Anthracnose is one of the primary diseases that affect olive production before and after harvest, causing severe damage and economic losses. The objective of this work is to detect this disease in the early stages, using hyperspectral images and advanced modelling techniques of Deep Learning (DL) and convolutional neural networks (CNN). The olives were artificially inoculated with the fungus. Hyperspectral

It is very significant to monitor livestock foraging behavior accurately and in real-time to further improve pasture management and livestock welfare. Although various algorithms have been developed to identify and classify animals' foraging behavior, it still has room to be improved in generality and function. In this study, a representative acoustic dataset generated by typical bodyweight sheep when

Rice, which is among the most widely produced grain products worldwide, has many genetic varieties. These varieties are separated from each other due to some of their features. These are usually features such as texture, shape, and color. With these features that distinguish rice varieties, it is possible to classify and evaluate the quality of seeds. In this study, Arborio, Basmati, Ipsala, Jasmine

Respiratory diseases in pigs impact the wellbeing of animals and increase the cost of production. One of the most appropriate approaches to minimizing these negative effects is the early detection of ill animals. The use of cameras coupled with computer-based techniques could assist the early detection of physiological changes in pigs when they are beginning to become ill and prior to exhibiting clinical

To achieve a non-destructive detection of chlorophyll content in field crops based on the reflectance characteristics of chlorophyll in the visible and near-infrared spectrum (400 nm–1000 nm), a crop chlorophyll detector based on an interference filter optical sensor was designed. The hardware part of this detector mainly comprises a microcontroller unit, a sensor module, an input/output module, and

Digitisation of fruit trees using LiDAR enables analysis which can be used to better growing practices to improve yield. Sophisticated analysis requires geometric and semantic understanding of the data, including the ability to discern individual trees as well as identifying leafy and structural matter. Extraction of this information should be rapid, as should data capture, so that entire orchards

Tracking data are increasingly used for studying social behavior in dairy cows but guidelines are not available regarding the appropriate system setup and data quality. In this study, we investigated the effects of system calibration, as well as data filtering and smoothing methods on the detection of the location and neighbor preferences of dairy cows in a barn using an ultra-wideband real-time location

Weeds are harmful to crop yield. The segmentation of weeds in images is of great significance for precise weeding and reducing herbicide pollution. However, in the field environment, crops and weeds are similar, so it is difficult to accurately segment weed from complex field images. In this paper, an algorithm based on deep learning was proposed to segment weeds from images. This algorithm can segment

Pollination is the key process in seed production of hybrid rice; however, the low level of mechanization and the lack of pollination equipment have restricted pollination efficiency. Therefore, a mechanical impact pollinator with a power chassis, a lifting unit, and an impact device was developed for hybrid rice cross-pollination. The impact device installed with a reciprocating leading screw mechanism

Manual inspection has been a common application for counting the trees and plants in orchards in precision agriculture processes. However, it is a time-consuming and, labour-intensive and expensive task. Recent remote sensing tools and methods provide a revolutionizing innovation for monitoring individual trees and crop recognition as an alternative to manual detection useful for long-term agricultural

Plant transpiration and evaporation from soil surface or growth media in greenhouses produce a large amount of moisture. High relative humidity (RH) favors fungus growth and causes plant diseases thus dehumidification is essential in greenhouses. In this study, a novel model (DehumReq) is developed to predict the dehumidification requirement of greenhouses. The model is based on the crop characteristics

Decision support systems have become increasingly popular in the domain of agriculture. With the development of automated machine learning, agricultural experts are now able to train, evaluate and make predictions using cutting edge machine learning (ML) models without the need for much ML knowledge. Although this automated approach has led to successful results in many scenarios, in certain cases

An essential objective of Precision Livestock Farming (PLF) is to use sensors that monitor bio-responses that contain important information on the health, well-being and productivity of farmed animals. In the literature, vocalisations of animals have been shown to contain information that can enable farmers to improve their animal husbandry practices. In this study, we focus on the vocalisation bio-responses

Studying the spatial distribution of soil types is an important academic and practical issue in agriculture. With the rapid development of remote sensing technology, the role of image texture as an auxiliary variable in remote sensing identification of objects has increased. It is of great importance to ascertain the optimal window size for extracting texture features and the multiscale fusion of texture

