Knowledge of the body composition of growing pigs is of interest to breeding companies and producers, as it can be used to optimize production. As an emerging noninvasive technology computed tomography (CT) has been extensively applied in animal production studies. Recently, deep learning has generated new insights in medical image segmentation. In this paper, we describe a noninvasive method to automatically

Physiological storage disorders affect a range of commercially important pomefruit and result in fruit losses and wastage of resources. Disorders can develop during and/or after storage and symptoms are strongly influenced by the growing environment and orchard management. Furthermore, fruit which receive similar orchard management and storage can vary greatly in disorder incidence and severity. Biological

In this study, Deep Learning (DL) was used to detect powdery mildew (PM), persistent fungal disease in strawberries to reduce the amount of unnecessary fungicide use, and the need for field scouts. This study optimised and evaluated several well-established learners, including AlexNet, SqueezeNet, GoogLeNet, ResNet-50, SqueezeNet-MOD1, and SqueezeNet-MOD2. Data augmentation was carried out from among

In order to improve the risk evaluation and management in fresh grape supply chain and enhance the sustainable level of the supply chain, this study applied neural network to evaluate the risk of fresh grape supply chain from the perspective of sustainable development. Firstly, the possible risk factors in the supply chain were identified and the risk evaluation index system were proposed; then risk

Agricultural pest catches on sticky traps can be used for the early detection and identification of hotspots, as well as for estimating relative abundances of adult pests, occurring in greenhouses. This study aimed to construct a detection model for whitefly and thrips from sticky trap images acquired in greenhouse conditions. An end-to-end model, based on the Faster regional-convolutional neural network

The marketability and adoption of robotic systems in agriculture is largely limited by economic and technology barriers that prevent highly efficient autonomous operations at a cost that justifies the generally low commodity values. From the technology perspective, autonomous systems exhibit brittleness in uncontrolled, unforeseen, and unlearned situations, prevalent in complex agricultural environments

The use of birthing sensors in controlled dairy environments have been effectively employed to increase cow and calf survival at birth through prompt intervention of dystocia events. Yet, they have not been developed or utilized in large-scale environments (i.e., ranching operations) where cattle are managed using free-grazing production systems. This study evaluated vaginal birthing sensors which

Physiological assumptions incorporated in crop models need improvement to more realistically represent responses to heat stress, rising CO2, and genetic diversity. Coupling of leaf-level photosynthesis and stomatal conductance sub-models within an energy balance has been proposed to improve gas exchange predictions underlying many of these responses. The purpose of this study was to calibrate model

In supplementary irrigation areas, it is very important to formulate an irrigation plan based on the previous precipitation. However, because of the difficulty of accurately predicting the weather, most studies on irrigation decision making have recommended irrigation schemes under different historical weather years. In this study, a water-saving irrigation system for winter wheat based on the DSSAT

The fraction of green canopy cover (CC) is an important feature commonly used to characterize crop growth and for calibration of crop and hydrological models. It is well accepted that there is a relation between CC and NDVI through linear or quadratic models, however a straight-forward empirical approach, to derive CC from NDVI observations, is still lacking. In this study, we conducted a meta-analysis

Whether from the perspective of agricultural production or food safety, potato germination detection is of great significance. Since the features (color, texture and context) of the germination area are similar to those of the non-germination area, the existing vision frameworks are difficult to accurately detect the germinations on the surface of potatoes. In this study, the method for detecting potato

In order to improve the efficiency and reduce high cost for seedlings sorting in the raising process of hydroponic lettuce seedlings, we propose an automatic detection method for hydroponic lettuce seedlings based on improved Faster RCNN framework, taking the dead and double-planting status of seedlings growing in a single hole as our research objects. Since the characteristics of hydroponic lettuce

Learning from a few samples to automatically recognize the pests or plants is an attractive and promising study with a low cost of data to protect the agricultural yield and quality. Although there have been a handful of efforts on the few-shot classification in agriculture, none of them involves the task-driven meta-learning paradigm. This study is the first work of task-driven meta-learning few-shot

Aiming at the difficulty of sugarcane nodes identification and location during automatic cutting of sugarcane seeds, based on machine vision system, this paper proposed a sugarcane nodes identification algorithm based on sum of local pixel of minimum points of vertical projection function. Firstly, according to the color and texture characteristics of yellow sugarcane, the captured RGB image of sugarcane

For many horticultural crops, variation in quality (e.g., shape and size) contributes significantly to the crop’s market value. Metrics characterizing less subjective harvest quantities (e.g., yield and total biomass) are routinely monitored. In contrast, metrics quantifying more subjective crop quality characteristics such as ideal size and shape remain difficult to characterize objectively at the

