental release and commercial seed registration requires the harmonization of government policies in various sectors relevant to seed systems, most notably those affecting plant breeding research, testing, and variety release. Critical areas for such policy reforms and harmonization were analyzed in this paper, with associated recommendations. [ABSTRACT FROM AUTHOR]

Agricultural robotics has been a popular subject in recent years from an academic as well as a commercial point of view. This is because agricultural robotics addresses critical issues such as seasonal shortages in manual labor, e.g., during harvest, as well as the increasing concern regarding environmentally friendly practices. On one hand, several individual agricultural robots have already been developed for specific tasks (e.g., for monitoring, spraying, harvesting, transport, etc.) with varying degrees of effectiveness. On the other hand, the use of cooperative teams of agricultural robots in farming tasks is not as widespread; yet, it is an emerging trend. This paper presents a comprehensive overview of the work carried out so far in the area of cooperative agricultural robotics and identifies the state-of-the-art. This paper also outlines challenges to be addressed in fully automating agricultural production; the latter is promising for sustaining an increasingly vast human population, especially in cases of pandemics such as the recent COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Sustainable development has become a central and key goal for humanity (1 UN: Millenium Development Goals, 2 SDGs). There is no scientific or political consensus, however, about the root cause of unsustainability, so an effective cure is impossible. Nevertheless, unlimited inequality among nations and individuals, extreme poverty, and hunger are always among the most significant problems. It is also often claimed that the world's current food production would be sufficient to feed all 7.7 billion people on Earth (see Appendix A 1) with a better (more even, 'just') distribution. Others use food hunger and poverty data to support claims for increased food production and agricultural output. In this paper we examine the first claim with real data, but from a theoretical perspective: is it true, theoretically, that if all of the produced and wasted food were made available to feed the extremely poor and hungry, we could reach zero hunger-related deaths? We will use food waste, hunger-related (+thirst) death, obesity, and calorie consumption data from the "happy" and "unhappy" parts of the Earth. The cause of food waste is dissonant in different economic systems, so its macro- and microeconomic approach is also completely different in different countries. The relationship between obesity, hunger, and food waste has not yet been explored in a structured framework; data are available but scattered. In this paper we propose a unified framework of comparable data as a first step in mapping the biggest missing, or at least wobbling circle of modern bio-economy: global food supply. Thinking globally is a first step toward progress development in food justice and a solution to UN sustainable development goals (SDG 2, 1, 3, and 10). [ABSTRACT FROM AUTHOR]

The harvesting operation is a recurring task in the production of any crop, thus making it an excellent candidate for automation. In protected horticulture, one of the crops with high added value is tomatoes. However, its robotic harvesting is still far from maturity. That said, the development of an accurate fruit detection system is a crucial step towards achieving fully automated robotic harvesting. Deep Learning (DL) and detection frameworks like Single Shot MultiBox Detector (SSD) or You Only Look Once (YOLO) are more robust and accurate alternatives with better response to highly complex scenarios. The use of DL can be easily used to detect tomatoes, but when their classification is intended, the task becomes harsh, demanding a huge amount of data. Therefore, this paper proposes the use of DL models (SSD MobileNet v2 and YOLOv4) to efficiently detect the tomatoes and compare those systems with a proposed histogram-based HSV colour space model to classify each tomato and determine its ripening stage, through two image datasets acquired. Regarding detection, both models obtained promising results, with the YOLOv4 model standing out with an F1-Score of 85.81%. For classification task the YOLOv4 was again the best model with an Macro F1-Score of 74.16%. The HSV colour space model outperformed the SSD MobileNet v2 model, obtaining results similar to the YOLOv4 model, with a Balanced Accuracy of 68.10%. [ABSTRACT FROM AUTHOR]

In the last few decades, the increase in the world's population has created a need to produce more food, generating, consequently, greater pressure on agricultural production. In addition, problems related to climate change, water scarcity or decreasing amounts of arable land have serious implications for farming sustainability. Weeds can affect food production in agricultural systems, decreasing the product quality and productivity due to the competition for natural resources. On the other hand, weeds can also be considered to be valuable indicators of biodiversity because of their role in providing ecosystem services. In this sense, there is a need to carry out an effective and sustainable weed management process, integrating the various control methods (i.e., cultural, mechanical and chemical) in a harmonious way, without harming the entire agrarian ecosystem. Thus, intensive mechanization and herbicide use should be avoided. Herbicide resistance in some weed biotypes is a major concern today and must be tackled. On the other hand, the recent development of weed control technologies can promote higher levels of food production, lower the amount of inputs needed and reduce environmental damage, invariably bringing us closer to more sustainable agricultural systems. In this paper, we review the most common conventional and non-conventional weed control strategies from a sustainability perspective, highlighting the application of the precision and automated weed control technologies associated with precision weed management (PWM). [ABSTRACT FROM AUTHOR]

