A mathematical simulation model for the multi-annual assessment of Integrated Weed Management strategies is presented. The model allows the simulation of the competitive interaction between annual weeds and crops. For weed development, the following processes are represented: (i) demographic dynamics on a daily basis considering the numeric composition of the different phenological states, (ii) intra

Aboveground plant efficiency has improved significantly in recent years, and the improvement has led to a steady increase in global food production. The improvement of belowground plant efficiency has potential to further increase food production. However, belowground plant roots are harder to study, due to inherent challenges presented by root phenotyping. Several tools for identifying root anatomical

Timely and accurate monitoring of crop patterns in smallholder agricultural areas is essential for guiding local crop yield estimates, agricultural subsidy allocations, and food security policy formulation. The household-operated land management model leads to a highly fragmented and heterogeneous agricultural landscape; therefore, fine crop mapping in smallholder agricultural areas remains challenging

Heat stress in domestic animal production consistently alters several physiological, growth, and reproductive metrics within the livestock industry. To increase commercial productivity and general animal husbandry, the agricultural research community has been investigating technologies to combat the effects of heat stress for several decades. A hog cooling pad has been developed at Purdue University

Mechanized coffee harvesting is performed by machines that use the principle of mechanical vibration to detach the fruit. In this type of harvest, the machines transmit kinetic energy to the plant. Numerical simulations attached to representative models have been shown to be a viable tool for understanding the coffee harvesting process. In this sense, this study aimed to evaluate the dynamic behavior

The supervised deep learning models rely on large labeled training samples, which is a common challenge affecting current plant phenotyping studies. One practical approach to alleviate the insufficient training samples is data augmentation. In this study, we investigated the data augmentation approach using improved cDCGAN (conditional deep convolutional generative adversarial network) for vigor rating

Bovine Ephemeral Fever affects livestock across Africa, Asia, and Australia with symptoms of fever, lameness, and inappetence. Currently, cattle producers must visually observe symptoms to detect Bovine Ephemeral Fever. The aim of this study is to determine the potential for accelerometer sensors deployed on the animal to identify when cattle are becoming ill with Bovine Ephemeral Fever. Two of 8 heifers

Over the past ten years, plant phenotyping technologies that utilize sensing and data mining approaches to estimate crop traits in a high-throughput and objective manner, have been evaluated and applied in different crop improvement and breeding programs. Multiple platforms, from proximal to unmanned aerial systems based remote sensing, have been developed and applied to increase the throughput, efficiency

Moving and operating autonomously in a field or orchard environment is challenging for agricultural robots due to the complex task requirements and highly unstructured conditions. The human–computer interaction-based remote control can provide the robots with an alternative solution to assist decision making and motion planning. In this study, a virtual reality (VR) and Kinect-based immersive teleoperation

Weeds are normally sprayed uniformly across field irrespective of their density in the field. Automatic methods can be developed that help us to locate and spray weed according to its density. Automatic weed detection is necessary for precision spraying that can improve rice yield and reduce production cost. This aids farmers to get an idea of local weed coverage and spray only the weed infested areas

Nowadays, several studies in the field of deep learning in agriculture obtain high performances in weeds identification by fine-tuning neural networks, previously trained on general-purpose datasets containing images unrelated to agriculture. This work examines whether these achievements could be further improved by fine-tuning neural networks pre-trained on agricultural datasets instead of ImageNet

Crop maps produced by remote sensing data play an important role in agricultural crop studies. Most of the crop mapping methods rely on field samples to train a model, which is a costly and time-consuming process. On the other hand, automatic crop mapping methods which are based on unique spectral characteristics are independent of ground truth data. Since most crops have specific spectral and temporal

Farm managements and system designs could be improved based on the responses of broiler feeding and drinking behaviors. The objective of this study was to develop and validate image processing algorithms for automatic monitoring of feeding and drinking behaviors of group-reared broilers. Sixty Ross® 708 broilers at 26–28 days of age were kept in a 2.9 m × 1.4 m pen with a tube feeder and five nipple

