Random Forest (RF) is a machine learning technique that has been proved to be highly accurate in several agricultural applications. However, to yield prediction, how much this technique may be improved with the adoption of a ranking-based strategy is still an unknown issue. Here we propose a ranking-based approach to potentialize the RF method for maize yield prediction. This approach is based on the

In animal agriculture, animal health directly influences productivity. For dairy cows, many health conditions can be evaluated by trained observers based on visual appearance and movement. However, to manually evaluate every cow in a commercial farm is expensive and impractical. This study introduces a video-analytic system which automatically detects the cow structure from captured video sequences

In cultures like grapevine, growth, yield and fruit quality depend on precise regulation of water stress using precision irrigation. In arid climates with seasonal droughts, karst areas and porous stony soils it is difficult to assess irrigation thresholds only based on soil water balance, requiring novel, plant-based techniques of early drought stress detection. In plants, water stress causes an increase

One of the most important challenges in any society is the issue of food and water supply. Undoubtedly, agriculture has a major role in water consumption, and grains have the largest use in the food of people. Bread is also an essential commodity in households and constitutes the staple food of many people. Proper investigation and management of its various stages, from production to distribution,

With the new era of the Internet, we have a large amount of data available in the form of ratings, reviews, graphs, images, etc. However, still, people face difficulty in finding useful information or knowledge from those data. To address these challenges, recommendation systems come into the picture by providing useful content to the user based on users’ history and similarity among users. Content-based

The digital boundaries of crop fields represent a prerequisite for designing parcel-based crop management platforms, implementing online site-specific agronomic practices and monitoring crop growth per field. Previous approaches on field boundary delineation were mostly developed with medium resolution imagery (e.g., Landsat) for the regions or countries with intensive agriculture and large-sized crop

Leaf disease recognition using image processing techniques is currently a hot research topic. High disease recognition accuracies have been reported in literature particularly in studies that relied on lab images captured under controlled lighting and uniform background conditions. When these systems were tested under real field conditions, however, their performance dropped sharply. Studies have demonstrated

The monitoring of pests in the field or lab experiments allows to identify the variation of infection levels and to enhance the development of integrated pest management programs. The use of traps to capture insects is an alternative in different crops and regions. However, identification and manual counting of captured specimens is often time-consuming, requires taxonomic knowledge, and relies on

In this paper, the color index based thresholding method for background and foreground segmentation of plant images is presented. The proposed method is implemented with color index approach, for this purpose two color indexes are modified to provide better information about the green color of the plants. Two fixed threshold methods are proposed for the color indexes to discriminate between foreground

Stomata are pores in the epidermal tissue of leaf plants formed by specialised cells called guard cells, which regulate the stomatal opening. Stomata facilitate gas exchange, being pivotal in the regulation of processes such as photosynthesis and transpiration. The analysis of the number and behaviour of stomata is a task carried out by studying microscopic images, and that can serve, among other things

Leaf chlorophyll content (LCC) is a crucial indicator of nutrition in crop plants and can be applied to assess the adequacy of nitrogen (N) fertilizer for crops while reducing N losses to farmland. This study estimated the LCC of maize and wheat, and comprehensively examined the effects of the spectral information and spatial scale of unmanned aerial vehicle (UAV) imagery, and the effects of phenotype

Achieving the rapid and accurate detection of apple flowers in natural environments is essential for yield estimation and the development of an automatic flower thinner. A real-time apple flower detection method using the channel pruned YOLO v4 deep learning algorithm was proposed. First, the YOLO v4 model under the CSPDarknet53 framework was built, and then, to simplify the apple flower detection

Precise nitrogen (N) fertilization requires new indices of plants’ nutritional status. Non-structural carbohydrates (NSC) are the energetic currency of plants and can, thus, serve as a physiological indicator for their condition. Nevertheless, only a few records exist about NSC composition and allocation in crops, and their relationship with N uptake and the current methods to detect NSC compositions

In this work we study the effect of changing the vehicle guidance point position over the collision risk and motion control performance of an N-trailer vehicle. Based on a Monte Carlo method, we are able to find the correlation among motion control performance, trajectory curvature variation and guidance point position, to strategically select the latter to enhance the motion control performance. The