Agrometeorological services, as part of weather and climate services, are expected to play a key role in supporting sub-Saharan agriculture facing climate change and variability. In the Sahel, smallholder farmers relying on rainfed crop production systems are particularly vulnerable to climate change and variability because of low resilience and coping capacity. The provision of agrometeorological services is growing across Africa, but they often remain inaccessible for the majority of smallholder farmers or are not very relevant to support on-the-ground decision-making. Our work aims to demonstrate the hypothesis that agrometeorological services can effectively improve agricultural productivity and sustainability provided that appropriate mechanisms are put in place to ensure access, uptake and action. The paper illustrates the case study of Burkina Faso, where the National Meteorological Service, with the support of the World Meteorological Organization, engaged in the provision of accessible, reliable and relevant agrometeorological services for farmers. The study demonstrates that farmers, even in remote rural areas, are willing to profit from weather and climate services for strategic and tactical decisions in agricultural management because of relevant economic benefit. These benefits can be summarized as a 40% reduction in production costs and a 41% increase in income. Results also highlight environmental positive impacts such as the reduction by 50% in the use of fertilizers. Nevertheless, the study concludes that in order to scale-up weather and climate services in West Africa, a new business model released from the development projects approach should be explored. [ABSTRACT FROM AUTHOR]

This paper is focused on intervention scenarios for smallholder potato value chains in Kenya that improve yields, reduce losses, minimize greenhouse gas (GHG) emissions and increase economic performance and food security. For that, business models and GHG emission estimates are developed, based on the existing data, combined with relevancy and time-to-impact analysis using practical real-life information from stakeholders in the potato sector, which is the second largest agricultural crop in Kenya after maize. The results show that profitability, environmental improvement and food security can go hand in hand with realistic short-term interventions for potato production at smallholder farms. [ABSTRACT FROM AUTHOR]

Agriculture is the main and, in some cases, the only, source of income and employment in rural areas. The change in the conditions under which agriculture is practiced has various effects on the agricultural economy but also on the social structure of rural areas. Climate change has multiple effects on agricultural production, necessitating the reorganization of agricultural production in some cases. These effects of climate change will also impact the economic and social aspects of farms in rural areas. This paper attempts to identify these effects by measuring the socioeconomic impacts of climate change in the region of Central Macedonia in Greece. For this reason, a multicriteria model was developed to simulate these impacts by estimating a set of seven social and economic indicators. The model was implemented to the average farm which was estimated from the main cultivations of the region. A scenario analysis was also used in combination with the multicriteria model. The multicriteria model suggests modifications are needed in the average farm crop plan of the region as a result of the climate change impact. The scenarios results show that climate change will negatively affect all the social and economic indicators and will continue to affect them over the years. These results can be used by policymakers to understand the economic and social impacts of climate change in the region to plan their future policies. [ABSTRACT FROM AUTHOR]

Agricultural advancements have significantly impacted people's lives and their surroundings in recent years. The insufficient knowledge of the whole agricultural production system and conventional ways of irrigation have limited agricultural yields in the past. The remote sensing innovations recently implemented in agriculture have dramatically revolutionized production efficiency by offering unparalleled opportunities for convenient, versatile, and quick collection of land images to collect critical details on the crop's conditions. These innovations have enabled automated data collection, simulation, and interpretation based on crop analytics facilitated by deep learning techniques. This paper aims to reveal the transformative patterns of old Chinese agrarian development and fruit production by focusing on the major crop production (from 1980 to 2050) taking into account various forms of data from fruit production (e.g., apples, bananas, citrus fruits, pears, and grapes). In this study, we used production data for different fruits grown in China to predict the future production of these fruits. The study employs deep neural networks to project future fruit production based on the statistics issued by China's National Bureau of Statistics on the total fruit growth output for this period. The proposed method exhibits encouraging results with an accuracy of 95.56% calculating by accuracy formula based on fruit production variation. Authors further provide recommendations on the AGR-DL (agricultural deep learning) method being helpful for developing countries. The results suggest that the agricultural development in China is acceptable but demands more improvement and government needs to prioritize expanding the fruit production by establishing new strategies for cultivators to boost their performance. [ABSTRACT FROM AUTHOR]