Fusarium head blight (FHB), caused by the fungus Gibberella zeae, infects spikelets on wheat heads and can cause significant yield and quality losses in wheat. Application of hyperspectral imaging on the detection of FHB was evaluated in the current study. Hyperspectral images were acquired from a total of 1,680 Fusarium-infected wheat head samples over a wavelength range of 400–1000 nm. The principal

The objective of this paper is to provide recognition for Pantaneira cattle breed using Convolutional Neural Networks (CNN). Fifty-one animals from the Aquidauana Pantaneira cattle Center (NUBOPAN) were studied. The center is located in the Midwest region of Brazil. Four monitoring cameras were distributed in the fences and took 27,849 images of Pantaneira cattle breed using different angles and positions

Accurate and up-to-date spatial agricultural information is essential for applications including agro-environmental assessment, crop management, and appropriate targeting of agricultural technologies. There is growing research interest in spatial analysis of agricultural ecosystems applying satellite remote sensing technologies. However, usability of information generated from many of remotely sensed

Honeybees are one of the highly valued pollinators. Their work as individuals is appreciated for crops pollination and honey production. It is believed that work of an entire bee colony is intense and almost continuous. The goal of the work presented in this paper is identification of bees circadian rhythm with a use of sound-based analysis. In our research as a source of information on bee colony

An important factor in a circular agricultural system is the efficient use of animal manure. Until now, the applied quantity of manure is regulated by law at farm level, based on fixed phosphorus (P) application norms. However, a first step towards more efficient manure application is to better balance P input and output at field level by predicting future P yields. Machine learning techniques can

The efficient and sustainable irrigation mechanism results in reduced water loss and improved crop productivity. This paper presents a real-time fuzzy inference based automated irrigation controller implemented in LabVIEW, that utilizes the data logged from the sensor network and communicated using GSM/GPRS module. The proposed drip irrigation scheduler receives the data from soil moisture, humidity

Having rapid and reliable knowledge of the quality of dairy farm forage is crucial for technicians and producers. However, the necessary equipment to fulfil these requirements is oftentimes expensive and complex to use. This work presents a portable instrument system to analyze the nutritional values of dairy farm forage using near-infrared spectrometry (NIRS) techniques. Using the Internet of Things

The aim of this study was to compare the CPU time of two most widely used Fortran compilers – the Absoft and the Intel, in the context of two programs used in the genomic evaluation system of dairy cattle - a program for re-coding of the raw genotyping data of single nucleotide polymorphisms and a program for the estimation of additive genetic effects of single nucleotide polymorphisms. The data set

The cleaning of rice on a combine harvester is a complex process, which leads to differences in impurity ratio of harvested grain, and the impurity ratio is one of the key criteria for the assessment of performance of a combine harvester. Combine operators usually optimize parameter settings only once for harvesting each crop because of time pressure, and therefore differences in site- and temporal-specific

Articulated vehicles composed of a tractor and several passive trailers are commonly used for transportation purposes in agricultural applications. The increment of the number of trailers increases the payload capacity, but on the other hand, it also implies serious motion constraints, especially in turning scenarios where there is a greater risk of collisions with the crops. In this context, to reduce

Prompt plant disease detection is critical to prevent plagues and to mitigate their effects on crops. The most accurate automatic algorithms for plant disease identification using plant field images are based on deep learning. These methods require the acquisition and annotation of large image datasets, which is frequently technically or economically unfeasible. This study introduces Few-Shot Learning

As providing objects that pigs prefer can reduce the occurrence of tail-biting and aggression and consequently improve animal welfare, automatic recognition of pigs’ engagement with different objects can have practical value. Therefore, aim of this study was to develop a computer vision based approach that utilised a recurrent neural network-based deep learning algorithm to recognise pig enrichment

Intensive livestock production might have a negative environmental impact, by producing large amounts of animal manure, which, if not properly managed, can contaminate nearby water bodies with nutrient excess. However, if animal manure is exported to nearby crop fields, to be used as organic fertilizer, pollution can be mitigated. It is a single-objective optimization problem, in regards to finding

