To improve the counting accuracy in dense rice seed scenarios, this study proposes a YOLOv5-based dense rice seed counting method that integrates C3CBAM and Soft-NMS. This method integrates the CBAM attention module into the shallow C3 modules of the backbone network to enhance image features. Additionally, it removes the original large and medium-sized object detection heads of YOLOv5 and adds a dedicated detection head for tiny rice seeds. For post-processing of model prediction data, the Soft-NMS algorithm is employed to replace standard Non-Maximum Suppression (NMS) and reduce missed detections. Finally, image acquisition, seed counting, and a user interface are integrated into a single system, enabling rice breeders to conduct seed counting tasks more intuitively and efficiently. Compared with the baseline YOLOv5 model, the recall and mAP@[0.5:0.95] of the improved model increase by 6.4 % and 5.7 %, respectively. Furthermore, this study designs experiments with three levels of seed density. In the intermediate-type rice seed samples, the detection accuracy reaches 100 % under light and moderate density conditions, while it maintains stable counting performance under heavy density conditions with an accuracy above 99.7 %. This work significantly enhances rice seed counting efficiency for researchers and facilitates rice variety improvement studies.
Nico Heider, Lorenz Gunreben, Sebastian Zürner
et al.
In agricultural research, there has been a recent surge in the amount of Computer Vision (CV) focused work. But unlike general CV research, large high-quality public datasets are sparsely available. This can be partially attributed to the high variability between different agricultural tasks, crops and environments as well as the complexity of data collection, but it is also influenced by the reticence to publish datasets by many authors. This, as well as the lack of a widely used agricultural data repository, are impactful factors that hinder research in applied CV for agriculture as well as the usage of agricultural data in general-purpose CV research. In this survey, we provide a large number of high-quality datasets of images taken on fields. Overall, we find 45 datasets, which are listed in this paper as well as in an online catalog on the project website: https://smartfarminglab.github.io/field_dataset_survey/.
Data scarcity in African agriculture hampers machine learning (ML) model performance, limiting innovations in precision agriculture. The Synthetic Agriculture Data for Africa (SAGDA) library, a Python-based open-source toolkit, addresses this gap by generating, augmenting, and validating synthetic agricultural datasets. We present SAGDA's design and development practices, highlighting its core functions: generate, model, augment, validate, visualize, optimize, and simulate, as well as their roles in applications of ML for agriculture. Two use cases are detailed: yield prediction enhanced via data augmentation, and multi-objective NPK (nitrogen, phosphorus, potassium) fertilizer recommendation. We conclude with future plans for expanding SAGDA's capabilities, underscoring the vital role of open-source, data-driven practices for African agriculture.
Kevin Lumbard, Vinod Kumar Ahuja, Matt Cantu Snell
Agriculture contributes trillions of dollars to the US economy each year. Digital technologies are disruptive forces in agriculture. The open source movement is beginning to emerge in agriculture technology and has dramatic implications for the future of farming and agriculture digital technologies. The convergence of open source and agriculture digital technology is observable in scientific research, but the implications of open source ideals related to agriculture technology have yet to be explored. This study explores open agriculture digital technology through a systematic mapping of available open agriculture digital technology research. The study contributes to Information Systems research by illuminating current trends and future research opportunities.
By studying the Inca Empire's agricultural accomplishments, we learn how ancient civilizations adapted to their circumstances, used natural resources effectively, and sustained agriculture for millennia. This understanding affects global food production systems as we face land degradation, climate change, and sustainable farming. Inca terrace farming is a sustainable and innovative food production method still relevant today. By studying the past and applying its ideas to modern agriculture, we can make global food production more sustainable and resilient. Keywords: Inca Empire, Terrace farming, Agricultural innovation, Food production, ancient civilizations, Crop diversity
Omar H. Khater, Abdul Jabbar Siddiqui, M. Shamim Hossain
et al.
