Yield Prognosis for the Agrarian Management of Vineyards using Deep Learning for Object Counting

In various applications, the counting of objects based on image data plays a pivotal role. In this paper we first conducted a literature review to display the state of the art in counting objects and summarized the results by extracting several important concepts that describe the counting problem as well as the solution. In a second step we applied this knowledge to yield prognosis in vineyards, where we used Deep Learning models to detect the objects. While these methods used in the detection step are state of the art and perform very well, several problems are usually introduced by the constraint of only counting an object once in the counting step. We provide a solution for this common problem by identifying unique objects and tracking them throughout a sequence of images in order to avoid counting objects more than once, resulting in an automated yield prognosis model for vineyards

Sky-Farmers: Applications of Unmanned Aerial Vehicles (UAV) in Agriculture

Unmanned aerial vehicles (UAVs) are unpiloted flying robots. The term UAVs broadly encompasses drones, micro-, and nanoair/aerial vehicles. UAVs are largely made up of a main control unit, mounted with one or more fans or propulsion system to lift and push them through the air. Though initially developed and used by the military, UAVs are now used in surveillance, disaster management, firefighting, border-patrol, and courier services. In this chapter, applications of UAVs in agriculture are of particular interest with major focus on their uses in livestock and crop farming. This chapter discusses the different types of UAVs, their application in pest control, crop irrigation, health monitoring, animal mustering, geo-fencing, and other agriculture-related activities. Beyond applications, the advantages and potential benefits of UAVs in agriculture are also presented alongside discussions on business-related challenges and other open challenges that hinder the wide-spread adaptation of UAVs in agriculture

Using a GIS technology to plan an agroforestry sustainable system in Sardinia

This study was conducted with the aim to quantify the spread of livestock agroforestry in a Mediterranean ecosystem (island of Sardinia, Italy) and evaluate its sustainability in terms of grazing impact. By using GIS software ArcMap 10.2.2, the map of Sardinia vegetal landscape, obtained by information of Sardinia nature map based on the classification of habitat according to CORINE-Biotopes system, have been overplayed with the map of livestock grazing impact map CAIA developed by INTREGA (spin-off ENEA), to obtain for Meriagos (local agro-silvo-pastoral systems; classified “Dehesa 84.6” according to CORINE-Biotopes system), bushlands and woodlands, the surfaces under grazing and evaluate the extension of overgrazing for each of them

Autonomous UAS-Based Agriculture Applications: General Overview and Relevant European Case Studies

Emerging precision agriculture techniques rely on the frequent collection of high-quality data which can be acquired efficiently by unmanned aerial systems (UAS). The main obstacle for wider adoption of this technology is related to UAS operational costs. The path forward requires a high degree of autonomy and integration of the UAS and other cyber physical systems on the farm into a common Farm Management System (FMS) to facilitate the use of big data and artificial intelligence (AI) techniques for decision support. Such a solution has been implemented in the EU project AFarCloud (Aggregated Farming in the Cloud). The regulation of UAS operations is another important factor that impacts the adoption rate of agricultural UAS. An analysis of the new European UAS regulations relevant for autonomous operation is included. Autonomous UAS operation through the AFarCloud FMS solution has been demonstrated at several test farms in multiple European countries. Novel applications have been developed, such as the retrieval of data from remote field sensors using UAS and in situ measurements using dedicated UAS payloads designed for physical contact with the environment. The main findings include that (1) autonomous UAS operation in the agricultural sector is feasible once the regulations allow this; (2) the UAS should be integrated with the FMS and include autonomous data processing and charging functionality to offer a practical solution; and (3) several applications beyond just asset monitoring are relevant for the UAS and will help to justify the cost of this equipment.publishedVersio

Survey of Impact of Technology on Effective Implementation of Precision Farming in India

The advancements in technology have made its impact on almost every field. India being an agricultural country, proper use of technology can greatly help in improving the standard of living of the farmers. With varying weather conditions, illiteracy of farmers and non-availability of timely assistance, the farmers of this country could not get the best out of their efforts. Precision farming focuses mainly on the aspects that can improve the efficiency based on the data collected from various sources viz. meteorology, sensors, GIS, GPS, etc. The information pertaining to farmland (e.g., soil moisture, soil pH, soil nitrogen) and agro-meteorology (e.g., temperature & humidity, solar radiation, wind speed, atmospheric CO2 concentration, rainfall, climate change and global warming) are used as input parameters to decide the varying requirements of the crop cultivation. Historical farm land data are used as a means to decide on the kind of actions to be taken under a specific scenario. This paper surveys the existing methods of precision farming and highlights the impact of technology in farming. An overview of different technologies used in precision farming around the world and their implications on the yield are discussed. The methods adopted towards managing different types of crops, the varying environmental conditions and the use of realtime data being collected through sensors are also analyzed. Also, the need for dynamic approaches to assist the farmers in taking context specific decisions has been highlighted

