Precision plant protection

U radu je prikazana suvremena poljoprivredna tehnika u zaštiti bilja. Primjenom navigacijskih sustava sa korekcijskim signalom visoke točnosti (RTK) te upotrebom senzora OptRx omogućena je precizna aplikacija kemijskih sredstava putem izrađene karte zaštite bilja. Korištenjem telematskih sustava omogućuje se rukovateljima strojeva mogućnost grupnog rada na istoj proizvodnoj površini te uvid u stanje usjeva dostupno u realnom vremenu. Razvojem tehnologije slanja informacija o stanju proizvodne površine omogućeno je isto slati u virtualni oblak (cloud) gdje se one mogu analizirati ručno ili korištenjem strojnog učenja (machine learning).The paper presents modern agricultural techniques in plant protection. The application of navigation systems with high-precision correction signal (RTK) from the manufacturer AgLeader and the use of OptRx sensors enabled the precise application of chemical agents through a prepared plant protection map. The use of telematics systems enables machine operators to work in groups on the same production area and with accurate information on the condition of the crop available at any time. With the development of technology for sending information about the state of the production area, it is possible to send the same to a virtual cloud where it can be analyzed manually or using machine learning

Towards Automated Weed Detection Through Two-Stage Semantic Segmentation of Tobacco and Weed Pixels in Aerial Imagery

In precision farming, weed detection is required for precise weedicide application, and the detection of tobacco crops is necessary for pesticide application on tobacco leaves. Automated accurate detection of tobacco and weeds through aerial visual cues holds promise. Precise weed detection in crop field imagery can be treated as a semantic segmentation problem. Many image processing, classical machine learning, and deep learning-based approaches have been devised in the past, out of which deep learning-based techniques promise better accuracies for semantic segmentation, i.e., pixel-level classification. We present a new method that improves the precision of pixel-level inter-class classification of the crop and the weed pixels. The technique applies semantic segmentation in two stages. In stage I, a binary pixel-level classifier is developed to segment background and vegetation. In stage II, a three-class pixel-level classifier is designed to classify background, weeds, and tobacco. The output of the first stage is the input of the second stage. To test our designed classifier, a new tobacco crop aerial dataset was captured and manually labeled pixel-wise. The two-stage semantic segmentation architecture has shown better tobacco and weeds pixel-level classification precision. The intersection over union (IOU) for the tobacco crop was improved from 0.67 to 0.85, and IOU for weeds enhanced from 0.76 to 0.91 with the new approach compared to the traditional one-stage semantic segmentation application. We observe that in stage I shallower, a smaller semantic segmentation model is enough compared to stage II, where a segmentation network with more neurons serves the purpose of good detection

Secured Framework for Smart Farming in Hydroponics with Intelligent and Precise Management based on IoT with Blockchain Technology

Hydroponics is a type of soil-free farming that uses less water and other resources than conventional soil-based farming methods. However, due to the simultaneous supervision of multiple factors, nutrition advice, and plant diagnosis system, monitoring hydroponics farming is a difficult task. Hydroponic techniques utilizing the IoT show to deliver the finest outcomes, despite the usage of various artificial culture methods. Though, the usage of smart communication technologies and IoT exposes environments for smart farming to a wide range of cybersecurity risks and weaknesses. However, the adoption of intelligence-based controlling algorithms in the agricultural industry is a good use of current technical advancements to address these issues. This paper presented a secured framework for smart farming in hydroponics system. The proposed architecture is characterized into four-layer IoT based framework, sensor, communication, fog and cloud layer. Data analytics is performed using supervised machine learning techniques with intelligent and precise management and is applied at the fog layer for efficient computation over the cloud layer. The data security over channel is protected by using Blockchain Technology. The experimental results are evaluated and analyzed for several statistical parameters in order to improve the system efficacy