Bambara groundnut (Vigna subterranea [L.] Verdc.) is a nutritionally rich grain legume crop indigenous to Africa. It is tolerant to drought stress and has become adapted to grow under low input and marginal agricultural production systems in Africa and Asia. Bambara groundnut is an orphan crop, and represents a neglected and under researched plant genetic resource. Modern crop management, production technologies, and value chains are yet to be developed in Africa to achieve the potential economic gains from Bambara groundnut production and marketing. In sub-Saharan Africa (SSA) the production and productivity of Bambara groundnut is low and stagnant because of diverse abiotic and biotic stresses and socio-economic constraints. Improved crop management and post handling technologies, modern varieties with high yield and nutritional quality, value addition, and market access are among the key considerations in current and future Bambara groundnut research and development programs. This paper presents progress on Bambara groundnut production, utilization, and genetic improvement in SSA. It presents the key production constraints, genetic resources and analysis, breeding methods and genetic gains on yield, and nutritional quality and outlook. The information presented will guide the sustainable production and effective breeding of the crop in order to pursue food and nutrition security, and improve livelihoods through Bambara groundnut enterprises. [ABSTRACT FROM AUTHOR]

This paper assessed the application of the Agricultural Production Systems sIMulator (APSIM)–maize module as a decision support tool for optimizing nitrogen application to determine yield and net return of maize production under current agricultural practices in the Nigeria savannas. The model was calibrated for two maize varieties using data from field experiments conducted under optimum conditions in three locations during the 2017 and 2018 cropping seasons. The model was evaluated using an independent dataset from an experiment conducted under different nitrogen (N) levels in two locations within Southern and Northern Guinea savannas. The results show that model accurately predicted days to 50% anthesis and physiological maturity, leaf area index (LAI), grain yield and total dry matter (TDM) of both varieties with low RMSE and RMSEn (%) values within the range of acceptable statistics indices. Based on 31-year seasonal simulation, optimum mean grain yield of 3941 kg ha−1 for Abuja, and 4549 for Kano was simulated at N rate of 120 kg ha–1 for the early maturing variety 2009EVDT. Meanwhile in Zaria, optimum mean yield of 4173 kg ha–1 was simulated at N rate of 90 kg ha−1. For the intermediate maturing variety, IWDC2SYNF2 mean optimum yields of 5152, 5462, and 4849 kg ha−1, were simulated at N application of 120 kg ha−1 for all the locations. The probability of exceeding attainable mean grain yield of 3000 and 4000 kg ha−1 for 2009EVDT and IWDC2SYNF2, respectively would be expected in 95% of the years with application of 90 kg N ha−1 across the three sites. Following the profitability scenarios analysis, the realistic net incomes of US$ 536 ha–1 for Abuja, and US$ 657 ha−1 for Zaria were estimated at N rate of 90 kg ha−1 and at Kano site, realistic net income of US$ 720 ha–1was estimated at N rate of 120 kg ha−1 for 2009EVDT.For IWDC2SYNF2, realistic net incomes of US$ 870, 974, and 818 ha−1 were estimated at N application of 120 kg ha−1 for Abuja, Zaria, and Kano respectively. The result of this study suggests that 90 kg N ha−1 can be recommended for 2009EVDT and 120 kg N ha–1 for IWDC2SYNF2 in Abuja and Zaria while in Kano, 120 kg N ha−1 should be applied to both varieties to attain optimum yield and profit. [ABSTRACT FROM AUTHOR]

In this work, an exhaustive revision is given of the literature associated with advanced information and communication technologies in agriculture within a window of 25 years using bibliometric tools enabled to detect of the main actors, structure, and dynamics in the scientific papers. The main findings are a trend of growth in the dynamics of publications associated with advanced information and communication technologies in agriculture productivity. Another assertion is that countries, like the USA, China, and Brazil, stand out in many publications due to allocating more resources to research, development, and agricultural productivity. In addition, the collaboration networks between countries are frequently in regions with closer cultural and idiomatic ties; additionally, terms' occurrence are obtained with Louvain algorithm predominating four clusters: precision agriculture, smart agriculture, remote sensing, and climate smart agriculture. Finally, the thematic-map characterization with Callon's density and centrality is applied in three periods. The first period of thematic analysis shows a transition in detecting the variability of a nutrient, such as nitrogen, through the help of immature georeferenced techniques, towards greater remote sensing involvement. In the transition from the second to the third stage, the maturation of technologies, such as unmanned aerial vehicles, wireless sensor networks, and the machine learning area, is observed. [ABSTRACT FROM AUTHOR]