Growing Kiwiberries in New England: A Guide for Regional Producers

The kiwiberry (Actinidia arguta) has an extensive 140-year history of being grown in New England as an ornamental vine but has only recently been adopted as a commercial fruit crop. As regional and international acreage in commercial production continues to increase, the need for a comprehensive production guides and regionally-specific enterprise analyses has become evident. This thesis brings together the most recent findings of the Kiwiberry Development Program at the New Hampshire Agricultural Experiment Station, along with available commercial kiwiberry production information, to address this need. Specifically, this guide presents an overview of the species, current best production practices, regionally-relevant market information, and an enterprise analysis for this emerging fruit crop in the northeastern US

UAVino

UAVino is a drone solution that uses aerial imagery to determine the overall plant health and water content of vineyards. In general, the system focuses on automating crop inspection by taking aerial imagery of a vineyard, conducting post-processing, and outputting an easily interpreted map of the vineyard\u27s overall health. The project\u27s key innovation is an auto-docking system that allows the drone to automatically return to its launch point and recharge in order to extend mission duration. Long term, UAVino is envisioned as a multi-year, interdisciplinary project involving both the Santa Clara University Robotics Systems Laboratory and local wineries in order to develop a fully functional drone agricultural inspection service

Unmanned Ground Vehicles for Smart Farms

Forecasts of world population increases in the coming decades demand new production processes that are more efficient, safer, and less destructive to the environment. Industries are working to fulfill this mission by developing the smart factory concept. The agriculture world should follow industry leadership and develop approaches to implement the smart farm concept. One of the most vital elements that must be configured to meet the requirements of the new smart farms is the unmanned ground vehicles (UGV). Thus, this chapter focuses on the characteristics that the UGVs must have to function efficiently in this type of future farm. Two main approaches are discussed: automating conventional vehicles and developing specifically designed mobile platforms. The latter includes both wheeled and wheel-legged robots and an analysis of their adaptability to terrain and crops

Landings, vol. 29, no. 1

Landings content emphasizes science, history, resource sustainability, economic development, and human interest stories related to Maine’s lobster industry. The newsletter emphasizes lobstering as a traditional, majority-European American lifeway with an economic and social heritage unique to the coast of Maine. The publication focuses how ongoing research to engage in sustainable, non-harmful, and non-wasteful commercial fishing practices benefit both the fishery and Maine\u27s coastal legacy. Maine Lobstermen’s Community Alliance (MLCA) started publication of Landings, a 24-page newsletter in January 2013 as the successor of the Maine Lobstermen’s Association (MLA) Newsletter. As of 2022, the MLCA published over 6,500 copies of the monthly newsletter for distribution by mail to all of Maine’s commercial lobstermen, Maine state government agency staff, Maine Legislators, members of Maine\u27s U.S. Congressional delegation, subscribers, and marine businesses. For more information, please visit the Maine Lobstermen’s Community Alliance (MLCA) website. Headlines in this issue include: Governor’s Offshore Wind Plan Frustrates Fishermen Massachusetts Offshore Wind Project Screeches to a Halt Reflections of 2020 in the rear view mirror 2020 – the year of challenges Fishermen’s Forum Will Still Offer Student Scholarships in 2021 Maine Lobstermen’s Association Update Draft Whale Rules and Biological Opinion Backlog at Federal Register May Slow New Rules State Waters Seasonal Closure Proposed in Massachusetts Three Right Whale Calves Seen Thus Far Offshore Lobster Enforcement Turning to ROVS Lobster Electronic Tracking Pilot Program Offshore Wind Projects Fall Under Jones Act Provisions Funds Help Maine Transition to 100% Harvester Reporting DMR Aquaculture Lease Application Status for January (as of 12/20/20) Good-bye 2020: An End of the Year Wrap-up from DMR DMR Struggling with Aquaculture Application Backlog Hand and wrist injuries can be avoided Groups Petition Department of Interior for New Closures MSC Says Canada Lobster Fishing Poses Low Risk to Right Whales Lower Catch Estimate Projected for 2021 Scallop Season Massachusetts Lobstermen Facing New Rules Federal Stimulus Bill Provides Additional Funds for Fisheries Sector DMR Joins White Shark Research Effort Vital Gulf Phytoplankton Survey Resumes DMR Begins Maine Seafood Promotion Campaign Stonington Lobsterman’s Tales of Hard Work, Communit

Continuous Plant-Based and Remote Sensing for Determination of Fruit Tree Water Status

Climate change poses significant challenges to agricultural productivity, making the efficient management of water resources essential for sustainable crop production. The assessment of plant water status is crucial for understanding plant physiological responses to water stress and optimizing water management practices in agriculture. Proximal and remote sensing techniques have emerged as powerful tools for the non-destructive, efficient, and spatially extensive monitoring of plant water status. This review aims to examine the recent advancements in proximal and remote sensing methodologies utilized for assessing the water status, consumption, and irrigation needs of fruit tree crops. Several proximal sensing tools have proved useful in the continuous estimation of tree water status but have strong limitations in terms of spatial variability. On the contrary, remote sensing technologies, although less precise in terms of water status estimates, can easily cover from medium to large areas with drone or satellite images. The integration of proximal and remote sensing would definitely improve plant water status assessment, resulting in higher accuracy by integrating temporal and spatial scales. This paper consists of three parts: the first part covers current plant-based proximal sensing tools, the second part covers remote sensing techniques, and the third part includes an update on the on the combined use of the two methodologies