Sustainable agriculture plays a crucial role in ensuring world food security for consumers. A critical challenge faced by sustainable precision agriculture is weed growth, as weeds compete for essential resources with crops, such as water, soil nutrients, and sunlight, which notably affect crop yields. The adoption of automated computer vision technologies and ground agricultural consumer electronic vehicles in precision agriculture offers sustainable, low-carbon solutions. However, prior works suffer from issues such as low accuracy and precision, as well as high computational expense. This work proposes EcoWeedNet, a novel model that enhances weed detection performance without introducing significant computational complexity, aligning with the goals of low-carbon agricultural practices. The effectiveness of the proposed model is demonstrated through comprehensive experiments on the CottonWeedDet12 benchmark dataset, which reflects real-world scenarios. EcoWeedNet achieves performance comparable to that of large models (mAP@0.5 = 95.2%), yet with significantly fewer parameters (approximately 4.21% of the parameters of YOLOv4), lower computational complexity and better computational efficiency 6.59% of the GFLOPs of YOLOv4). These key findings indicate EcoWeedNet's deployability on low-power consumer hardware, lower energy consumption, and hence reduced carbon footprint, thereby emphasizing the application prospects of EcoWeedNet in next-generation sustainable agriculture. These findings provide the way forward for increased application of environmentally-friendly agricultural consumer technologies.
Large models can play important roles in many domains. Agriculture is another key factor affecting the lives of people around the world. It provides food, fabric, and coal for humanity. However, facing many challenges such as pests and diseases, soil degradation, global warming, and food security, how to steadily increase the yield in the agricultural sector is a problem that humans still need to solve. Large models can help farmers improve production efficiency and harvest by detecting a series of agricultural production tasks such as pests and diseases, soil quality, and seed quality. It can also help farmers make wise decisions through a variety of information, such as images, text, etc. Herein, we delve into the potential applications of large models in agriculture, from large language model (LLM) and large vision model (LVM) to large vision-language models (LVLM). After gaining a deeper understanding of multimodal large language models (MLLM), it can be recognized that problems such as agricultural image processing, agricultural question answering systems, and agricultural machine automation can all be solved by large models. Large models have great potential in the field of agriculture. We outline the current applications of agricultural large models, and aims to emphasize the importance of large models in the domain of agriculture. In the end, we envisage a future in which famers use MLLM to accomplish many tasks in agriculture, which can greatly improve agricultural production efficiency and yield.
Agriculture faces critical challenges from population growth, resource scarcity, and climate change, driving a shift toward advanced, technology-integrated farming. Mechanization has transformed agriculture, enhancing sustainability and crop productivity. Now, technologies like artificial intelligence (AI), robotics, biotechnology, blockchain, and the Internet of Things (IoT) are advancing precision agriculture. The concept of the Internet of Everything (IoE) has gained traction due to its holistic approach to integrating various IoT specializations, called IoXs with X referring to a specific domain. This paper explores the transformative role of IoE in agriculture, expanding beyond traditional IoT applications to integrate niche subdomains like molecular communication (MC), the Internet of Nano Things (IoNT), the Internet of Bio-Nano Things (IoBNT), designer phages, and the Internet of Fungus (IoF). Our study provides a detailed review of how these IoE subdomains, in conjunction with 6G, blockchain, and machine learning (ML), can enhance precision farming in areas like crop monitoring, resource management, and disease control. Unlike prior IoT centric reviews, this work uniquely focuses on IoEs potential to advance agriculture at molecular and biological scales, achieving more precise resource utilization and resilience. Key contributions include an exploration of these technologies applicability, associated challenges, and recommendations for future research directions within precision agriculture.
Ashwin Natraj Arun, Byunghyun Lee, Fabio A. Castiblanco
et al.
One of the most intriguing 6G vertical markets is precision agriculture, where communications, sensing, control, and robotics technologies are used to improve agricultural outputs and decrease environmental impact. Ambient IoT (A-IoT), which uses a network of devices that harvest ambient energy to enable communications, is expected to play an important role in agricultural use cases due to its low costs, simplicity, and battery-free (or battery-assisted) operation. In this paper, we review the use cases of precision agriculture and discuss the challenges. We discuss how A-IoT can be used for precision agriculture and compare it with other ambient energy source technologies. We also discuss research directions related to both A-IoT and precision agriculture.
