Search Results (46)

Field mapping is one of the most important aspects of precision agriculture, and community drones will be able to empower young rural entrepreneurs who will be the generational replacement of a new agrosocial paradigm. This research presents an agroecological participatory innovation methodology that utilizes precision technology through geographic information systems and unmanned aerial vehicles to evaluate the integrated ecological management of weeds for glyphosate substitution in a transitional area of Citrus sinensis in San Luis Potosí, Mexico. Modeling methods and spatial analyses supported by intelligent georeference protocols were used to determine the number of weeds with tolerance and glyphosate resistance. Four control flights were conducted to monitor seven treatments. Glyphosate-resistant weeds were represented with the highest number of individuals and frequency in all experimental treatments. Although the treatment with maize stubble showed a slightly better result than the use of Mucuna pruriens mulch, which prevents the emergence of glyphosate resistant weeds before emergence, the second treatment is considered better in terms of the cost–benefit ratio, not only because of significantly lower cost but also because of the additional benefits it offers. Geospatial technologies will determine the nature of citrus and fruit tree agroecological treatments and highlight areas of the plot with binomial soil and plant nutrient deficiencies and pest and disease infestations, which will improve the timely application of bio-inputs through the development of accurate maps of agroecological transitions. Full article

Precision pesticide application is a key focus in orchard management, with targeted spraying serving as a core technology to optimize pesticide delivery and reduce environmental pollution. However, its accurate implementation relies on high-precision environmental sensing technologies to enable the precise identification of target objects and dynamic regulation of spraying strategies. This paper systematically reviews the application of orchard environmental sensing technologies in targeted spraying. It first focuses on key sensors used in environmental sensing, providing an in-depth analysis of their operational mechanisms and advantages in orchard environmental perception. Subsequently, this paper discusses the role of multi-source data fusion and artificial intelligence analysis techniques in improving the accuracy and stability of orchard environmental sensing, supporting crown structure modeling, pest and disease monitoring, and weed recognition. Additionally, this paper reviews the practical paths of environmental sensing-driven targeted spraying technologies, including variable spraying strategies based on canopy structure perception, precise pesticide application methods combined with intelligent pest and disease recognition, and targeted weed control technologies relying on weed and non-target area detection. Finally, this paper summarizes the challenges faced by multi-source sensing and targeted spraying technologies in light of current research progress and industry needs, and explores potential future developments in low-cost sensors, real-time data processing, intelligent decision making, and unmanned agricultural machinery. Full article

Lettuce, a vital economic crop, benefits significantly from intelligent advancements in its production, which are crucial for sustainable agriculture. Deep learning, a core technology in smart agriculture, has revolutionized the lettuce industry through powerful computer vision techniques like convolutional neural networks (CNNs) and YOLO-based models. This review systematically examines deep learning applications in lettuce production, including pest and disease diagnosis, precision spraying, pesticide residue detection, crop condition monitoring, growth stage classification, yield prediction, weed management, and irrigation and fertilization management. Notwithstanding its significant contributions, several critical challenges persist, including constrained model generalizability in dynamic settings, exorbitant computational requirements, and the paucity of meticulously annotated datasets. Addressing these challenges is essential for improving the efficiency, adaptability, and sustainability of deep learning-driven solutions in lettuce production. By enhancing resource efficiency, reducing chemical inputs, and optimizing cultivation practices, deep learning contributes to the broader goal of sustainable agriculture. This review explores research progress, optimization strategies, and future directions to strengthen deep learning’s role in fostering intelligent and sustainable lettuce farming. Full article

