Sustainable Agrochemical Exploitation of Ligurian Aromatic Plant Ecotypes.

The characterization of local improved varieties as well as the reduction of synthetic chemical fertilizers are sustainable approaches in the vision of a new precision Farming. Aim of our study was to improve the geographical characterization of local ecotypes and to identify peculiar features of new crops in terms of bioactive compounds. NMR and LC-MS metabolite profiling approaches followed by multivariate data analysis were applied to characterize local rosemary and garlic ecotypes. With the aim of applying for a protected designation of origin, orthogonal partial least squares discriminant analysis (OPLS-DA) was used to identify representative sensory quality indicators for Vessalico garlic and rosemary "Eretto Liguria" local ecotypes, Variable Influence on Projections (VIP) values of OPLS-DA indicated six metabolites as quality indicators for Vessalico garlic and sixteen metabolites as quality indicators for rosemary "Eretto Liguria". Finally, to discover and utilize new ecotypes in a sustainable way, Vessalico garlic extracts antiviral activity, previously evaluated against Tomato brown rugose fruit virus (ToBRFV), a Tobamovirus affecting tomato crops, was extended to Pepino mosaic virus (PepMV) with positive results.

Weed Detection from Unmanned Aerial Vehicle Imagery Using Deep Learning-A Comparison between High-End and Low-Cost Multispectral Sensors.

In order to meet the increasing demand for crops under challenging climate conditions, efficient and sustainable cultivation strategies are becoming essential in agriculture. Targeted herbicide use reduces environmental pollution and effectively controls weeds as a major cause of yield reduction. The key requirement is a reliable weed detection system that is accessible to a wide range of end users. This research paper introduces a self-built, low-cost, multispectral camera system and evaluates it against the high-end MicaSense Altum system. Pixel-based weed and crop classification was performed on UAV datasets collected with both sensors in maize using a U-Net. The training and testing data were generated via an index-based thresholding approach followed by annotation. As a result, the F1-score for the weed class reached 82% on the Altum system and 76% on the low-cost system, with recall values of 75% and 68%, respectively. Misclassifications occurred on the low-cost system images for small weeds and overlaps, with minor oversegmentation. However, with a precision of 90%, the results show great potential for application in automated weed control. The proposed system thereby enables sustainable precision farming for the general public. In future research, its spectral properties, as well as its use on different crops with real-time on-board processing, should be further investigated.

Heart Rate and Acceleration Dynamics during Swim-Fitness and Stress Challenge Tests in Yellowtail Kingfish (Seriola lalandi).

The yellowtail kingfish is a highly active and fast-growing marine fish with promising potential for aquaculture. In this study, essential insights were gained into the energy economy of this species by heart rate and acceleration logging during a swim-fitness test and a subsequent stress challenge test. Oxygen consumption values of the 600-800 g fish, when swimming in the range of 0.2 up to 1 m·s-1, were high-between 550 and 800 mg·kg-1·h-1-and the heart rate values-up to 228 bpm-were even among the highest ever measured for fishes. When swimming at these increasing speeds, their heart rate increased from 126 up to 162 bpm, and acceleration increased from 11 up to 26 milli-g. When exposed to four sequential steps of increasing stress load, the decreasing peaks of acceleration (baseline values of 12 to peaks of 26, 19 and 15 milli-g) indicated anticipatory behavior, but the heart rate increases (110 up to 138-144 bpm) remained similar. During the fourth step, when fish were also chased, peaking values of 186 bpm and 44 milli-g were measured. Oxygen consumption and heart rate increased with swimming speed and was well reflected by increases in tail beat and head width frequencies. Only when swimming steadily near the optimal swimming speed were these parameters strongly correlated.

Factors influencing fruit cracking: an environmental and agronomic perspective.