Agriculture is a vital sector that significantly contributes to the economy and food security, particularly in regions like Varanasi, India. This paper explores various types of agriculture practiced in the area, including subsistence, commercial, intensive, extensive, industrial, organic, agroforestry, aquaculture, and urban agriculture. Each type presents unique challenges and opportunities, necessitating innovative approaches to enhance productivity and sustainability. To address these challenges, the integration of advanced technologies such as sensors and communication protocols is essential. Sensors can provide real-time data on soil health, moisture levels, and crop conditions, enabling farmers to make informed decisions. Communication technologies facilitate the seamless transfer of this data, allowing for timely interventions and optimized resource management. Moreover, programming techniques play a crucial role in developing applications that process and analyze agricultural data. By leveraging machine learning algorithms, farmers can gain insights into crop performance, predict yields, and implement precision agriculture practices. This paper highlights the significance of combining traditional agricultural practices with modern technologies to create a resilient agricultural ecosystem. The findings underscore the potential of integrating sensors, communication technologies, and programming in transforming agricultural practices in Varanasi. By fostering a data-driven approach, this research aims to contribute to sustainable farming, enhance food security, and improve the livelihoods of farmers in the region.
White mango scale (WMS) Aulacaspis tubercularis Newstead (Hemiptera: Diaspididae) is a polyphagous armored scale insect which is considered one of the key pests of mango (Mangifera indica L.) around the world. Mango is widely grown in Ethiopia whereas its production is challenged by WMS in the last decade. Effective formulations that could help manage the scale as part of IPM practice were sought from field experiments at Seka mango farm, Ethiopia in 2019 and 2020 seasons. The study aimed to evaluate the efficacy of some formulations against WMS on mango trees. Randomized complete block designs with three replications were used for the experiments and each tree served as a plot. Allocation of each treatment within each replication was done randomly. The treatments were applied sequentially three times at 14 days interval using motorized Knapsack sprayer coinciding with peak period of natural infestation. Scale numbers before and after each spray were counted using a microscope with LCD. Sum of live crawler, female and male was registered as WMS count data. Results showed that dimethoate, diazinon, imidacloprid & λ-cyhalothrin sprayed alone; dimethoate rotated with imidacloprid & λ-cyhalothrin, chlorpyrifos-ethyl rotated with paraffin, and diazinon rotated with azadirachtin, caused total mortality of the scales. The results also showed that, chlorpyrifos-ethyl, deltamethrin, paraffin oil and λ-cyhalothrin sprayed alone caused percent reduction with range 83–95 % in both seasons. Hence, the study revealed that dimethoate, diazinon, imidacloprid & λ-cyhalothrin applied individually, dimethoate rotated with imidacloprid & λ-cyhalothrin, chlorpyrifos-ethyl rotated with paraffin and diazinon rotated with azadirachtin fully protect mango trees from WMS and significantly superior to other treatments. Therefore, chemical control of A. tubercularis may consider the use of these materials as foliar application and can be used as components for integrated pest management plans for WMS. However, application in the form of rotation is preferred to the alone spray as the former could substantially reduce the likelihood of inducing pesticide resistance. Cost implications and effects of the products on the natural enemy and residual toxicity in fruits need to be studied.