In modern agriculture, plant protection is the key to ensuring crop health and improving yields. Intelligent pesticide prescription spraying (IPPS) technologies monitor, diagnose, and make scientific decisions about pests, diseases, and weeds; formulate personalized and precision control plans; and prevent and control pests through the use of intelligent equipment. This study discusses key IPSS technologies from four perspectives: target information acquisition, information processing, pesticide prescription spraying, and implementation and control. In the target information acquisition section, target identification technologies based on images, remote sensing, acoustic waves, and electronic nose are introduced. In the information processing section, information processing methods such as information pre-processing, feature extraction, pest and disease identification, bioinformatics analysis, and time series data are addressed. In the pesticide prescription spraying section, the impact of pesticide selection, dose calculation, spraying time, and method on the resulting effect and the formulation of prescription pesticide spraying in a certain area are explored. In the implement and control section, vehicle automatic control technology, precision spraying technology, and droplet characteristic control technology and their applications are studied. In addition, this study discusses the future development prospectives of IPPS technologies, including multifunctional target information acquisition systems, decision-support systems based on generative AI, and the development of precision intelligent sprayers. The advancement of these technologies will enhance agricultural productivity in a more efficient, environmentally sustainable manner. Full article

Weeds in paddy fields compete with rice for nutrients and cause pests and diseases, greatly affecting rice yield. Accurate weed detection is vital for implementing variable spraying with unmanned aerial vehicles (UAV) for weed control. Therefore, this paper presents an improved weed detection algorithm, YOLOv10n-FCDS (YOLOv10n with FasterNet, CGBlock, Dysample, and Structure of Lightweight Detection Head), using UAV images of Sagittaria trifolia in rice fields as the research object, to address challenges like the detection of small targets, obscured weeds and weeds similar to rice. We enhanced the YOLOv10n model by incorporating FasterNet as the backbone for better small target detection. CGBlock replaced standard convolution and SCDown modules to improve the detection ability of obscured weeds, while DySample enhanced discrimination between weeds and rice. Additionally, we proposed a lightweight detection head based on shared convolution and scale scaling, maintaining accuracy while reducing model parameters. Ablation studies revealed that YOLOv10n-FCDS achieved a 2.6% increase in mean average precision at intersection over union 50% for weed detection, reaching 87.4%. The model also improved small target detection (increasing mAP50 by 2.5%), obscured weed detection (increasing mAP50 by 2.8%), and similar weed detection (increasing mAP50 by 3.0%). In conclusion, YOLOv10n-FCDS enables effective weed detection, supporting variable spraying applications by UAVs in rice fields. Full article

Currently, one of the main challenges is the mitigation of the effects of climate change on the agricultural sector. Conventional agriculture, with the intensive use of herbicides and pesticides to control weeds and pests, and the improper use of mineral fertilizers, contributes to climate change by causing increased greenhouse gases and groundwater pollution. Therefore, more innovative technologies must be used to overcome these problems. One possible solution is nanotechnology, which has the potential to revolutionize the conventional agricultural system. Active nanoparticles can be used both as a direct source of micronutrients and as a delivery platform for bioactive agrochemicals to improve crop growth, yield, and quality. The use of nanoparticle formulations, including nano-pesticides, nano-herbicides, nano-fertilizers, and nano-emulsions, has been extensively studied to improve crop health and shelf-life of agricultural products. Comprehensive knowledge of the interactions between plants and nanoparticles opens up new opportunities to improve cropping practices through the enhancement of properties such as disease resistance, crop yield, and nutrient use. The main objective of this review is to analyze the main effects of climate change on conventional agricultural practices, such as the use of pesticides, herbicides, and fertilizers. It also focuses on how the introduction of nanoparticles into conventional practices can improve the efficiency of chemical pest control and crop nutrition. Finally, this review examines in depth the last 10 years (2014–2024) of scientific literature regarding the use of nanoparticles in agriculture to mitigate the effects of climate change. Full article