Fruit cracking, a widespread physiological disorder affecting various fruit crops and vegetables, has profound implications for fruit quality and marketability. This mini review delves into the multifaceted factors contributing to fruit cracking and emphasizes the pivotal roles of environmental and agronomic factors in its occurrence. Environmental variables such as temperature, relative humidity, and light exposure are explored as determinants factors influencing fruit cracking susceptibility. Furthermore, the significance of mineral nutrition and plant growth regulators in mitigating fruit cracking risk is elucidated, being calcium deficiency identified as a prominent variable in various fruit species. In recent years, precision farming and monitoring systems have emerged as valuable tools for managing environmental factors and optimizing fruit production. By meticulously tracking parameters such as temperature, humidity, soil moisture, and fruit skin temperature, growers can make informed decisions to prevent or alleviate fruit cracking. In conclusion, effective prevention of fruit cracking necessitates a comprehensive approach that encompasses both environmental and agronomic factors.

In Vivo Prediction of Breast Muscle Weight in Broiler Chickens Using X-ray Images Based on Deep Learning and Machine Learning.

Accurately estimating the breast muscle weight of broilers is important for poultry production. However, existing related methods are plagued by cumbersome processes and limited automation. To address these issues, this study proposed an efficient method for predicting the breast muscle weight of broilers. First, because existing deep learning models struggle to strike a balance between accuracy and memory consumption, this study designed a multistage attention enhancement fusion segmentation network (MAEFNet) to automatically acquire pectoral muscle mask images from X-ray images. MAEFNet employs the pruned MobileNetV3 as the encoder to efficiently capture features and adopts a novel decoder to enhance and fuse the effective features at various stages. Next, the selected shape features were automatically extracted from the mask images. Finally, these features, including live weight, were input to the SVR (Support Vector Regression) model to predict breast muscle weight. MAEFNet achieved the highest intersection over union (96.35%) with the lowest parameter count (1.51 M) compared to the other segmentation models. The SVR model performed best (R2 = 0.8810) compared to the other prediction models in the five-fold cross-validation. The research findings can be applied to broiler production and breeding, reducing measurement costs, and enhancing breeding efficiency.

Intelligent weight prediction of cows based on semantic segmentation and back propagation neural network.

Accurate prediction of cattle weight is essential for enhancing the efficiency and sustainability of livestock management practices. However, conventional methods often involve labor-intensive procedures and lack instant and non-invasive solutions. This study proposed an intelligent weight prediction approach for cows based on semantic segmentation and Back Propagation (BP) neural network. The proposed semantic segmentation method leveraged a hybrid model which combined ResNet-101-D with the Squeeze-and-Excitation (SE) attention mechanism to obtain precise morphological features from cow images. The body size parameters and physical measurements were then used for training the regression-based machine learning models to estimate the weight of individual cattle. The comparative analysis methods revealed that the BP neural network achieved the best results with an MAE of 13.11 pounds and an RMSE of 22.73 pounds. By eliminating the need for physical contact, this approach not only improves animal welfare but also mitigates potential risks. The work addresses the specific needs of welfare farming and aims to promote animal welfare and advance the field of precision agriculture.

Cultivating a sustainable future in the artificial intelligence era: A comprehensive assessment of greenhouse gas emissions and removals in agriculture.

Agriculture is a leading sector in international initiatives to mitigate climate change and promote sustainability. This article exhaustively examines the removals and emissions of greenhouse gases (GHGs) in the agriculture industry. It also investigates an extensive range of GHG sources, including rice cultivation, enteric fermentation in livestock, and synthetic fertilisers and manure management. This research reveals the complex array of obstacles that are faced in the pursuit of reducing emissions and also investigates novel approaches to tackling them. This encompasses the implementation of monitoring systems powered by artificial intelligence, which have the capacity to fundamentally transform initiatives aimed at reducing emissions. Carbon capture technologies, another area investigated in this study, exhibit potential in further reducing GHGs. Sophisticated technologies, such as precision agriculture and the integration of renewable energy sources, can concurrently mitigate emissions and augment agricultural output. Conservation agriculture and agroforestry, among other sustainable agricultural practices, have the potential to facilitate emission reduction and enhance environmental stewardship. The paper emphasises the significance of financial incentives and policy frameworks that are conducive to the adoption of sustainable technologies and practices. This exhaustive evaluation provides a strategic plan for the agriculture industry to become more environmentally conscious and sustainable. Agriculture can significantly contribute to climate change mitigation and the promotion of a sustainable future by adopting a comprehensive approach that incorporates policy changes, technological advancements, and technological innovations.