Early detection of internal bruise is one of the major challenges in postharvest quality sorting processes in Lingwu long jujube. In this study, the visible/near infrared (VIS/NIR) hyperspectral imaging system (400–1000 nm) was used to rapidly detect the intact and damaged jujube at five time points after mechanical damage (2 h, 4 h, 8 h, 12 h and 24 h). The region of interest of samples was selected

An unmanned ground-based canopy density measurement system to support precision spraying in apple orchards was developed to precisely apply pesticides to orchard canopies. The automated measurement system was comprised of a light detection and ranging (LiDAR) sensor, an interface box for data transmission, and a laptop computer. A data processing and analysis algorithm was developed to measure point

Limited available land for livestock farming and increasing land-use costs have forced some large animal feeding operations to build animal barns from conventional one-floor to multi-floor in China recently because multi-floor animal building (MFAB) has a great advantage in saving land utilization. The ventilation system is an important part of animal building determining the indoor environmental condition

Deep learning has achieved kiwifruit detection with high accuracy and fast speed. However, all the kiwifruits have been labeled and detected as only one class in most researches for robotic fruit picking, where fruits occluded by branches or wires have been detected as pickable targets. End-effectors or robots may be damaged by the branches or wires when they are forced to pick those fruits. Therefore

Accurate and timely crop areas monitoring and yield modeling are of great significance for food security and sustainability of agro-ecosystems. This study provides a rapid and robust method for planted area monitoring and yield modeling of winter wheat based on Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2001 to 2016 in the Huang-Huai-Hai Plain of China. Statistical data at the

Microalgae lipid and its degree of fatty acid unsaturation (DU) have the ability to undergo programmatic changes in response to different environmental and culture conditions. A real-time and reliable approach to monitor the change patterns of lipid-accumulation and DU is crucial for microalgal culture and bioprocess control. In this study, a fast visual and nondestructive method based on visible/short-wave

Maintaining the massive forests of the forestry industry requires physical samples from the tree canopy to enable breeding, genetics research and monitoring of diseases. An autonomous Unmanned Aerial Vehicle (UAV) canopy sampling solution requires locating and tracking a highly unstructured, moving object against a dynamic, cluttered background. The developed algorithm utilises stereo vision techniques

Lamb survival is influenced by the culmination of a sequence of often interrelated events including genetics, physiology, behaviour and nutrition, with the environment providing an overarching complication. Machine learning algorithms offer great flexibility with regard to problems of complex interactions among variables. The objective of this study was to use machine learning algorithms to identify

Excessive use of herbicides for weed control increases the cost of crop production and can lead to environmental degradation. An intelligent spraying system can apply agrochemicals on an as-needed basis by detecting and selectively targeting the weeds. The objective of this research was to investigate the feasibility of using deep convolutional neural networks (DCNNs) for detecting lamb’s quarters

Due to high rate of nutrient removal by cotton plants, the productive cotton-growing soils of Australia are becoming depleted of exchangeable (exch.) cations. For long-term development, data on exch. calcium (Ca), magnesium (Mg), potassium (K) and sodium (Na) throughout the soil profile is required. However, traditional laboratory analysis is tedious. The visible-near-infrared (Vis-NIR) spectroscopy

To improve the automation level of the round baler, an automatic net feeding control system with the function of remote monitoring was developed and tested both in the laboratory and the field. The system can automatically trigger the electromagnetic clutch to wrap the net based on the sensors. In order to avoid false alarms caused by tailgate vibration at the end of the bale formation, a time filter

Alfalfa (Medicago sativa L.) is an important forage crop grown worldwide for animal feed, green manure, and as a land cover. However, very few approaches exist for timely field scale mapping of crop status, yield and quality attributes for management of inputs, harvest and storage resources, budgeting, crop insurance, etc. This study aims to apply high-resolution aerial multispectral and thermal infrared

Developments in remote sensing technology have led to a continuous increase in the volume of remote-sensing data, which can be qualified as big remote sensing data. A wide range of potential applications is using these data including land cover classification, regional planning, catastrophe prediction and management, and climate-change estimation. Big remote sensing data are characterized by different

Barley is one of the most important Scottish crops with major economic implications for the Scottish economy because of its use in whisky and beer production. Managing nitrogen (N) fertilizer at field scale for barley is difficult because of the complexity to simultaneously achieve profitability, malting quality and reducing N losses to groundwater. The aim of this study was to model spatial and temporal