The Internet of Things Research in Agriculture: A Bibliometric Analysis

An in-depth analysis of Internet of Things (IoT) applications expertise for agriculture. This article's primary purpose is to provide a comprehensive and organized review of IoT research in agriculture themes. Although recent research has offered some pertinent information regarding the analysis of IoT applications in agriculture, further information is needed. Bibliometric analysis is utilized to objectively investigate, and develop information knowledge of IoT applications in agriculture. The papers investigated and examined the themes of IoT-agriculture by analyzing the co-occurrence keywords. The analysis began by picking 550 papers from the Scopus database that were published from 2003 to May 2023. The results show that IoT agriculture papers have grown rapidly since 2015 until now. The three journals that published the most IoT agricultural publications are Sensors (Switzerland), Computers and Electronics in Agriculture, and IEEE Access. Based on the co-occurrence keywords, the focus of IoT paper in agriculture is wireless sensors network (WSN) and radio frequency identification (RFID) for agricultural monitoring, smart agriculture with IoT blockchain and machine learning, IoT greenhouses with cloud computing and artificial intelligence (AI), components of IoT agriculture, precision agriculture with low power low range (LoRa) communication network and IoT cloud platform, smart farming with sensor networks and automation. The study provided an understanding of themes of the IoT agriculture that has been carried out and its future growth. The future of IoT application will elaborate a system efficient, consumes less energy, and emits less carbon dioxide. It has begun by combining IoT-agriculture with the technology edge-fog-cloud layer

Segmentación de imágenes agrícolas adquiridas con drone mediante algoritmos paralelos

The images obtained with drones from a vertical perspective present important information on crops, which makes it possible to systematize various activities related to precision agriculture (AP). Sequential algorithms developed in traditional programming languages ​​consume high time and hardware resources in processing large digital images. A parallel algorithm will be developed to segment images using the OpenMP library to reduce computation times. OpenMP is a library compatible with low-level programming languages ​​that allow the implementation of parallel algorithms in C or C++ languages ​​in Linux environments for multicore architectures. The sequential algorithm to implement it through parallelism was necessary to divide into several smaller tasks and run them on several cores available on multicore processors to improve processing speed. The metrics used (execution time, acceleration, efficiency, and computational cost) allowed evaluating of the algorithms' performance with images of different dimensions, obtaining favorable results that verify the improvement of the parallel algorithm. The parallelization of sequential algorithms shows a significant reduction in execution times (66.37%) with large images and (74.73%) with small images using the maximum number of cores (8)Las imágenes obtenidas con drones desde perspectiva vertical presentan información importante de los cultivos agrícolas, lo que permite sistematizar diversas actividades relacionadas con la agricultura de precisión (AP). Los algoritmos secuenciales desarrollados en lenguajes de programación tradicionales consumen tiempos y recursos de hardware altos en el procesamiento de imágenes digitales de grandes tamaños. Para reducir los tiempos de cómputo se desarrolló un algoritmo paralelo para segmentar imágenes utilizando la librería OpenMP. OpenMP es una librería compatible con lenguajes de programación de bajo nivel que permiten la implementación de algoritmos paralelos en lenguajes C o C++ en entornos Linux para arquitecturas multicore. El algoritmo secuencial para implementarlo mediante paralelismo fue necesario dividir en varias tareas más pequeñas y ejecutarlas en varios núcleos disponibles en procesadores multicore para mejorar la velocidad de procesamiento. Las métricas utilizadas (tiempo de ejecución, aceleración, eficiencia y costo computacional) permitieron evaluar el rendimiento de los algoritmos con imágenes de diferentes dimensiones obteniendo resultados favorables que verifica la mejora del algoritmo paralelo. La paralelización de algoritmos secuenciales muestra un importante reducción de los tiempos de ejecución (66.37%) con imágenes grandes y (74.73% ) con imágenes pequeñas utilizando el número máximo de núcleos (8

Detecting Volunteer Cotton Plants in a Corn Field with Deep Learning on UAV Remote-Sensing Imagery

The cotton boll weevil, Anthonomus grandis Boheman is a serious pest to the U.S. cotton industry that has cost more than 16 billion USD in damages since it entered the United States from Mexico in the late 1800s. This pest has been nearly eradicated; however, southern part of Texas still faces this issue and is always prone to the pest reinfestation each year due to its sub-tropical climate where cotton plants can grow year-round. Volunteer cotton (VC) plants growing in the fields of inter-seasonal crops, like corn, can serve as hosts to these pests once they reach pin-head square stage (5-6 leaf stage) and therefore need to be detected, located, and destroyed or sprayed . In this paper, we present a study to detect VC plants in a corn field using YOLOv3 on three band aerial images collected by unmanned aircraft system (UAS). The two-fold objectives of this paper were : (i) to determine whether YOLOv3 can be used for VC detection in a corn field using RGB (red, green, and blue) aerial images collected by UAS and (ii) to investigate the behavior of YOLOv3 on images at three different scales (320 x 320, S1; 416 x 416, S2; and 512 x 512, S3 pixels) based on average precision (AP), mean average precision (mAP) and F1-score at 95% confidence level. No significant differences existed for mAP among the three scales, while a significant difference was found for AP between S1 and S3 (p = 0.04) and S2 and S3 (p = 0.02). A significant difference was also found for F1-score between S2 and S3 (p = 0.02). The lack of significant differences of mAP at all the three scales indicated that the trained YOLOv3 model can be used on a computer vision-based remotely piloted aerial application system (RPAAS) for VC detection and spray application in near real-time.Comment: 38 Page