Towards the synthesis of spectral imaging and machine learning-based approaches for non-invasive phenotyping of plants.

High-throughput phenotyping is now central to the progress of plant sciences, accelerated breeding, and precision farming. The power of phenotyping comes from the automated, rapid, non-invasive collection of large datasets describing plant objects. In this context, the goal of extracting relevant information from different kinds of images is of paramount importance. We review both the spectral and machine learning-based approaches to imaging of plants for the purpose of their phenotyping. The advantages and drawbacks of both approaches will be discussed with a focus on the monitoring of plants. We argue that an emerging approach combining the strengths of the spectral and the machine learning-based approaches will remain a promising direction in plant phenotyping in the nearest future.

Nitrogen deficiency in maize: Annotated image classification dataset.

Nitrogen (N) is one of the key inputs in maize production applied in the form of fertilizers. Nitrogen deficiency during the vegetation period leads to lower yields since N is utilized in proteins and enzymes that enable important biochemical processes such as photosynthesis. Nitrogen deficiency leads to specific symptoms that eventually become visible to the naked eye during vegetation. Our hypothesis was that N deficiency can be detected from maize RGB images in parametric process such as a deep neural network. The aim of the reported dataset is to optimize the usage of N in the farmer's fields and accordingly, reduce its environmental footprint. This dataset contains 1200 images of maize canopy from field trials, annotated by an expert from an agricultural institution. The field trials included three levels of N fertilization: N0 without N fertilization, N75 with 75 kg of added N fertilizer, and NFull with 136 kg of added N fertilizer. For each fertilizer level, 400 plots were created with 238 different maize genotypes, resulting in a total of 1200 plots. Images were taken with a tripod mounted DSLR camera, aperture priority set to f/8 and sensor sensitivity set to ISO400. Images were taken at a 45° angle to each plot. This dataset can be useful to both researchers, data scientists and agronomists, especially in the context of emerging technologies in precision agriculture, such as robotics, 5G networks and unmanned aerial vehicle (UAV). The dataset is one of the first publicly accessible datasets of maize canopy images under different N fertilization levels and represents a valuable public resource for development of machine learning models for in-season detection of N deficiency in maize.

Identifying Early-Life Behavior to Predict Mothering Ability in Swine Utilizing NUtrack System.

Early indicator traits for swine reproduction and longevity support economical selection decision-making. Activity is a key variable impacting a sow's herd life and productivity. Early-life activities could contribute to farrowing traits including gestation length (GL), number born alive (NBA), and number weaned (NW). Beginning at 20 weeks of age, 480 gilts were video recorded for 7 consecutive days and processed using the NUtrack system. Activity traits included angle rotated (radians), average speed (m/s), distance traveled (m), time spent eating (s), lying lateral (s), lying sternal (s), standing (s), and sitting (s). Final daily activity values were averaged across the period under cameras. Parity one data were collected for all gilts considered. Data were analyzed using linear regression models (R version 4.0.2). GL was significantly impacted by angle rotated (p = 0.03), average speed (p = 0.07), distance traveled (p = 0.05), time spent lying lateral (p = 0.003), and lying sternal (0.02). NBA was significantly impacted by time spent lying lateral (p = 0.01), lying sternal (p = 0.07), and time spent sitting (p = 0.08). NW was significantly impacted by time spent eating (p = 0.09), time spent lying lateral (p = 0.04), and time spent sitting (p = 0.007). This analysis suggests early-life gilt activities are associated with sow productivity traits of importance. Further examination of the link between behaviors compiled utilizing NUtrack and reproductive traits is necessitated to further isolate behavioral differences for potential use in selection decisions.

Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review.