The widespread use of IoT devices has led to the generation of a huge amount of data and driven the need for analytical solutions in many areas of human activities, such as the field of smart agriculture. Continuous monitoring of crop growth stages enables timely interventions, such as control of weeds and plant diseases, as well as pest control, ensuring optimal development. Decision-making systems in smart agriculture involve image analysis with the potential to increase productivity, efficiency and sustainability. By applying Convolutional Neural Networks (CNNs), state recognition and classification can be performed based on images from specific locations. Thus, we have developed a solution for early problem detection and resource management optimization. The main concept of the proposed solution relies on a direct connection between Cloud and Edge devices, which is achieved through Fog computing. The goal of our work is creation of a deep learning model for image classification that can be optimized and adapted for implementation on devices with limited hardware resources at the level of Fog computing. This could increase the importance of image processing in the reduction of agricultural operating costs and manual labor. As a result of the off-load data processing at Edge and Fog devices, the system responsiveness can be improved, the costs associated with data transmission and storage can be reduced, and the overall system reliability and security can be increased. The proposed solution can choose classification algorithms to find a trade-off between size and accuracy of the model optimized for devices with limited hardware resources. After testing our model for tomato disease classification compiled for execution on FPGA, it was found that the decrease in test accuracy is as small as 0.83% (from 96.29% to 95.46%). Full article

Uncrewed aerial vehicles (UAVs) for plant protection play a vital role in modern agricultural operations. In recent years, advancements in UAVs and pest control technologies have significantly enhanced operational efficiency. These innovations have addressed historical challenges in agricultural practices by improving automation and precision in managing insect pests, diseases, and weeds. UAVs offer high operational efficiency, wide adaptability to different terrain, and safe applications. The development and demand for these technologies have increased to boost agricultural production. In agricultural settings where conventional machinery struggles to carry out farming operations, UAVs have transformed farming practices by providing high operational efficiency and significant profitability. The integration of UAVs and other smart technologies has driven advancements. The UAV sector has received substantial attention as a convergence of production, service, and delivery, introducing synergy through the presence of several developing areas. The market for this technology is expected to grow in the future. In this comprehensive review, we analyzed an overview of historical research, diverse techniques, the transition from conventional to advanced application, development trends, and operational milestones across diverse cropping systems. We also discussed adoption and subsidy policies. In order to properly understand UAV operational efficiency, we also analyzed and discussed smart atomization systems, spray drift, droplet deposition detection technologies, and the capabilities of related technologies. Additionally, we reviewed the role of software programs, data-driven tools, biodegradable materials, payloads, batteries, sensing technologies, weather, and operational and spraying factors. Regulatory limitations, operating and farmer’s training, economic effects, and guidelines were also acknowledged in this review. This review highlights deficiencies and provides essential knowledge of the use of UAVs for agriculture tasks in different regions. Finally, we examine the urgency of UAV technology implementations in the agricultural sector. In conclusion, we summarize the integration of UAVs and their related technologies with applications and future research prospects, offering directions for follow-up research on the key technologies of UAVs and encouraging the enhancement of agricultural production management in terms of efficiency, accuracy, and sustainability. Full article

Watermelon is one of the most commonly grown vegetable crops worldwide due to the economic and nutritional importance of its fruits. The yield and quality of watermelon fruits are affected by constant attacks from pests, diseases, and weeds throughout all phenological stages of the crop. Labor shortages and unevenness of pesticide applications using backpack and tractor sprayers are significant challenges. The objective of this study was to evaluate the effect of different spray nozzles (XR110015 and MGA60015) and application rates (8, 12, and 16 L ha⁻¹) on droplet distribution on different targets in watermelon plants using an unmanned aerial vehicle. Water-sensitive papers were used as targets to analyze the droplet coverage, deposition, density, and volume median diameter. Data were collected from targets placed on the leaf adaxial and abaxial sides, fruit, apical bud, and stem of each plant. The mean droplet coverage and density increased as the application rate was increased, with no significant interaction between the factors or statistical difference between spray nozzles, except for the leaf abaxial side. No significant differences were found for the variables analyzed at application rates of 12 and 16 L ha⁻¹, whereas significant differences were observed at 8 L ha⁻¹. The use of unmanned aerial vehicles in watermelon crops is efficient; however, further studies should be conducted to evaluate their effectiveness in pest control and compare them with other application methods. Full article