In this paper, a sequential deep neural network in conjunction with a joint time-frequency domain data representation is explored for the problem of cattle behaviour classification. The experimental evaluation is based on a real-world dataset with over 3 million samples, collected from sensors with a tri-axial accelerometer, magnetometer and gyroscope, attached to the collar tags of 10 beef steers

While point clouds hold promise for measuring the geometrical features of 3D objects, their application to plants remains problematic. Plants are three dimensional (3D) organisms whose morphology is complex, varies from one individual to another and changes over time. Objective measurement of attributes in 3D point cloud domain is increasingly attractive as techniques improve the accuracy and reduce

A hot-air coupled microwave drying (MHD) system equipped with an online aroma detection and feedback with Portable Electronic Nose, PEN3, was developed to preserve high volatile components in celery stalk. Celery stalk slices were dried at constant temperatures of 50 °C, 60 °C, 70 °C, and 80 °C, and sensors 7 and 9 of 10 sensors in PEN3 were selected and set at stage-cooling drying. Aroma volatiles

The use of radiation technology in agriculture has increased remarkably in recent years. The stimulating effect of low-dose gamma rays, known as hormesis, improves the adaptive response of living organisms to more severe challenges. Different areas of agriculture such as plant physiology, crop breeding and seed biology demand accurate methods for real-time diagnosis of the ionizing radiation effect

Automatic tracking is an important basis for abnormal behavior management and disease prediction of livestock. In commercial farms, the use of surveillance video to track and monitor dairy goats is conducive to improving production efficiency and commercial welfare. In this paper, an algorithm-based on Siamese strategy is presented for the automated tracking of a single dairy goat in the surveillance

The increasing demand for sustainable livestock products also demands new considerations in animal breeding. Breeding programs are now seeking to integrate animal behavioural phenotypes, as these relate to the productivity, health and welfare of the animals and thereby can influence yield and economic benefits in the industry. Traditional manual observation of pig behaviour is time-consuming, laborious

This paper aims to investigate the feasibility combination of X-ray fluorescence (XRF), near-infrared (NIR), and mid-infrared (MIR) sensors for the detection of seven key monitoring elements in the soil. Two strategies for data fusion were adopted: (i) the XRF characteristic bands were fused with all spectral data of NIR and MIR separately, and (ii) the XRF characteristic bands were fused with the

Real Time Kinematic (RTK) systems, together with Global Navigation Satellite System (GNSS) networks, are real-time location systems that are widely used in precision agriculture for autoguidance systems in farm vehicles. The RTK uses a reference signal from a base station, and the accuracy of the autoguidance system depends strongly on the signal quality arriving at the receiver that is installed in

Spectroscopy technology is one of the most important methods for non-destructive and rapid detection of soil total nitrogen (STN) content. It would be convenient and practical for developing a low-cost portable detector of STN content based on spectroscopy if limited wavelengths could be singled out as soil nitrogen characteristic wavelengths. This paper proposed a deep learning-based method for STN

The ability to compensate for seeding rates of planter units has the potential to mitigate the problem of inaccurate seed application rates due to lateral speed differences of planter units when planters are turning. This would result in an increase in crop yields and a reduction of the seed usage. In this article, a GPS-based turn compensation algorithm was designed to offset the seeding rates of

The traditional method based on passive light source for measuring vegetation indices (VIs) has poor adaptability to the light environment and is inconvenient to use. In this paper, utilizing the characteristics of constant proportion of solar radiation energy in each component band, the standard whiteboard response ratio CW with constant value was introduced to construct a new method for calculating

Large-scale, high-accuracy, and adaptive three-dimensional (3D) perception are the basic technical requirements for constructing a practical and stable fruit-picking robot. The latest vision-based fruit-picking robots have been able to adapt to the complex background, uneven lighting and low color contrast of the orchard environment. However, most of them have, until now, been limited to a small field

Biological control is a green and effective pest management method to mitigate or inhibit pests and pest effects by introducing natural enemies. The stinkbug is one of the insects used for biological control and fed on armyworm. It is necessary to separate the farm-raised armyworm from the sticky substrate after pupation. At present, the work is done manually, with high labor intensity, low efficiency