Digital Twins serve as virtual counterparts, replicating the characteristics and functionalities of tangible objects, processes, or systems within the digital space, leveraging their capability to simulate and forecast real-world behavior. They have found valuable applications in smart farming, facilitating a comprehensive virtual replica of a farm that encompasses vital aspects such as crop cultivation, soil composition, and prevailing weather conditions. By amalgamating data from diverse sources, including soil, plants condition, environmental sensor networks, meteorological predictions, and high-resolution UAV and Satellite imagery, farmers gain access to dynamic and up-to-date visualization of their agricultural domains empowering them to make well-informed and timely choices concerning critical aspects like efficient irrigation plans, optimal fertilization methods, and effective pest management strategies, enhancing overall farm productivity and sustainability. This research paper aims to present a comprehensive overview of the contemporary state of research on digital twins in smart farming, including crop modelling, precision agriculture, and associated technologies, while exploring their potential applications and their impact on agricultural practices, addressing the challenges and limitations such as data privacy concerns, the need for high-quality data for accurate simulations and predictions, and the complexity of integrating multiple data sources. Lastly, the paper explores the prospects of digital twins in agriculture, highlighting potential avenues for future research and advancement in this domain.

WeedNet-R: a sugar beet field weed detection algorithm based on enhanced RetinaNet and context semantic fusion.

Accurate and dependable weed detection technology is a prerequisite for weed control robots to do autonomous weeding. Due to the complexity of the farmland environment and the resemblance between crops and weeds, detecting weeds in the field under natural settings is a difficult task. Existing deep learning-based weed detection approaches often suffer from issues such as monotonous detection scene, lack of picture samples and location information for detected items, low detection accuracy, etc. as compared to conventional weed detection methods. To address these issues, WeedNet-R, a vision-based network for weed identification and localization in sugar beet fields, is proposed. WeedNet-R adds numerous context modules to RetinaNet's neck in order to combine context information from many feature maps and so expand the effective receptive fields of the entire network. During model training, meantime, a learning rate adjustment method combining an untuned exponential warmup schedule and cosine annealing technique is implemented. As a result, the suggested method for weed detection is more accurate without requiring a considerable increase in model parameters. The WeedNet-R was trained and assessed using the OD-SugarBeets dataset, which is enhanced by manually adding the bounding box labels based on the publicly available agricultural dataset, i.e. SugarBeet2016. Compared to the original RetinaNet, the mAP of the proposed WeedNet-R increased in the weed detection job in sugar beet fields by 4.65% to 92.30%. WeedNet-R's average precision for weed and sugar beet is 85.70% and 98.89%, respectively. WeedNet-R outperforms other sophisticated object detection algorithms in terms of detection accuracy while matching other single-stage detectors in terms of detection speed.

The Relationship between Reticuloruminal Temperature, Reticuloruminal pH, Cow Activity, and Clinical Mastitis in Dairy Cows.

We hypothesized that reticuloruminal temperature, pH as well as cow activity can be used as parameters for the early diagnosis of clinical mastitis in dairy cows. Therefore, we aimed to detect the relationship between these factors and the disease. We randomly selected cows with clinical mastitis and clinically healthy cows (HG) out of 600 milking cows. We recorded the following parameters during the experiment: reticulorumen temperature (RR temp.), reticulorumen pH (RR pH), and cow activity. We used smaXtec boluses (smaXtec animal care technology®, Graz, Austria). In this investigation, reticulorumen data obtained seven days before diagnosis were compared to HG data from the same time period. CM cows were observed on the same days as the healthy cows. The healthy group's RR pH was 7.32% higher than that of cows with CM. Reticulorumen temperature was also 1.25% higher in the CM group than in the control group. The healthy group had a higher average value for walking activity, which was 17.37% higher than the CM group. The data of reticulorumen pH changes during 24 h showed that during the day, the pH changed from 5.53 to 5.83 in the CM group. By contrast, pH changed from 6.05 to 6.31 in the control group. The lowest reticulorumen pH in the CM group was detected on the third day before diagnosis, which was 15.76% lower than the highest reticulorumen pH detected on the sixth day before diagnosis. The lowest reticulorumen pH in CM cows was detected at 0 and 1 days before diagnosis and it was 1.45% lower than the highest reticulorumen pH detected on the second day before diagnosis. The lowest walking activity in the CM group was detected 0 days before diagnosis, which was 50.60% lower than on the fifth day before diagnosis. Overall, the results confirmed our hypothesis that reticuloruminal temperature, reticuloruminal pH, and cow activity could be used as parameters for the early diagnosis of clinical mastitis in dairy cows.

Dairy Cow Mastitis Detection by Thermal Infrared Images Based on CLE-UNet.