The Green Deal strategy has the very ambitious goal of transforming the European Union into the first climate-neutral continent by 2050. For the agricultural sector, one of the main challenges is to reduce the use of synthetic fertilizers and pesticides. Crop protection measures aim to maintain and ensure certain standards of yield and quality, which are generally achieved by the control of pests, diseases, and weeds. One of the possibilities to reduce the use of pesticides could be allelopathic plants, which are not only potential sources of allelochemicals but also renewable biomass sources. The aim of this study was to analyze the productivity of Artemisia dubia Wall and evaluate the allelopathic effects of biomass on crops and weeds. It was determined that the biomass productivity of A. dubia varied from 2 to 18 t ha⁻¹, depending on how many times it is cut during the growing season and the fertilizer rate. A. dubia has allelopathic properties, which were verified using an aqueous extract and can completely suppress the germination of Taraxacum officinale seeds. Young plants harvested in the middle of summer were characterized by the highest number of phenolic compounds. This shows the strong allelopathic effect of A. dubia biomass on other plants. Full article

Effective disease, pest, and weed control are essential for achieving sustainable agricultural practices. The ever-growing global population, coupled with the increasing demand for food, poses a significant challenge to agriculture systems globally. To address this challenge sustainably, farmers must employ effective disease, pest, and weed control measures that minimize the negative impacts on the environment, human health, and biodiversity. This study investigates the impact of innovative control methods on agricultural productivity, focusing on 30 farmers (21 male and 9 female) in the Bosome Freho District of Ghana. The goal of this research is to offer scalable solutions to maximize crop yields while reducing the use of environmentally-unfriendly agro-chemicals. This study employed a participatory approach, engaging farmers in the co-creation and implementation of sustainable control measures. Through a combination of integrated pest management techniques, biological control, and cultural practices, farmers were able to significantly reduce the prevalence of diseases, pests, and weeds on their fields. The results demonstrate a remarkable improvement in crop health, with increased yield and quality observed across various crops, such as maize, pepper, and plantain. The scalability of these achieved results is a key highlight, as the implemented strategies are easily transferable to other farms within the Bosome Freho District and beyond. The innovative nature of this study lies in the collaborative approach, which incorporates traditional knowledge and modern agricultural techniques, thereby bridging the gap between traditional and sustainable farming practices. This study proposes workable ways to increase agricultural productivity while safeguarding the environment and ensuring the long-term viability of farming communities by tackling the key issue of disease, pest, and weed control in a sustainable manner. Full article

The development and implementation of safe natural alternatives to synthetic pesticides are urgent needs that will provide ecological solutions for the control of plant diseases, bacteria, viruses, nematodes, pests, and weeds to ensure the economic stability of farmers and food security, as well as protection of the environment and human health. Unambiguously, production of botanical pesticides will allow for the sustainable and efficient use of natural resources and finally decrease the use of chemical inputs and burden. This is further underlined by the strict regulations on pesticide residues in agricultural products and is in harmony with the Farm to Fork strategy, which aims to reduce pesticide use by 50% by 2030. Thus, the present work aims to compile the scientific knowledge of the last 5 years (2017–February 2023) regarding the Mediterranean plants that present biopesticidal effects. The literature review revealed 40 families of Mediterranean plants with at least one species that have been investigated as potential biopesticides. However, only six families had the highest number of species, and they were reviewed comprehensively in this study. Following a systematic approach, the extraction methods, chemical composition, biopesticidal activity, and commonly used assays for evaluating the antimicrobial, pesticidal, repellant, and herbicidal activity of plant extracts, as well as the toxicological and safety aspects of biopesticide formulation, are discussed in detail. Finally, the aspects that have not yet been investigated or are under-investigated and future perspectives are highlighted. Full article