In this paper, a lightweight convolution neural network model is proposed to diagnose grape diseases, including black rot, black measles and leaf blight. Focusing on small and low-latency models carried on mobile devices, a novel method is proposed based on lightweight convolution neural networks applying the channelwise attention (CA) mechanism. ShuffleNet V1 and V2 are chosen as the backbones, and

Digital plant phenotyping is emerging as a key research domain at the interface of information technology and plant science. Digital phenotyping aims to deploy high-end non-destructive sensing techniques and information technology infrastructures to automate the extraction of both structural and physiological traits from plants under phenotyping experiments. One of the promising sensor technologies

Effective segmentation of plant leaves is very necessary for non-contact extraction of plant leaf phenotype, especially leaf phenotype under environmental stress. However, the phenotype of leaves will change due to the influence of the environment, which increases the difficulty of detection. In this study, we proposed an accurate automatic segmentation method that combines Mask R-CNN with Density-Based

Monitoring equipment for plant-factory automation requires big capital investments. Low-cost open-source microcontroller platforms have been employed in agriculture to monitor and control different variables of importance for crop production. A low-cost plant-factory monitoring system for measuring air temperature, relative humidity, plant-leaf temperature, pH, electrical conductivity, dissolved oxygen

The analysis of uncertainty in large-scale agricultural economic models has gained attention from a policymakers and researchers’ viewpoint. The different methodologies available vary depending on data availability and the nature of the variables subject to analysis, which in turn influences the outcomes. When evaluating the results of previously applied partial stochastic methodologies to partial

The increased use of pesticides across the globe has a major impact on public health. Advanced sensing methods are considered of significant importance to ensure that pesticide use on agricultural products remains within safety limits. This study presents the experimental testing of a hybrid, nanomaterial based gas-sensing array, for the detection of a commercial organophosphate pesticide, towards

The most challenging task in the agricultural sector is to accurately predict crop yield. A typical machine learning algorithm often uses real data to predict crop yield. In this study, we used data generated by the Wild Blueberry Pollination Model, a spatially explicit simulation model validated by field observation and experimental data collected in Maine USA during the last 30 years. The main aim

Irrigation is one of the most important activities in crop management. The aim of irrigation scheduling is to supply the correct amount of water to the crops and define appropriate irrigation times. The rational behavior of intelligent agents added to internet of things technologies could be a strategy for improving the performance of automatic irrigation systems. This paper presents the design and

Weed control is a global issue, and has attracted great attention in recent years. Deploying autonomous robots for weed removal has great potential in terms of constructing environment-friendly agriculture, and saving manpower. In this paper, we propose a weed/crop segmentation network that provides better performance for precisely recognizing the weed with arbitrary shape in complex environment condition

Stand counts is one of the most common ways farmers assess plant growth conditions and management practices throughout the season. The conventional method for early-season stand count is through manual inspection, which is time-consuming, laborious, and spatially limited in scope. In recent years, Unmanned Aerial Vehicles (UAV) based remote sensing has been widely used in agriculture to provide low-altitude

Frequent spring flooding in Southern Quebec’s Nicolet River watershed has a history of causing severe damage, which is likely to worsen as climate change progresses. Employing the ArcSWAT model, an attempt was made to assess the potential impacts of climate change on the Nicolet River watershed’s seasonal and annual streamflow, particularly that portion affected by snowmelt. Calibrated and validated

Mastitis is a disease that affects dairy cow health, and the timely detection of mastitis can improve the efficiency of mastitis treatment and reduce economic losses in the milk industry. To improve the detection speed and achieve automatic recognition of dairy cow mastitis, this study proposed a deep learning network EFMYOLOv3 (Enhanced Fusion MobileNetV3 You Only Look Once v3) based on the bilateral

Suitable design and management of drip irrigation systems depends on an accurate knowledge of the moisture distribution patterns around the emitters. An important parameter in the design of surface and subsurface drip irrigation systems is the wetted pattern of soil area in the up and down wetted areas of drippers. Accurate estimation of the up and down wetted areas in surface/subsurface irrigation

Manual harvesting of fresh-market crops like strawberries is very labor-intensive. Apart from picking fruits, pickers spend significant amounts of time carrying full trays to a collection station at the edge of the field. Small teams of harvest-aid robots that help large picking crews by transporting empty and full trays can increase harvest efficiency by reducing pickers’ non-productive walking times