Monitoring grapevine phenology during the agricultural season is one of the most important tasks within the vine field since this is a key input for the proper planning of agricultural labor management. Traditionally, vine growers make very few phenological observations at the field level, which are extrapolated to an entire production unit, without considering the field natural spatial variability

Crop water stress index (CWSI) is a reliable, economic and non-destructive method of monitoring the onset of water stress for irrigation scheduling purposes. Its application, however, is limited due to the need of obtaining the baseline canopy temperatures. This study developed a self-organizing map (SOM) based model to predict the CWSI using microclimatic variables, namely air temperature, canopy

Present study aims to optimize the process parameters and to develop a 3-dimensional, multi-layered finite element model for predicting moisture and temperature profile during simulated solar drying of paddy. Drying of paddy was conducted in an in-house designed photovoltaic integrated hybrid solar dryer. Design expert software was employed for optimization of drying process parameters (power level

Over recent years, many machine learning tools have been developed in the desire to replace conventional empirical models for reference evapotranspiration (ET0) estimation that evidently needed tedious data collection and calibration. The feasibility of novel hybrid inter-model ensembles as a catalyst to further improve the estimation of ET0 in a tropical climate region was investigated. The multilayer

Small distributed robots have the potential to be commercialized in greenhouse operations due to their low cost, high efficiency and light weight compared to conventional greenhouse robots. In order to guide the path of these distributed dynamic robots, a positioning system with a centimetre accuracy is needed. While a GPS navigation system does not perform well in such an environment, the passive

Accurately sorting high-quality soybean seeds is a key task in increasing soybean yield in the breeding industry. At present, sorting systems based on machine vision focus on the recognition of only one side surface. This paper designs and develops a sorting system based on deep learning that can recognize the full surface of soybean seeds. An alternate circumrotating mechanism is used to expose the

In the last decade, researchers have focused more on deep convolutional neural networks (CNNs) than other machine learning algorithms for object detection, localization, classification and segmentation. Such CNNs have achieved remarkable results in these fields and use the bounding boxes as the ground truth data. In this research article, we have used a fully connected network (FCN) for livestock detection

Discriminating winter wheat-summer maize distribution is fundamental for agricultural water resources management in the North China Plain (NCP) which confronts severe conflicts between food production and agricultural water scarcity. However, how the planting area changed since 2000 is still unclear. Therefore, the winter wheat-summer maize from 2001 to 2018 was identified by using the maximum likelihood

Soil aggregate stability (AS) is a crucial soil physical property for sustainable agricultural and environmental land management practices. Yet, its conventional laboratory methods are fastidious and time consuming thereby restricting high-density sampling and large-scale estimation. Therefore, the potential of fusing visible near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy to enhance the

In this study, we established an effective finite element model for branch-stem-fruit systems to: (i) simulate responses under different loads; and (ii) intuitively predict the breaking process of branch-stem joints. The constitutive parameters of fruit branches and apple stalks were measured based on the orthotropic transverse constitutive model. Moreover, the maximum traction force, maximum traction

Dissolved oxygen (DO) content is a significant indicator of water quality in intensive aquaculture ponds and is profoundly related to healthy fish growth. Accurately forecasting DO and analysing its change trends in aquaculture ponds is crucial for fish survival. A novel forecasting model based on a combination of complete ensemble empirical mode decomposition with an adaptive noise Lempel-Ziv complex

The main idea of this paper is to present a programmable logic controller (PLC) based data acquisition, telemetry, and control system for self-propelled pruning residue mulcher prototype. The main power source of the mulcher prototype is the diesel engine. Hydraulic systems were used to transmit the motion received from the motor to the wheels, pick up and mulching unit, and steering systems. The operation

Close range spectra imaging of agricultural plants is widely performed to support digital plant phenotyping, a task where physicochemical changes in plants are monitored in a non-destructive way. A major step before analyzing the spectral images of plants is to distinguish the plant from the background. Usually, this is an easy task and can be performed using mathematical operations on the combinations

Accurate estimation of reference crop evapotranspiration (ET0) is of great significance to crop water use and agricultural water resources management. This study evaluated the performance of four bio-inspired algorithm optimized kernel-based nonlinear extension of Arps decline (KNEA) models, namely KNEA with grasshopper optimization algorithm (GOA-KNEA), KNEA with grey wolf optimizer algorithm (GWO-KNEA)