Given the problem of climate change and the requirements laid down by the European Union in the field of gradual decarbonization of production, it is necessary to implement solutions of reducing greenhouse gas (GHG) emissions into agricultural practice. This research paper aimed to evaluate the carbon footprint and life-cycle costs of grain maize production in various tillage systems. The material for the analyses was data from 2015–2017 collected on 15 farms located in the Wielkopolska region (Poland) and growing maize for grain in three tillage systems: conventional, reduced, and no-tillage. The life-cycle assessment and life-cycle costing methodologies were applied to assess the GHG emissions and costs associated with the grain maize production in the stages from "cradle-to-farm gate", i.e., from obtaining raw materials and producing means for agricultural production, through the processes of maize cultivation to grain harvesting. The calculated values of the carbon footprint indicator for maize production in conventional, reduced, and no-tillage systems were 2347.4, 2353.4, and 1868.7 CO2 eq. ha−1, respectively. The largest source of GHG emissions was the use of nitrogen fertilizers. Non-inversion tillage with cover crops and leaving a large amount of crop residues in the field increased the sequestration of organic carbon and contributed to a significant reduction of the carbon footprint in maize production. The conventional tillage system demonstrated the highest overall life-cycle costs per hectare. [ABSTRACT FROM AUTHOR]

Protected cultivation systems such as greenhouses are becoming increasingly popular globally and have been adopted because of unpredictable climatic conditions and their ability to easily control micro- and macroenvironments. However, limitations such as hazardous work environments and shortages in labor are major concerns for agricultural production using these structures. This has led to the development and adoption of robotic systems. For the efficient use of robots in protected cultivation systems, we formulate the work efficiency problem and model a three-dimensional standard strawberry greenhouse to analyze the effectiveness of a strawberry-harvesting robot compared to different levels of human workforce (experienced, average, and beginner). Simulations are conducted using Quest software to compare the efficiency of different scenarios of robotics to humans. Different methods of improvement from battery capacity and charge rate to harvesting speed are investigated and optimal conditions are recommended. The average hourly production of the robot is about five times lower than that of skilled workers. However, robots are more productive due to their ability to work around the clock. Comparative analyses show that a reduction in harvesting time per strawberry from 3 to 1 s would result in an increase in daily production from 347.93 to 1021.30 kg. This would lead to a five-fold increase in comparison to present daily production. A 10% improvement in battery charge time would result in the battery capacity gaining two extra hours from the current 10 h and would cut the current 2 h needed for charge to 1 h. This paper proposes an operation process and suggestions for changes needed for improving the work efficiency of robots in a greenhouse. This could be extended to other crops and greenhouses. [ABSTRACT FROM AUTHOR]

Access and use of seed of improved varieties of groundnut among farmers can improve farmers' livelihoods and contribute to the potential of crop production in Tanzania. This paper analyzes factors underpinning the adoption of improved groundnut varieties among farmers to pave the way for upscaling quality seed used for increased production and commodity business in farming communities. A four-stage stratified sampling was used to collect data from 300 groundnut farmers in seven agro-ecological zones through individual interviews. Secondary data were collected from the literature and the Tanzania Agricultural Research Institute at Naliendele centre (TARI–Naliendele). Descriptive statistics and Probit regression model were used for data analysis. The empirical results showed that Johari 1985, Pendo 1998, Naliendele 2009, Mnanje 2009, Mangaka 2009 and Nachi 2015, are the main six improved groundnut varieties used by farmers, with Pendo 1998 having the highest adoption rate (17.1%). In the grain market, four varieties, namely Pendo 1998, Mnanje 2009, Nachi 2015 and Johari 1985, were observed to be highly preferred by grain off-takers. Furthermore, among the adopted improved varieties, Nachi 2015, is observed to be the most consistent high yielding variety, ranging from 1100 kg/ha to 1500 kg/ha in all agro-ecological zones. A farmer's decision to adopt new varieties is affected by age and gender, farmer group membership, availability of improved seed and seed cost. Overall, male farmers are more likely to adopt improved varieties of groundnut than female farmers. The implications of these findings are also discussed, in particular in the area of policy support. [ABSTRACT FROM AUTHOR]