Task assignment is a key problem in multi-machine cooperative navigation. In the context of regional farmland operation, multiple agricultural machines often need to complete multiple tasks together. In order to realize the management of multiple agricultural machinery cooperation, studies on task assignment based on the improved ant colony algorithm have been conducted under the farmland operation

Corn kernel moisture content is the most frequently used indicator for kernel maturity prediction and determination of the appropriate mechanized harvesting time. Corn varieties with lower moisture contents and thus faster dehydration of the corn kernels after physiological maturity are suitable for high plant density and mechanized harvesting. However, nondestructive measurement of the dynamic moisture

Precision Livestock Farming relies on several technological approaches to acquire in the most efficient way precise and up-to-date data concerning individual animals. In dairy farming, particular attention is paid to the automatic cow detection and tracking, as such information is closely related to animal welfare and thus to possible health issues. Computer vision represents a suitable and promising

Timely and accurate information on winter wheat distribution and planting area is of great significance to food security, policy-making, and ecological function evaluation. However, several problems exist in the traditional winter wheat mapping approaches using remote sensing data, such as the limited spatial resolution of the remote sensing image data, the utilization of full-season remote sensing

This study aimed to improve the potential of visible-near infrared (VIS-NIR) spectroscopy in predicting the contents of salt and major soluble ions in the topsoil at Hetao Irrigation District in Inner Mongolia, China. A total of 120 soil samples (sampling depth is 0–20 cm) were collected from the field. Fractional-order derivatives (FODs) (with intervals of 0.05 and range of 0–2) were used for soil

High-throughput phenotyping has been widely studied in plant science to monitor plant growth and analyze the influence of genotypes and environment on plant growth. To meet the demand of large-scale high-throughput phenotyping, unmanned aerial vehicles (UAVs) have been developed for near-ground remote sensing. UAVs based remote sensing has been used for high-throughput phenotyping of various traits

Electrical conductivity (EC) represents a material’s ability to conduct electric current. Soil EC has been used as a soil quality attribute related to soil pH, nutrient availability, crop suitability and soil microbial activity. Time domain reflectometry (TDR) estimates soil water content and EC based on the propagation/reflection and energy attenuation of voltage signals along a waveguide. To maximize

In precision farming, accurate estimation of canopy nitrogen concentration (CNC) is valuable for effective crop growth monitoring and nitrogen (N) fertiliser management. To date, many canopy multispectral indices have been proposed as indicators for CNC; however, many of these indices have also shown sensitivity to biomass and their performance drops at high biomass levels. Dependence on growth stage

This paper presents a novel approach for the detection of disease-infected leaves on trees with the use of deep learning. Focus of this study was to build an accurate and fast object detection system that can identify anthracnose-infected leaves on walnut trees, in order to be used in real agricultural environments. Similar studies in the literature address the disease identification issue; however

Forage analysis by near infrared reflectance (NIR) spectroscopy has had many advancements since it began in the 1970s. There have been steady improvements in instrumentation, in computers, and chemometric algorithms for developing calibrations. Thus, making NIR the most used technique to routinely analyze samples for forage producers, plant breeders, animal nutritionists, cattle farmers, and feed companies

Plant high-throughput phenotyping technology is taking more and more important roles in soybean breeding and genetic research thanks to the advance in sensing and data analytic technologies. However, commercial high-throughput phenotyping systems of general purpose are expensive and complicated for many research groups, and their data analytic methods are designed for specific research projects. The

Crop height is a key structure parameter for the modelling of crop growth, healthy status, yield forecasting and biomass estimation. Unmanned aerial vehicle (UAV) LiDAR systems can quickly and precisely acquire vegetation structure information at a low cost. UAV LiDAR data are increasingly used in vegetation parameters estimation. In this study, we estimated maize and soybean heights using two methods

Nitrogen (N) excess due to mineral fertilization in conventional crop farming has a significant negative impact on the environment. Variable rate N application (VRNA) is a promising tool to increase N recovery rates in spatially heterogeneous fields. Real-time sensor systems for VRNA usually consider only the crop’s N status and their fertilization algorithms are abundantly deterministic. Due to their

The body weight of breeding broiler chickens (broiler breeders) is an important control variable used to optimize the amount, quality and fertility of the eggs being laid. In modern breeding barns, the population of animals generally supports a female:male ratio of 10:1, wherein males and females each receive their own food supply. To control this supply the broiler breeders are weighed using automatic

Online monitoring and control of the drying processes are necessary to maintain the final products’ quality attributes, especially when a microwave system is used to facilitate the drying process. Machine learning techniques could be a suitable and very accurate approach for modelling the drying process. Two machine learning techniques including Support Vector Regression (SVR) and Artificial Neural