Research and development in mobile robotics are continuously growing. The ability of a human-made machine to navigate safely in a given environment is a challenging task. In agricultural environments, robot navigation can achieve high levels of complexity due to the harsh conditions that they present. Thus, the presence of a reliable map where the robot can localize itself is crucial, and feature extraction

This application note introduces an app for Android smartphones to predict the nitrogen-corrected metabolizable energy (AMEn) values of feedstuffs for broilers by using an artificial neural network model. This application, called AMEn Predictor, has been developed for Android devices. To calculate the AMEn prediction, the user has to insert values of the chemical composition of one concentrate feedstuff

Minirhizotron technology is widely used to study root growth and development. Yet, standard approaches for tracing roots in minirhiztron imagery is extremely tedious and time consuming. Machine learning approaches can help to automate this task. However, lack of enough annotated training data is a major limitation for the application of machine learning methods. Transfer learning is a useful technique

Mass flow estimation is of great importance to several industries, and it can be quite challenging to obtain accurate estimates due to limitation in expense or general infeasibility. In the context of agricultural applications, yield monitoring is a key component to precision agriculture and mass flow is the critical factor to measure. Measuring mass flow allows for field productivity analysis, cost

The accurate estimation of herbage intake is a key to adequately feed grazing ruminants. Ten dairy Sarda sheep fitted with a halter equipped with an accelerometer (BEHARUM device) were allowed to graze micro-swards of Italian ryegrass (Lolium multiflorum L.), alfalfa (Medicago sativa L.), oat (Avena sativa L.), chicory (Cichorium intibus L.) and a mixture (Italian ryegrass and alfalfa) for six minutes

Two transient open source solvers, OpenFOAM and ParMooN, and the commercial solver Ansys Fluent are assessed with respect to the simulation of the turbulent air flow inside and around a dairy barn. For this purpose, data were obtained in an experimental campaign at a 1:100 scaled wind tunnel model. All solvers used different meshes, discretization schemes, and turbulence models. The experimental data

In regions where economies that are highly dependent on agricultural activities, estimation of evapotranspiration is vital for scheduling and managing the water resources. In this study, the estimation of the reference evapotranspiration (ET0) was performed for the west coast of Peninsular Malaysia, where oil palm is the major crop. Three stations, namely Station 48603 (Alor Setar), Station 48620 (Sitiawan)

Features are the vital factor for image classification in the field of machine learning. The advancement of deep convolutional neural network (CNN) shows the way for identification of rice diseases using deep features with the expectation of high returns. This paper introduced 5932 on-field images of four types of rice leaf diseases, namely bacterial blight, blast, brown spot and tungro. In addition

There is an increasing need for small scale information about the temporal shifting of the phenological crop development for applications like fertilization, irrigation, crop and soil protection, weather index insurance, yield prediction, crop classification or as bio-indicator of climate change. This application note introduces PhenoWin, a tool to extract and visualize phenological windows for any

Drought stress is one of the major limiting factors in soybean growth and productivity. Canopy leaf wilting (i.e. fast- and slow-wilting) is considered as an important visible symptom of soybeans under drought conditions. In soybean breeding programs, genotypes with the slow-wilting trait have been identified as drought-tolerant cultivars. Traditional method measures canopy leaf wilting traits using

A comprehensive study of the feasibility of hyperspectral imaging in visible (400–1000 nm) and near infrared (900–1700 nm) regions was investigated for prediction and concentration mapping of Vitamin C, ascorbic acid (AA), dehydroascorbic acid (DHAA) and phenols in wild rocket (Diplotaxis tenuifolia) over a storage span of 12 days at 5 °C. Partial least squares regression (PLSR) with different data

Iron deficiency chlorosis (IDC) is a major yield-limiting factor for soybean production in the mid-western USA. The most practical solution in mitigating losses due to IDC is the development and characterization of IDC tolerant varieties. Leveraging the advanced technique of unmanned aircraft system (UAS) and the thriving deep learning methodology, a convolutional neural network (CNN) could be trained

The arrangement of parcels on a farm strongly affects agricultural profitability and is therefore taken into account when undertaking economic analyses, land reallocation modelling, or when evaluating the effects of land consolidation. The paper proposes an algorithm for the automatic calculation of distances that are good approximations of actual routes for people, harvests, and farm machinery. The