Salinity poses a significant constraint to cereal productivity particularly in arid and semiarid regions. The application of allelochemical has shown promising results in mitigating the intensity of abiotic stresses. A pot experiment was conducted to assess the efficacy of different concentrations of aqueous allelopathic extract derived from moringa leaves in mitigating the adverse impacts of salinity on the germination and growth of maize cultivars via seed priming. The study involved three variables: two cultivars of maize, ‘Pioneer 30Y87’ (salt tolerant) and ‘Pioneer 30T60’ (salt sensitive) e seed priming with moringa leaf extract (MLE) at varying concentrations of 0.5%, 1.0%, 1.5%, 2.0%, 2.5%, 3.0%, and hydro-priming as control; and different salinity levels of 0, 6, and 12 dS m-1. Salinity had a negative impact on the germination process, leading to delayed and suboptimal growth of seedlings. Additionally, salinity reduced the synthesis of photosynthetic pigments (20-50%), photosynthesis, transpiration, internal carbon, and stomatal conductance. Further, MLE also improved the antioxidant activities (catalase: CAT and peroxidase: POD) by 22-56% which reduced the hydrogen peroxide production. Moreover, ‘P-30Y87’ exhibited favorable performance in terms of better germination, growth, photosynthesis and antioxidant activities. The application of moringa leaf extract (3%) resulted in a more notable hermetic effect in elevating salinity stress thereby enhancing germination, growth, photosynthesis and antioxidant activities. In the conclusion, application of MLE (3%) is a promising approach to mitigate the adverse impacts of salinity by improving germination, growth, photosynthesis and antioxidant activities.
Agriculture is a huge domain where an enormous landscape of systems interact to support agricultural processes, which are becoming increasingly digital. From the perspective of agricultural service providers, a prominent challenge is interoperability. In the Fraunhofer lighthouse project Cognitive Agriculture (COGNAC), we investigated how the usage of Industry 4.0 digital twins (I4.0 DTs) can help overcome this challenge. This paper contributes architecture drivers and a solution concept using I4.0 DTs in the agricultural domain. Furthermore, we discuss the opportunities and limitations offered by I4.0 DTs for the agricultural domain.
Agriculture affects global warming, while its yields are threatened by it. Information and communication technology (ICT) is often considered as a potential lever to mitigate this tension, through monitoring and process optimization. However, while agricultural ICT is actively promoted, its environmental impact appears to be overlooked. Possible rebound effects could put at stake its net expected benefits and hamper agriculture sustainability. By adapting environmental footprint assessment methods to digital agriculture context, this research aims at defining a methodology taking into account the environmental footprint of agricultural ICT systems and their required infrastructures. The expected contribution is to propose present and prospective models based on possible digitalization scenarios, in order to assess effects and consequences of different technological paths on agriculture sustainability, sufficiency and resilience. The final results could be useful to enlighten societal debates and political decisions.
Peeyush Kumar, Andrew Nelson, Zerina Kapetanovic
et al.
Farms produce hundreds of thousands of data points on the ground daily. Farming technique which combines farming practices with the insights uncovered in these data points using AI technology is called precision farming. Precision farming technology augments and extends farmers' deep knowledge about their land, making production more sustainable and profitable. As part of the larger effort at Microsoft for empowering agricultural labor force to be more productive and sustainable, this paper presents the AI technology for predicting micro-climate conditions on the farm. This article is a chapter in publication by Food and Agriculture Organization of the United Nations and International Telecommunication Union Bangkok, 2021. This publication on artificial intelligence (AI) for agriculture is the fifth in the E-agriculture in Action series, launched in 2016 and jointly produced by FAO and ITU. It aims to raise awareness about existing AI applications in agriculture and to inspire stakeholders to develop and replicate the new ones. Improvement of capacity and tools for capturing and processing data and substantial advances in the field of machine learning open new horizons for data-driven solutions that can support decision-making, facilitate supervision and monitoring, improve the timeliness and effectiveness of safety measures (e.g. use of pesticides), and support automation of many resource-consuming tasks in agriculture. This publication presents the reader with a collection of informative applications highlighting various ways AI is used in agriculture and offering valuable insights on the implementation process, success factors, and lessons learnt.