Maximizing potato productivity and economic return calls for a proper seeding technology that is capable to manage in-field soil variability by optimizing the input seed densities. Current study evaluates the agronomic and economic potential of site-specific seeding (SSS) of consumable potato in comparison with uniform rate seeding (URS) approach. Two treatments of map-based SSS were tested in two

Proximal sensing is increasingly highlighted, owing to its potential for various applications in precision agriculture. However, the uses of its related special devices like visible/near-infrared (VNIR) cameras for crop mapping are yet limited in practice, due to their high costs in market. To handle this issue, this case study was dedicated to modifying a low-cost RGB camera as a VNIR one in a do-it-yourself

The current methods of phenotyping for breeding lines require a lot of time, labor and cost. In recent years, unmanned aerial system (UAS) has paved the way for the development of field high-throughput phenotyping for crops rapidly. Different sensors such as regular RGB camera (Red, Green and Blue), multispectral imaging camera (several wavebands), hyperspectral imaging camera (hundreds and even thousands

Computer vision technologies have attracted significant interest in precision agriculture in recent years. At the core of robotics and artificial intelligence, computer vision enables various tasks from planting to harvesting in the crop production cycle to be performed automatically and efficiently. However, the scarcity of public image datasets remains a crucial bottleneck for fast prototyping and

Blockchain quickly became an important technology in many applications of precision agriculture discipline. The need to develop smart P2P systems capable of verifying, securing, monitoring, and analyzing agricultural data is leading to thinking about building blockchain-based IoT systems in precision agriculture. Blockchain plays the role of pivotal in replacing the classical methods of storing, sorting

Farming and food production for the increasing world’s population require proper preparation of a diverse range of environmental parameters especially water availability and accessibility. This study investigated the effect of water scarcity on crop selection and spatial pattern of croplands in an agricultural landscape in central Iran. Towards this aim, the main crops including wheat, alfalfa, potato

Automatic identification of butterfly species has attracted more and more attention due to the increasing demand for the accuracy and timeliness of butterfly species identification. Since the butterfly images we captured are usually ecological images, which not only have butterflies but also contain many irrelevant objects, such as leaves, flowers and other complex backgrounds. Therefore, segmenting

Rice seeding in hill is beneficial to the growth of rice in the field and the increase of rice yield. However, rice seed metering device usually uses multiple rice seeds for sowing, which is difficult to form a hill. To improve the performance of rice seeding in hill, the seed throwing mechanism of the rice pneumatic seed metering device with adjustable seeding rate was study, and the trajectory of

Apple trees commonly require the removal of excessive flowers by thinning to produce high quality fruit. Machine vision has recently been applied to detect the flower density as the first step in this process. Existing work relying on color thresholding is sensitive to imaging conditions and the most recent published work using deep learning in this context has proven to be exceptionally slow to process

Employing leaf shape features for plant species recognition, especially for cultivar recognition is very challenging due to the high similarities of leaf shapes across different species and cultivars. In this paper, we attempted a new strategy of depicting leaf shapes by convolving the contour vector functions with Gaussian functions of different widths. The resulting Gaussian convolution vector (GCV)

Fruit instance segmentation is a key element for autonomous picking systems. This paper proposes a methodology for instance segmentation of strawberries using deep learning techniques. This methodology presents the following changes in relation to the well-known Mask R-CNN network: a new architecture is designed for the backbone and the mask network; the object classifier and the bounding-box regressor

Ensemble methods give better performance compared to a single machine learning algorithm. Vote is one of the best ensembles. Vote merges predictions from Simple Logistics and the Naive Bayes algorithms in the present work. The paper presents a new ensemble approach – Ensemble Particle Swarm Optimization (EnsPSO). The EnsPSO approach is a combination of (i) Vote, (ii) Correlation based Feature(s) Selection

On-farm monitoring of milk composition can support close control of the udder and metabolic health of individual dairy cows. In previous studies, near-infrared (NIR) spectroscopy applied to milk analysis has proven useful for predicting the main components of raw milk (fat, protein, and lactose). In this contribution, we present and evaluate a precise tool for online milk composition analysis on the