Sub-optimal pesticide application not only results in wastage and low yield but is also associated with harmful effects to the environment. To ensure optimal application of pesticide, a dynamic approach for pesticide management is required. Existing works do not consider the dynamic demand of pesticide and are based on static models. To solve these issues, this paper proposes a technique for the vehicle

Spatial simulations of crop growth under climate change have been limited to researchers who have access to the resources for high performance computing. The objective of this study was to develop an orchestration aid system for concurrent gridded simulations of crop growth, which would support the design of climate change adaptation options on crop production without expertise in distributed computing

Stressed Haematococcus Pluvialis cells would accumulate astaxanthin with the changes of lipid, pigment, and starch, which recently has been widely studied. Real-time dynamic monitoring of these components will have important guiding significance for screening high-yield and high-quality strains in the industry. Terahertz spectroscopy is not only suitable for measuring large flux samples, but also widely

Calibration regression models based on visible and near-infrared (Vis/NIR) spectroscopy are now widely used in the rapid non-destructive prediction of agricultural products' quality parameters. However, the distributions of products' quality parameters and spectral responses are different in various batches of products, so a calibration regression model built on one dataset might be ineffective when

Automation of grapevine agricultural tasks, e.g., harvesting, requires reliable methods for detecting the exact cutting points of the grape bunches. Dynamically changing vineyard environments, differences between plant varieties, illumination, occlusion, color similarities, and varying contrast make the detection of the grapes’ stems in unstructured environments difficult. In this work, a grape stem

Optimum plant stand density and uniformity is vital in order to maximize corn (Zea mays L.) yield potential. Assessment of stand density can occur shortly after seedlings begin to emerge, allowing for timely replant decisions. The conventional methods for evaluating an early plant stand rely on manual measurement and visual observation, which are time consuming, subjective because of the small sampling

More than two-thirds of human in the world view rice or wheat as their diet, rice and wheat are grown in some regions of China and other countries in Asian. However, a variety of diseases can affect the growth of rice and wheat, reducing their harvest and even cause famine in some areas. Diseases in leaves, as a kind of diseases, have negative impacts on plants. Under this background, quickly and accurately

In order to reduce the mechanical damage caused by apple harvesting robot to apples and realize the non-destructive grasping during the grasping process, an adaptive impedance control strategy for apple harvesting robot compliant grasping is proposed based on the analysis of grasping modes in this paper. Firstly, burgers viscoelastic model is established to describe the rheological characteristics

Locusts (insect pest) often cause serious loss to crops world-wide. Monitoring the occurrence of locust plagues through remote sensing technology has become a common method. However, with the abundance of remote sensing data resources, the corresponding locust remote sensing monitoring models are also diversified, and the selection of model parameters are more complex. How to manage these models scientifically

Demands for rape seeds oil rapidly increase in recent years. The empirical model based on the remotely sensed data provides an efficient approach to predict the rapeseed yield at small scale. The vegetation indices (VIs) derived from the remotely sensed data at the stage of the peak leaf area index (LAI) are usually the main determinant of the empirical model. The LAI of oilseed rape reaches the peak

Transformation and upgrading are crucial for the development of agri-products, whereas an appropriate creative agri-product scheme is the key. How to match the demanders and suppliers of creative agri-product schemes based on the demand analysis of demanders and the expected implement effect analysis of creative schemes provided by suppliers is of great significance, while research concerning this

In respect of pig instance segmentation, the application of traditional computer vision techniques is constrained by sundries barrier, overlapping, and different perspectives in the pig breeding environment. In recent years, the attention-based methods have achieved remarkable performance. In this paper, we introduce two types of attention blocks into the feature pyramid network (FPN) (see nomenclature

This study evaluates the potential of AMSR2 (Advance Microwave Scanning Radiometer2) data for the estimation of Volumetric Soil Moisture (VSM) for bare and agricultural areas. At the first step, the sensitivity of the Microwave Polarization Difference Index (MPDI) to variations in soil and vegetation characteristics were examined at different frequencies. At lower frequencies, the signal attenuation