Diverse on-farm agronomic data, gathered via precision agriculture (PA), require depuration and joint analysis of multiple georeferenced field characteristics such as crop yields, and soil and topographic aspects. Multivariate analysis of spatial variability has been recommended to understand variation of data within fields and classify sites into zones of broad similarity to support crop management

Selectively picking a target fruit surrounded by obstacles is one of the major challenges for fruit harvesting robots. Different from traditional obstacle avoidance methods, this paper presents an active obstacle separation strategy that combines push and drag motions based on 3D visual perception to separate obstacles from the target. We define a region of interest 3D point cloud with a number of

Body measurement is an important technique to acquire the livestock body parameters for precision agriculture and effective management of livestock. Traditionally, the livestock body parameters are often manually measured, which suffers from several drawbacks, including unstable accuracy, low efficiency, difficulty in measuring complex parameters, and potentially negative influence on animal welfare

Precision agriculture (PA) constitutes one of the most critical sectors of remote sensing applications that allow obtaining spatial segmentation and within-field variability information from field crops. In the last decade, an increasing source of information is provided by unmanned aerial vehicle (UAVs) platforms, mainly equipped with optical multispectral cameras, to map, monitor, and analyze, temporal

Hops are a key ingredient for beer brewing due to their role in preservation, the creation of foam characteristics, the bitterness and aroma of the beers. Drying significantly impacts on the composition of hops which directly affects the brewing quality of beers. Therefore, it is pivotal to understand the changes during the drying process to optimize the process with the central aim of improving product

Heat and light-instigated abiotic stresses during summer can cause several physiological disorders in perennial specialty crops. Such stressors increase the fruit surface temperature (FST) and prolonged exposure above a critical FST can result in sunburn. Sunburn in apple may cause considerable crop loss and reduce fresh produce marketability. Existing approaches for sunburn prediction and management

In agriculture, many types of weeds have a harmful impact on agricultural productivity. Recognizing weeds and understanding the threat they pose to farmlands is a significant challenge because many weeds are quite similar in their external structure, making it difficult to classify them. A weeds classification approach with high accuracy and quick processing should be incorporated into automatic devices

The research presented in this paper was conducted as part of a project that aimed at using biosensors on sentinel oysters to provide a biological perspective of environmental changes. The physiological response patterns (e.g. heart rate variability, shell gape) of sentinel oysters can provide valuable insight into their exposure to environmental stressors. Sensors were attached to measure both heart

The improvement of the sensor power supply system in intelligent greenhouse is a research hotspot at present. In intelligent greenhouse environment monitoring demand is high, the traditional cable power supply volume is big, poor security and inconvenient to the characteristics of the paper by using the principle of the wireless power supply, design a kind of orthogonal magnetic field, the Transmitting

Fish telemetry is a potential method to monitor the well-being of farmed fish, where a reference group of fish are equipped with sensors tags that oversee their health and wirelessly transmit the data. Many fish must be tagged to get statistically reliable information. In this paper, very-high-frequency (250 kHz) acoustic channels are characterized using data recorded in an Atlantic salmon pen containing

Digital image recognition has been used in the different aspects of life, mostly in object classification and detections. Monitoring of animal life with image recognition in natural habitats is essential for animal health and production. Currently, Sheep Pain Facial Expression Scale (SPFES) has become the focus of monitoring sheep from facial expression. In contrast, pain level estimation from facial

The AEFishBIT tri-axial accelerometer was externally attached to the operculum to assess the divergent activity and respiratory patterns of two marine farmed fish, the gilthead sea bream (Sparus aurata) and European sea bass (Dicentrarchus labrax). Analysis of raw data from exercised fish highlighted the large amplitude of operculum aperture and body tail movements in European sea bass, which were

Uncomfortable working conditions can lead to a deterioration of product quality, and employee health, as well as reduction in work efficiency. The article presents the results of the research determining the relationship between the height of fruit harvesting, the picker's muscle tension and the surface pressure exerted during harvest. The EMG method was used to measure muscle tension of the thumb