The management of plant disease is a significant economic and environmental factor in the production of greenhouse tomato plants. Human expertise for assessing the presence and extent of disease is important in creating and implementing management plans, but it is difficult and expensive to acquire. In this paper, we present a new computer vision system to automatically recognize several diseases,

Sudden death syndrome (SDS), a fungal infection in soybean caused by Fusarium virguliforme, greatly affects the plant health and in some cases, can cause yield losses of more than 70%. Infected plants are scored by visual assessment based on severity and extent of infection. This manual process is time intensive and not practical for large acreages. Diseased and stress in plants show elevated canopy

The modern poultry industry is large-scale and breeding-intensive, making the spread of disease in poultry easier, faster and more harmful. Avian influenza (AI) is the most important disease in poultry, and prevention and detection of avian influenza in poultry is a focus of scientific research and the poultry industry. In this paper, a new sound recognition method, the chicken sound convolutional

The currently used large-scale flower seedling transplanting equipment is expensive and has poor adaptability, while the small-scale transplanting equipment has problems with seedling damage and low automation. In view of this situation, a 2-DoF five-bar transplanting mechanism for multiple-point picking to single-point planting was designed. In a motion cycle, the seedlings could be picked from the

This paper presents the Hybrid Particle Swarm Optimization and Neighborhood Strategy Search (HPSO-NS) to solve a tractor scheduling and routing problem with equipment allocation constraint in sugarcane field preparation, to help the growers catch the season and ensure advantageous production of sugar from sugarcane. This problem can be formulated as the flexible flow shop scheduling problem with machine

In order to solve the problem of blockage of the clamping conveying system for peanut combine harvester, a peanut vine clamping conveying system (PVCCS) with flow control scheme is proposed, and the system mathematical modeling of hydraulic system is presented based on its physical attributes. Taking Baisha peanut variety as the research subject, the reasonable field working speed and blockage pressure

As a key part of vineyard management, irrigation of grapevines puts forward the need of scheduling water resources in a more precise and efficient way. Typically, irrigation plans are generated through the use of mathematical models. However, the unwelcoming fact is that such models usually require a massive quantity of monitored data that is often unavailable or incomplete in most developing countries

Accurate and rapid recognition of dairy cow’s motion behaviours is the key to intelligent perception of its health state. To achieve the recognition of cows’ lying, standing, walking, drinking and feeding behaviours, first, based on the advantage of the efficient feature extraction of EfficientNet, the spatial feature extraction of cow’s video frames was realized. Then, to fully extract the characteristics

Daily reference evapotranspiration (ETo) forecasts can help farmers in irrigation planning. Therefore, this study assesses the potential of deep learning (long short-term memory (LSTM), one-dimensional convolutional neural network (1D CNN) and a combination of the two previous models (CNN-LSTM)) and traditional machine learning models (artificial neural network (ANN) and random forest (RF)), in regional

Predicting crop yields and their spatio-temporal variability under a changing climate is a challenging but essential undertaking for crop management and policymaking purposes. The availability of information on risks associated with effects of climatic variability on agricultural activity outcomes is critical for stakeholders ranging from individual landowners to national economists alike. This research

Accurate spatial information of agricultural parcels is fundamental to any system used in monitoring greenhouse gas emissions, biodiversity developments, and nutrient loading in agriculture. The inefficiency of the traditional methods used in obtaining this information is increasingly paving the way for Remote Sensing (RS). The Multiresolution Segmentation (MRS) algorithm is a well-known method for

In the existing machine vision technology for tomato leaf disease recognition, due to interference from the external environment, it is easy to generate noise during image acquisition, and the characteristics of different diseases are similar, which makes image disease recognition difficult. Therefore, we propose a new framework for tomato leaf disease recognition. First, the image is denoised and

Animal liveweight (LW) data collection is key to monitor health, nutrition, and reproduction of cattle. However, this is challenging in grazing systems using traditional technology due to the need of mustering animals into handling facilities with the required frequency. Such practical constraints make it difficult to gather frequent LW data to study the effects of different intervals between LW measures

In this study, hyperspectral imagery was used to develop models for predicting onion yields in two years (2017 and 2018). First, the bands for the full width half maximum (FWHM) measurement of 5 nm in the canopied areas were merged as FWHM 10, 25, and 50 nm. This was based on a commercialized band-pass filter that considered the development of the compact multispectral image sensors. Then, band rationing