Drone and sensor technology for sustainable weed management: a review

Weeds are amongst the most impacting abiotic factors in agriculture, causing important yield loss worldwide. Integrated Weed Management coupled with the use of Unmanned Aerial Vehicles (drones), allows for Site-Specific Weed Management, which is a highly efficient methodology as well as beneficial to the environment. The identification of weed patches in a cultivated field can be achieved by combining image acquisition by drones and further processing by machine learning techniques. Specific algorithms can be trained to manage weeds removal by Autonomous Weeding Robot systems via herbicide spray or mechanical procedures. However, scientific and technical understanding of the specific goals and available technology is necessary to rapidly advance in this field. In this review, we provide an overview of precision weed control with a focus on the potential and practical use of the most advanced sensors available in the market. Much effort is needed to fully understand weed population dynamics and their competition with crops so as to implement this approach in real agricultural contexts

Adoption of artificial intelligence based technologies in sub-saharan african agriculture

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceSub-Saharan Africa (SSA) is currently facing numerous agriculture related challenges such as climate change, lacking infrastructure, and limited institutional as well as economic support. However, current research does not provide holistic solutions to this problem. This study aims to shed light on this topic through the development of a model that can be used to assess the solution potential as well as high-level implementation requirements of selected artificial intelligence (AI) based agriculture technologies in the context of SSA. To thoroughly develop the above-mentioned model a design science approach was followed. First an in depth (systematic) literature review was conducted where the agriculture related challenges in SSA and state-of-the-art AI-based agriculture technologies are detailed. This step was followed by the creation of a model that aims to find a nexus between the researched challenges and available technologies as potential solutions. Furthermore, the framework outlines context specific technology adoption requirements. Lastly, expert interviews were conducted to validate and revise the proposed model. The final framework clearly highlights the positive impact AI based technologies can have in SSA’s agriculture and the basic conditions that need to be met to successfully implement them

Assessing efficiency differences in a common Agriculture Decision Support System - A comparative analysis between Greek and Italian durum wheat farms -

This study assesses inputs use efficiency of durum wheat farmers, subscribed under a common Agricultural Decision Support System (ADSS), especially designed by Barilla and HORTA for this cultivation. Data Envelopment Analysis was the main analysis used to highlight differences in the implementation stage of ADSS’s suggestions, between 4 agricultural firms (2 Italian and 2 Greek) (N= 563 farmers). By incorporating economic (variable costs) and environmental factors (Carbon, Water and Environmental footprints), performance differences between farms both on regional and national level arose. Lastly, closer monitoring for clarifying the reasoning of the obtained differences in the implementation stage is proposed

Spatial Modelling of Within-Field Weed Populations - a Review

Concerns around herbicide resistance, human risk, and the environmental impacts of current weed control strategies have led to an increasing demand for alternative weed management methods. Many new weed management strategies are under development; however, the poor availability of accurate weed maps, and a lack of confidence in the outcomes of alternative weed management strategies, has hindered their adoption. Developments in field sampling and processing, combined with spatial modelling, can support the implementation and assessment of new and more integrated weed management strategies. Our review focuses on the biological and mathematical aspects of assembling within-field weed models. We describe both static and spatio-temporal models of within-field weed distributions (including both cellular automata (CA) and non-CA models), discussing issues surrounding the spatial processes of weed dispersal and competition and the environmental and anthropogenic processes that affect weed spatial and spatio-temporal distributions. We also examine issues surrounding model uncertainty. By reviewing the current state-of-the-art in both static and temporally dynamic weed spatial modelling we highlight some of the strengths and weaknesses of current techniques, together with current and emerging areas of interest for the application of spatial models, including targeted weed treatments, economic analysis, herbicide resistance and integrated weed management, the dispersal of biocontrol agents, and invasive weed species