The ability of plants to release chemicals that affect the growth of other plants offers potential benefits for weed management and sustainable agriculture. This review explores the use of Camelina sativa as a promising cover crop with weed control potential. Camelina sativa, known for its high oil content and adaptability to diverse climatic conditions, exhibits allelopathic potential by releasing chemical compounds that inhibit weed growth. The crop’s vigorous growth and canopy architecture contribute to effective weed suppression, reducing the prevalence and spread of associated pathogens. Furthermore, the chemical compounds released by camelina through the solubilization of compounds from leaves by rain, root exudation, or deriving from microbial-mediated decay of camelina’s tissues interfere with the growth of neighbouring plants, indicating allelopathic interactions. The isolation and identification of benzylamine and glucosinolates as allelochemicals in camelina highlight their role in plant–plant interactions. However, the studies carried out on this species are outdated, and it cannot be excluded that other chemicals deriving from the breakdown of the glucosinolates or belonging to other classes of specialized metabolites can be involved in its allelopathic potential. Camelina sativa also demonstrates disease suppression capabilities, with glucosinolates exhibiting fungicidal, nematocidal, and bactericidal activities. Additionally, camelina cover crops have been found to reduce root diseases and enhance growth and yields in corn and soybeans. This review sheds light on the allelopathic and agronomic benefits of Camelina sativa, emphasizing its potential as a sustainable and integrated pest management strategy in agriculture. Full article

Agriculture 5.0 refers to the next phase of agricultural development, building upon the previous digital revolution in the agrarian sector and aiming to transform the agricultural industry to be smarter, more effective, and ecologically conscious. Farming processes have already started becoming more efficient due to the development of digital technologies, including big data, artificial intelligence (AI), robotics, the Internet of Things (IoT), and virtual and augmented reality. Farmers can make the most of the resources at their disposal thanks to this data-driven approach, allowing them to effectively cultivate and sustain crops on arable land. The European Union (EU) aims to make food systems fair, healthy, and environmentally sustainable through the Green Deal and its farm-to-fork, soil, and biodiversity strategies, zero pollution action plan, and upcoming sustainable use of pesticides regulation. Many of the historical synthetic pesticides are not currently registered in the EU market. In addition, the continuous use of a limited number of active ingredients with the same mode of action scales up pests/pathogens/weed resistance potential. Increasing plant protection challenges as well as having fewer chemical pesticides to apply require innovation and smart solutions for crop production. Biopesticides tend to pose fewer risks to human health and the environment, their efficacy depends on various factors that cannot be controlled through traditional application strategies. This paper aims to disclose the contribution of robotic systems in Agriculture 5.0 ecosystems, highlighting both the challenges and limitations of this technology. Specifically, this work documents current threats to agriculture (climate change, invasive pests, diseases, and costs) and how robotics and AI can act as countermeasures to deal with such threats. Finally, specific case studies and the application of intelligent robotic systems to them are analyzed, and the architecture for our intelligent decision system is proposed. Full article

Substantial amounts of pesticides, used in agricultural production to control pests, diseases, and weeds, and thereby attain high product quantities and quality, can severely affect the ecosystem and human health. The amounts of pesticides used depend on the specifics of the current production system but also exhibit large effects of past practices. Pesticides do not act only on the target organisms but also on organisms for which the chemicals were not specifically formulated, constituting hazardous molecules for humans and the environment. Pesticides, therefore, also influence soil microbial communities including organisms that engage in mutualistic plant symbioses that play a crucial role in its mineral nutrition, such as arbuscular mycorrhizal fungi. In this review, we summarize the current knowledge on the effects of synthetic and natural (‘green’) pesticides (fungicides, herbicides, and insecticides) on arbuscular mycorrhizal symbiosis. We deal with both the direct effects (spore germination and extraradical and intraradical growth of the mycelium) and indirect effects on the agroecosystem level. Such indirect effects include effects through the spread of herbicide-resistant crops and weeds to neighboring ecosystems, thereby modifying the mycorrhizal inoculum potential and altering the plant–plant interactions. We also briefly discuss the possibility that mycorrhizal plants can be used to enhance the phytoremediation of organic pesticides. Full article