In this study, a traction double-row fully automatic transplanter was designed to improve the transplanting efficiency and automation of vegetable plug seedlings. Special attention was given to the designing of specific key components of the fully automatic transplanter. It is primarily composed of the following specialized devices: seedling feeding, combing, inserting ejecting-type seedling extraction

Soft grasping is a great challenge for picking robots and its bionic inspiration originates from human fingers. In this study, the hand was scanned to obtain the internal structure of fingers by a computerized tomography (CT) scanner, and the soft contact mechanical index a was defined for characterizing the degree of softness of a finger region during gentle grasping. The effects of mechanics and

In recent years, map-based variable rate (VR) fertilization has become more and more established in conventional crop farming, as it enables a better match of inputs to the growing conditions of cultivated crops and thus gives the opportunity for further environmental and economic optimization of plant production processes. Nonetheless, the VR application of mineral fertilizer performed by a centrifugal

With the increasing global water scarcity, efficient assessment methods for crop water stress have become a prerequisite to perform precision irrigation scheduling. The 1accessibility of infrared thermal sensor provides a powerful tool to detect and quantify crop water stress. This paper reviews the current practices of infrared thermal imagery utilized to assess crop water stress. Overall, three technological

Seed genuineness and varietal purity are key indicators of seed quality. Detecting the genuineness of a single seed can simultaneously determine seed purity. The traditional methods for detecting seed genuineness or identifying a variety are time-consuming, costly, and destructive. This study intends to establish a low-cost, efficient, and non-destructive method to detect the genuineness of single

The basic behaviors of dairy cows (drinking, ruminating, walking, standing and lying) are closely related to their physiological health status. Consequently, intelligent behavior recognition is of significance for the automatic diagnosis and precision farming of dairy cows. Realizing the accurate behaviors classification in complex environments involving low quality surveillance videos, complex illumination

Efficient selection of drought-tolerant crops requires identification and high-throughput phenotyping (HTP) of the complex functional (especially canopy-conductance) traits that elicit plant responses to continually fluctuating environmental conditions. However, phenotyping of such dynamic physiology-based traits has been immensely challenging especially due to the limited availability of adequate

Water status is critical since it affects photosynthetic efficiency and limits crop yield. Thus it is essential to calculate the water content of crop fast and nondestructively. In this work, hyperspectral reflectance in 900–1700 nm was used to estimate water content of corn leaves. The fractional order Savitzky-Golay derivation (FOSGD) was used to pretreat the hyperspectral data. The result indicated

Some non-digitized measuring instruments such as pointer meters are utilized by some farmlands by virtue of their high stability and accuracy, and the data collection depending on the human movements requires considerable time and effort and has a low technology level. Therefore, an intelligent pointer meter indication reading and automatic collection method is proposed using machine vision and wireless

Stored grain pests severely affect both grain production and quality. It is essential to adopt technology to identify its kinds as quickly as possible. The objective of this work was to use deep learning to identify six kinds of common stored grain pests. To reduce the cost of manual identification, a method based on computer vision technology, namely modified dilated residual network (MDRN) was proposed

In Viticulture, visual inspection of the plant is a necessary task for measuring relevant variables. In many cases, these visual inspections are susceptible to automation through computer vision methods. Bud detection is one such visual task, central for the measurement of important variables such as: measurement of bud sunlight exposure, autonomous pruning, bud counting, type-of-bud classification

Pests may lead to low crop productivity and profitability. Pesticides are commonly used to protect crops from pests. However, too much pesticide is not only associated with harmful effects to the environment but may also lead to sub-optimal pest management. The existing works focus on the vehicle routing problem for pesticide management without giving due consideration to finding the optimal time,

Diseases and pests cause serious damage in crop production, reducing yield and fruit quality. Their identification is often time-consuming and requires trained personnel. New sensing technologies and artificial intelligence could be used for automatic identification of disease and pest symptoms on grapevine in precision viticulture. The aim of this work was to apply deep learning modelling and computer

Grasslands are under continuous threat of conversion and subsequent degradation, which has a detrimental impact on grassland productivity and grazing capacity, affecting the livestock industry. Fractional vegetation cover as indicator of grassland condition and productivity has been extensively researched, however, existing approaches and products are limited with respect to accessibility, affordability

The coconut palm plantation industry relies heavily on expert advice to identify and treat infections. Computer vision in deep learning technology opened up an avenue in the agriculture domain to find a solution. This study focuses on the development of an end-to-end framework to detect stem bleeding disease, leaf blight disease, and pest infection by Red palm weevil in coconut trees by applying image