Review on Application of Drone in Spraying Pesticides and Fertilizers

In today's agriculture, there are far too many innovations involved. One of the emerging technologies is pesticide spraying using drones. Manual pesticide spraying has a number of negative consequences for the people who are involved in the spraying operation. The result of exposure symptoms can include minor skin inflammation and birth abnormalities, tumors, genetic modifications, nerve and blood diseases, endocrinal interference, coma or death. However, Drone can be used to automate fertilizer application, pesticide spraying, and field tracking. This paper provides a concise overview of the use of drones for field inspection and pesticide spraying. displays different methodologies and controllers of agriculture drone and explains some essential Drone Hardware, Software elements and application

Review on Automatic Variable-Rate Spraying Systems Based on Orchard Canopy Characterization

Pesticide consumption and environmental pollution in orchards can be greatly decreased by combining variable-rate spray treatments with proportional control systems. Nowadays, farmers can use variable-rate canopy spraying to apply weed killers only where they are required which provides environmental friendly and cost-effective crop protection chemicals. Moreover, restricting the use of pesticides as Plant Protection Products (PPP) while maintaining appropriate canopy deposition is a serious challenge. Additionally, automatic sprayers that adjust their application rates to the size and shape of orchard plantations has indicated a significant potential for reducing the use of pesticides. For the automatic spraying, the existing research used an Artificial Intelligence and Machine Learning. Also, spraying efficiency can be increased by lowering spray losses from ground deposition and off-target drift. Therefore, this study involves a thorough examination of the existing variable-rate spraying techniques in orchards. In addition to providing examples of their predictions and briefly addressing the influences on spraying parameters, it also presents various alternatives to avoiding pesticide overuse and explores their advantages and disadvantages

Assessing the advancement of artificial intelligence and drones’ integration in agriculture through a bibliometric study

Integrating artificial intelligence (AI) with drones has emerged as a promising paradigm for advancing agriculture. This bibliometric analysis investigates the current state of research in this transformative domain by comprehensively reviewing 234 pertinent articles from Scopus and Web of Science databases. The problem involves harnessing AI-driven drones' potential to address agricultural challenges effectively. To address this, we conducted a bibliometric review, looking at critical components, such as prominent journals, co-authorship patterns across countries, highly cited articles, and the co-citation network of keywords. Our findings underscore a growing interest in using AI-integrated drones to revolutionize various agricultural practices. Noteworthy applications include crop monitoring, precision agriculture, and environmental sensing, indicative of the field’s transformative capacity. This pioneering bibliometric study presents a comprehensive synthesis of the dynamic research landscape, signifying the first extensive exploration of AI and drones in agriculture. The identified knowledge gaps point to future research opportunities, fostering the adoption and implementation of these technologies for sustainable farming practices and resource optimization. Our analysis provides essential insights for researchers and practitioners, laying the groundwork for steering agricultural advancements toward an enhanced efficiency and innovation era

Usos de la Ciencia de Datos aplicados al sector agrícola

[ES] La ciencia de datos es una disciplina que intenta obtener conocimiento nuevo a partir de grandes cantidades de datos. Esta disciplina cuenta con innumerables aplicaciones en los sectores económicos secundario y terciario, siendo especialmente significativo su papel como herramienta en procesos de producción o ciencias de la salud. También puede ejercer un papel importante en el sector primario, donde puede participar de forma activa en el proceso de toma de decisiones. En este trabajo se estudia la viabilidad del uso de la ciencia de datos en el sector agrícola describiendo varias aplicaciones y realizando algunos experimentos para ejemplificar esta relación, haciendo hincapié en sus usos en tareas como: el análisis de viabilidad y productividad de un producto, control de salud de cultivos, control de plagas y control de calidad.[EN] Data science is a discipline based in obtaining new knowledge from large amounts of data. Data science has plenty of uses in secondary and tertiary economic sectors being specially important its role as a tool in manufacturing or health sciences. But data science can also play an important role in the primary sector participating actively in decisionmaking processes. In this paper the usability of data science in the agricultural sector is studied, describing some applications and making experiments to illustrate this relationship, emphasizing its uses in: analysis of viability and productivity of a product, crop health control, pest control and quality control.Mompó Serrano, A. (2021). Usos de la Ciencia de Datos aplicados al sector agrícola. Universitat Politècnica de València. http://hdl.handle.net/10251/179224TFG

Design of Plant Protection UAV Variable Spray System Based on Neural Networks

Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Based on existing variable spray research, a plant protection UAV variable spray system integrating neural network based decision making is designed. Using the existing data on plant protection UAV operations, combined with artificial neural network (ANN) technology, an error back propagation (BP) neural network model between the factors affecting droplet deposition is trained. The factors affecting droplet deposition include ambient temperature, ambient humidity, wind speed, flight speed, flight altitude, propeller pitch, nozzles pitch and prescription value. Subsequently, the BP neural network model is combined with variable rate spray control for plant protection UAVs, and real-time information is collected by multi-sensor. The deposition rate is determined by the neural network model, and the flow rate of the spray system is regulated according to the predicted deposition amount. The amount of droplet deposition can meet the prescription requirement. The results show that the training variance of the ANN is 0.003, and thus, the model is stable and reliable. The outdoor tests show that the error between the predicted droplet deposition and actual droplet deposition is less than 20%. The ratio of droplet deposition to prescription value in each unit is approximately equal, and a variable spray operation under different conditions is realized

Development of a Recognition System for Spraying Areas from Unmanned Aerial Vehicles Using a Machine Learning Approach

Unmanned aerial vehicle (UAV)-based spraying systems have recently become important for the precision application of pesticides, using machine learning approaches. Therefore, the objective of this research was to develop a machine learning system that has the advantages of high computational speed and good accuracy for recognizing spray and non-spray areas for UAV-based sprayers. A machine learning system was developed by using the mutual subspace method (MSM) for images collected from a UAV. Two target lands: agricultural croplands and orchard areas, were considered in building two classifiers for distinguishing spray and non-spray areas. The field experiments were conducted in target areas to train and test the system by using a commercial UAV (DJI Phantom 3 Pro) with an onboard 4K camera. The images were collected from low (5 m) and high (15 m) altitudes for croplands and orchards, respectively. The recognition system was divided into offline and online systems. In the offline recognition system, 74.4% accuracy was obtained for the classifiers in recognizing spray and non-spray areas for croplands. In the case of orchards, the average classifier recognition accuracy of spray and non-spray areas was 77%. On the other hand, the online recognition system performance had an average accuracy of 65.1% for croplands, and 75.1% for orchards. The computational time for the online recognition system was minimal, with an average of 0.0031 s for classifier recognition. The developed machine learning system had an average recognition accuracy of 70%, which can be implemented in an autonomous UAV spray system for recognizing spray and non-spray areas for real-time applications