Kulwa Mwita Mang'ana, Daniel Wilson Ndyetabula, Silver John Hokororo
Small and Medium-Sized Enterprises in agriculture sector, contribute significantly to economic change in developing countries by addressing a wide range of unemployment, nutrition, income poverty, and food security issues. Despite their critical role and contribution to economic growth, they have received a great deal of criticism for their poor performance. Most of the challenges confronting these agro-enterprises, however, are the result of poor financial management practices. Previous research studies have indicated generally that financial management practices have an impact on the performance and success for small businesses, yet scholarly research shows there is limited empirical evidence on which financial management practices have an influence on the agri-SMEs performance, which is why it was critical to examine this phenomenon. A total of 427 SMEs in Tanzania's agricultural sector were surveyed and examined. The developed hypotheses were evaluated using Structural Equation Modeling (SEM) with Smart PLS 4 to determine the effect of implementing financial management practices on the performance of agri-SME. Findings from the empirical study shows that working capital management practices and financing management practices have significant positive influence on both financial and organizational performance of the surveyed agro enterprises, while the accounting, financial reporting practices and capital budgeting management practices have insignificant influence on the performance agri-SMEs performance. Based on the findings, the study recommends that the government and regulatory authorities such as the Small Industries Development Organization (SIDO) must continue to emphasize their policies for improved agri-SME performance and sustainability while directly or indirectly encourage managers (venture owners) to consider working capital and financing practices as core to their financial management strategies.
History of scholarship and learning. The humanities, Social sciences (General)
Z. K. Gadzhiev, E. S. Surzhikova, D. D. Evlagina
et al.
Aim. Polymorphism is the result of evolutionary processes and is inherited, associated with biodiversity and modified by natural selection and functions to preserve various forms of population. It is economically advantageous to find genes that can be used in breeding in such a way as to increase the fertility of animals in different natural geographical zones, since the adaptive abilities of organisms are complicated. In this regard, the purpose of this study was to study the genetic potential of sheep of different breeds bred in various ecological and geographical regions of the North Caucasus with pasture-pasture and pasture-stall conditions of sheep breeding.Material and Methods. Genotyping was undertaken of sheep of different breeds contained in various natural and climatic zones of the Republic of Dagestan and the most arid region of Stavropol Territory in the zone of risky agriculture where the climate is sharply continental. Genotyping in the loci of the genes of differential growth factor (GDF9) and bone morphogenetic protein (BMP15) was carried out by PCR-PDRF (polymorphism of the lengths of restriction fragments) by cutting DNA using endonuclease restriction and further analysis of the size of the resulting PCR fragments. The genetic structure of genes has been studied by methods of genetic and statistical analysis of the data obtained.Conclusion. The genetic structure of sheep populations of different breeds bred in the foothills of the Republic of Dagestan and the arid region of Stavropol Territory was studied for the first time. Information obtained on the role of the degree of genetic variability of sheep of different breeds provides data which can contribute to the ecological well-being of herds and the status characterising the signs of multiple birth rates is also determined. This will further improve and significantly accelerate breeding work with livestock, since an important task of further increasing the efficiency of the industry is the reproduction of the herd together with a simultaneous increase in the productivity of animals.
In agricultural image analysis, optimal model performance is keenly pursued for better fulfilling visual recognition tasks (e.g., image classification, segmentation, object detection and localization), in the presence of challenges with biological variability and unstructured environments. Large-scale, balanced and ground-truthed image datasets, however, are often difficult to obtain to fuel the development of advanced, high-performance models. As artificial intelligence through deep learning is impacting analysis and modeling of agricultural images, data augmentation plays a crucial role in boosting model performance while reducing manual efforts for data preparation, by algorithmically expanding training datasets. Beyond traditional data augmentation techniques, generative adversarial network (GAN) invented in 2014 in the computer vision community, provides a suite of novel approaches that can learn good data representations and generate highly realistic samples. Since 2017, there has been a growth of research into GANs for image augmentation or synthesis in agriculture for improved model performance. This paper presents an overview of the evolution of GAN architectures followed by a systematic review of their application to agriculture (https://github.com/Derekabc/GANs-Agriculture), involving various vision tasks for plant health, weeds, fruits, aquaculture, animal farming, plant phenotyping as well as postharvest detection of fruit defects. Challenges and opportunities of GANs are discussed for future research.