Thermal infrared technology is utilized for detecting mastitis in cows owing to its non-invasive and efficient characteristics. However, the presence of surrounding regions and obstacles can impede accurate temperature measurement, thereby compromising the effectiveness of dairy mastitis detection. To address these problems, we proposed the CLE-UNet (Centroid Loss Ellipticization UNet) semantic segmentation algorithm. The algorithm consists of three main parts. Firstly, we introduced the efficient channel attention (ECA) mechanism in the feature extraction layer of UNet to improve the segmentation accuracy by focusing on more useful channel features. Secondly, we proposed a new centroid loss function to facilitate the network's output to be closer to the position of the real label during the training process. Finally, we used a cow's eye ellipse fitting operation based on the similarity between the shape of the cow's eye and the ellipse. The results indicated that the CLE-UNet model obtained a mean intersection over union (MIoU) of 89.32% and an average segmentation speed of 0.049 s per frame. Compared to somatic cell count (SCC), this method achieved an accuracy, sensitivity, and F1 value of 86.67%, 82.35%, and 87.5%, respectively, for detecting mastitis in dairy cows. In conclusion, the innovative use of the CLE-UNet algorithm has significantly improved the segmentation accuracy and has proven to be an effective tool for accurately detecting cow mastitis.

Environmental hazards of nanopesticides to non-target soil species - commercial nanoformulation versus its active substance (Karate Zeon® and lambda-cyhalothrin).

Nanopesticides (Npes) carry the potential of increased efficacy while reducing application rates, hence increasing agricultural productivity in a more sustainable way. However, given its novelty, the environmental risk assessment of these advanced materials is mostly absent. In the present study we investigated the ecotoxicity of a commercial insecticide, with reported nanofeatures, Karate Zeon®, and compared it to its active substance lambda-cyhalothrin. It is hypothesised that the use of the nanopesticide Karate Zeon® poses lower risk to enchytraeids than its active substance. The standard non-target soil invertebrate Enchytraeus crypticus was used, and exposure was done in LUFA 2.2 soil in 4 tests (endpoints: days): avoidance test [avoidance behaviour: 2 days], OECD standard reproduction test [survival, reproduction plus adults' size: 28 days] and its extension [total number organisms: 56 days], and Full Life Cycle (FLC) test [hatching and juveniles' size: 13 days; survival, reproduction and adults' size: 46 days]. Results showed that enchytraeids did not avoid Karate Zeon® nor its active substance lambda-cyhalothrin, which could be due to neurotoxicity. There was no indication of increased toxicity with prolonged exposure (46, 56d) compared to the standard (28d) for neither of the materials, being overall equally toxic in terms of hatching, survival, and reproduction. The FLCt results indicated that the juvenile stage was the most sensitive, resulting in higher toxicity for the adult animals when exposed from the cocoon stage. Although toxicity was similar between Karate Zeon and lambda-cyhalothrin, different patterns of uptake and elimination cannot be excluded. The benefits of using Karate Zeon will rely on reduced application rates.

Automatic detection of brown hens in cage-free houses with deep learning methods.

Computer vision technologies have been tested to monitor animals' behaviors and performance. High stocking density and small body size of chickens such as broiler and cage-free layers make effective automated monitoring quite challenging. Therefore, it is critical to improve the accuracy and robustness of laying hens clustering detection. In this study, we established a laying hens detection model YOLOv5-C3CBAM-BiFPN, and tested its performance in detecting birds on open litter. The model consists of 3 parts: 1) the basic YOLOv5 model for feature extraction and target detection of laying hens; 2) the convolution block attention module integrated with C3 module (C3CBAM) to improve the detection effect of targets and occluded targets; and 3) bidirectional feature pyramid network (BiFPN), which is used to enhance the transmission of feature information between different network layers and improve the accuracy of the algorithm. In order to better evaluate the effectiveness of the new model, a total of 720 images containing different numbers of laying hens were selected to construct complex datasets with different occlusion degrees and densities. In addition, this paper also compared the proposed model with a YOLOv5 model that combined other attention mechanisms. The test results show that the improved model YOLOv5-C3CBAM-BiFPN achieved a precision of 98.2%, a recall of 92.9%, a mAP (IoU = 0.5) of 96.7%, a classification rate 156.3 f/s (frames per second), and a F1 (F1 score) of 95.4%. In other words, the laying hen detection method based on deep learning proposed in the present study has excellent performance, can identify the target accurately and quickly, and can be applied to real-time detection of laying hens in real-world production environment.