Climate change is affecting all sectors of human activities worldwide, including crop production. The aim of the paper was to evaluate the average daily air temperatures measured at one hundred meteorological stations across Slovakia in 1961–2010 and calculate the maximum length of the vegetation period for Solanum lycopersicum L., Brassica oleracea L. var. capitata and Daucus carota L. Future trends predictions of the temporal and spatial development across the duration of the vegetation period in Slovakia were elaborated for decades 2011–2020, 2041–2050, 2071–2080 and 2091–2100. Our results show that there was an earlier start to the vegetation period in spring and a later termination in autumn for past 30 years. There is a predicted trend of prolongation of the maximum duration of the vegetation period up to 20 days (Solanum lycopersicum L., Brassica oleracea L. var. capitata) and 15 days (Daucus carota L.) in comparison with the refence decade 2001–2010. The maximum vegetation period duration will extend from the south of Slovakia towards the north of the country. The predicted potential increase in crop vegetation periods will be limited by other constraints such as the availability of arable land and soil water availability. [ABSTRACT FROM AUTHOR]

This paper investigates the influences of several limiting factors on the performance of ground penetrating radar (GPR) in accurately detecting huanglongbing (HLB)-infected citrus roots and determining their main structural characteristics. First, single-factor experiments were conducted to evaluate GPR performance. The factors that were evaluated were (i) root diameter; (ii) root moisture level; (iii) root depth; (iv) root spacing; (v) survey angle; and, (vi) soil moisture level. Second, two multi-factor field experiments were conducted to evaluate the performance of the GPR in complex orchard environments. The GPR generated a hyperbola in the radar profile upon root detection; the diameter of the root was successfully determined according to the width of the hyperbola when the roots were larger than 6 mm in diameter. The GPR also distinguished live from dead roots, a capability that is indispensable for studying the effects of soil-borne and other diseases on the citrus tree root system. The GPR can distinguish the roots only if their horizontal distance is greater than 10 cm and their vertical distance is greater than 5 cm if two or more roots are in proximity. GPR technology can be applied to determine the efficacy of advanced crop production strategies, especially under the pressures of disease and environmental stresses. [ABSTRACT FROM AUTHOR]

The insufficient food supply due to low agricultural productivity and quality standards is one of the major modern challenges of global agricultural food production. Advances in conventional breeding and crop domestication have begun to mitigate this issue by increasing varieties and generation of stress-resistant traits. Yet, very few species of legumes have been domesticated and perceived as usable food/feed material, while various wild species remain unknown and underexploited despite the critical global food demand. Besides the existence of a few domesticated species, there is a bottleneck challenge of product acceptability by both farmers and consumers. Therefore, this paper explores farmers' perceptions, preferences, and the possible utilization of some wild Vigna species of legumes toward their domestication and exploitation. Quantitative and qualitative surveys were conducted in a mid-altitude agro-ecological zone (Arusha region) and a high altitude agro-ecological zone (Kilimanjaro region) in Tanzania to obtain the opinions of 150 farmers regarding wild legumes and their uses. The study showed that very few farmers in the Arusha (28%) and Kilimanjaro (26%) regions were aware of wild legumes and their uses. The study further revealed through binary logistic regression analysis that the prior knowledge of wild legumes depended mainly on farmers' location and not on their gender, age groups, education level, or farming experience. From the experimental plot with 160 accessions of wild Vigna legumes planted and grown up to near complete maturity, 74 accessions of wild Vigna legumes attracted the interest of farmers who proposed various uses for each wild accession. A Χ2 test (likelihood ratio test) revealed that the selection of preferred accessions depended on the farmers' gender, location, and farming experience. Based on their morphological characteristics (leaves, pods, seeds, and general appearance), farmers perceived wild Vigna legumes as potentially useful resources that need the attention of researchers. Specifically, wild Vigna legumes were perceived as human food, animal feed, medicinal plants, soil enrichment material, and soil erosion-preventing materials. Therefore, it is necessary for the scientific community to consider these lines of farmers' suggestions before carrying out further research on agronomic and nutritional characteristics toward the domestication of these alien species for human exploitation and decision settings. [ABSTRACT FROM AUTHOR]