Delivery of Nematicides Using TMGMV-Derived Spherical Nanoparticles.

Spherical nanoparticles (SNPs) from tobacco mild green mosaic virus (TMGMV) were developed and characterized, and their application for agrochemical delivery was demonstrated. Specifically, we set out to develop a platform for pesticide delivery targeting nematodes in the rhizosphere. SNPs were obtained by thermal shape-switching of the TMGMV. We demonstrated that cargo can be loaded into the SNPs during thermal shape-switching, enabling the one-pot synthesis of functionalized nanocarriers. Cyanine 5 and ivermectin were encapsulated into SNPs to achieve 10% mass loading. SNPs demonstrated good mobility and soil retention slightly higher than that of TMGMV rods. Ivermectin delivery to Caenorhabditis elegans using SNPs was determined after passing the formulations through soil. Using a gel burrowing assay, we demonstrate the potent efficacy of SNP-delivered ivermectin against nematodes. Like many pesticides, free ivermectin is adsorbed in the soil and did not show efficacy. The SNP nanotechnology offers good soil mobility and a platform technology for pesticide delivery to the rhizosphere.

Time of insemination in relation to pregnancy rates in beef cattle after oestrus detection with automated activity monitoring system.

BACKGROUND: The timing of artificial insemination is critical to achieve acceptable results in cattle production systems. Over the past 60 years the length and expression of oestrus in dairy cattle has altered. Recent studies have indicated the optimal timing for insemination after the commencement of oestrus may now be earlier than traditional recommendations in beef cattle, as is the case in dairy cattle. The aim of the current study was to evaluate the effect of time from onset of oestrus [as determined by an automated activity monitoring system (AAMS)] to artificial insemination (AI) on pregnancy outcome in Norwegian beef cattle. Five commercial beef suckler herds participated in a cohort study by providing data on the time of AAMS alarm and time of AI. Blood sampling on the day of AI was performed and serum progesterone concentration measured. Pregnancy detection was performed by transrectal ultrasonography and aging of the fetus performed when necessary. A mixed logistic regression model was fitted to study the effect of time from AAMS alarm to AI on pregnancy outcome. Time categories used in the model were < 12 h, 12-24 h, and > 24 h. RESULTS: AI periods (n = 229) with serum progesterone concentration < 1 ng/mL were available for analysis. Overall pregnancy risk per AI for the whole study period was 65.5%, with an inter-herd variation from 10 to 91%. Median time elapsed from AAMS alarm to AI was 17.75 h. Herd affected pregnancy outcome (P = 0.001), while breed and parity status (heifer/cow) did not. The time category closer to AAMS alarm 0-12 h showed a numerically lower pregnancy risk as compared to the baseline group which had AI 12-24 h after onset of oestrus. CONCLUSION: This study found no evidence to support a change in the recommended timing of AI in beef suckler cows.

Advancement in artificial intelligence for on-farm fruit sorting and transportation.

On-farm sorting and transportation of postharvest fruit include sorting out defective products, grading them into categories based on quality, distributing them into bins, and carrying bins to field collecting stations. Advances in artificial intelligence (AI) can speed up on-farm sorting and transportation with high accuracy and robustness and significantly reduce postharvest losses. The primary objective of this literature review is to provide an overview to present a critical analysis and identify the challenges and opportunities of AI applications for on-farm sorting and transportation, with a focus on fruit. The challenges of on-farm sorting and transportation were discussed to specify the role of AI. Sensors and techniques for data acquisition were investigated to illustrate the tasks that AI models have addressed for on-farm sorting and transportation. AI models proposed in previous studies were compared to investigate the adequate approaches for on-farm sorting and transportation. Finally, the advantages and limitations of utilizing AI have been discussed, and in-depth analysis has been provided to identify future research directions. We anticipate that this survey will pave the way for further studies on the implementation of automated systems for on-farm fruit sorting and transportation.