<div style="text-align: justify;">&ldquo;Mastitis&rdquo; is one of the major challenges for dairy industry, worldwide, as this is most common and costliest disease of dairy animals and contributes a substantial economical loss to dairy farmers. Subclinical stage of mastitis i.e. Sub-clinical mastitis (SCM) in dairy cow is matter of great concern for farmers as its incidence is more as compared to clinical form. In most of animal farms, monitoring of sub-clinical and clinical mastitis (CM) is usually performed through indirect test such as pH, electrical conductivity, somatic cell count (SCC), California mastitis test (CMT), culture test and biomarker tests. But, apart from these, there is a need of new rapid and sensitive technology to identify udder infections at early stages. Early detection of mastitis using non-invasive technology is need of the hour to reduce the economic loss of the dairy industry and farmers. It is well evident that, skin surface temperature is a critical indicator of bio-physiological health status of an organism. As a result of infection-induced inflammatory reactions, local blood circulation, metabolism and skin surface temperature increases. Thus, by monitoring the emitted heat from udder, can help in early detection of mastitis. Moreover, Infrared thermography (IRT) using highly sensitive thermal camera is able to monitor subtle change in skin surface temperature. IRT with mobile based application can further play an important role in the management of dairy farms on various aspects.&nbsp;</div>

Infrared thermography as non-invasive technique for early detection of mastitis in dairy animals-A review

<div style="text-align:justify">The main objective of the current experiment was to assess the seasonal variation on seminal attributes in 109 Murrah buffalo (Bubalus bubalis) bulls using infrared thermography, maintained at commercial semen production stations with ideal nutrition and management conditions in Karnatka (south India). Data were collected on 6 ejaculates in three different seasons through &nbsp;a year with different age groups of bulls. Infrared thermograms and data were collected using an infrared FLIR i5 camera and analyzed by Quick report 1.2 SP2 softwere to determine the temperature gradient (TG, difference between the PPT and DPT) scrotal surface and Ocular temperature. Immediately after semen collection by AV method. Semen evaluation were performed twice per season throughout the year. The ejaculates were subjected to general linear model (GLM) analysis to determine the effect of various non-genetic factors viz., season (summer, rainy and winter), age (&lt;4, and &gt;4 year), housing (group and individual housing) on semen quality attributes of Murrah bull. The overall least square mean volume (VOL), sperm concentration (SCON&times;10v /ml), initial motility (IM), percent live spermatozoa (LIVE), acrosome integrity (AI), plasma membrane integrity (PMI), head abnormalities (HAN), midpiece abnormalities &nbsp;(MPAN), tail abnormalities (TAN) in fresh semen whereas post-thaw motility (PTM), acrosome integrity (AIPT) and plasma membrane integrity in post-thawed semen (PMIPT) were 3.88&plusmn;0.07 mL, 1041&plusmn;14.64 million/mL, 64.62&plusmn;0.50 %, 76.42&plusmn;0.51 %, 66.56&plusmn;0.48 %, 2.90&plusmn;0.05 %, 3.35&plusmn;0.05 %, 5.88&plusmn;0.10 %, 68.39&plusmn;0.47 %, 41.48&plusmn;0.55 % and 61.66&plusmn;0.47 %, respectively.The statistical analysis of variance revealed significant effect (P&lt;0.05) of season on semen quality attributes viz., VOL, SCON, IM, PTM, LIVE, HAN, MPAN, TAN, AI, AIPT, PMI and PMIPT. Age had significant effect on &nbsp;VOL, IM, LIVE, HAN, MPAN and &nbsp;house on SCON, PTM, AIPT and PMIPT.Thus, it may be concluded that the summer season adversely affected the various bio - physical characteristics of semen in Murrah buffalo bulls. Rainy season was the most favourable season for good quality semen production and the winter season might be considered as the intermediate between the two extremes.</div>

<div style="text-align:justify">Digital infrared thermography as a tool to assess the effect of season, age and housing on seminal traits in Murrah bulls</div>

<div style="text-align: justify;">The present investigation was carried out to study the effect of testicular covering thickness, scrotal circumference and scrotal surface temperature gradient insert semen quality attributes of Karan Fries breeding bull. Six ejaculates were collected from each randomly selected bulls (n=15), and seminal quality attributes were evaluated during peak winter season. Same time scrotal surface temperature gradient (SSTG), the thickness of testicular covering (TTC) and scrotal circumference (SC) of individual bulls were recorded using DarviDTL007 infrared thermography camera, ultrasonography and measuring tape, respectively. Three groups were formed based on &nbsp;SSTG (Group I- &pound; 4 &ordm;C, Group II- 4.1 to 6.8 &ordm;C and Group III- &sup3;6.9 &ordm;C), two groups based on &nbsp;TTC (Group I- 3.03 to 4.85 and Group II- 4.86 to 7.03 mm) and SC (Group I- 32 to 35 and Group II- 36 to 41 cm). Ejaculate rejection rate (ERR) was significantly decreased with the increase of SSTG and SC. ERR was not affected with TTC. The increase of SSTG (from GroupI to Group III), mass activity and non-eosinophilic sperm count were significantly increased, and sperm abnormalities were significantly (p&lt;0.01) decreased. Similarly, with the increase of SSTG and abnormalities were significantly (p&lt;0.01) increased among different groups. Ejaculate volume and non-eosinophilic sperm count were significantly (p&lt;0.01) increased with SC group I and group II. It can be concluded that scrotal surface temperature gradient was a better indicator of semen quality as compared to the thickness of testicular covering and bulls having a higher scrotal surface temperature gradient, scrotal circumference and lower thickness of testicular covering produced better quality semen.</div>

<div style="text-align: justify;">Use of scrotal infrared thermography and ultrasonography to understand thermoregulation of testis and sperm quality in Karan Fries breeding bull</div>

The main aim of this study was to determine the role of Infrared lamps in cold stress alleviation during winter in Murrah calves. Ten newborn calves were randomly divided into two groups (G1 and G2) of five each. The calves of G1 were provided with no additional protection; however calves of G2 were protected against cold weather by using Infrared lamps. The body weight (kg) of calves was recorded at weekly interval. The blood samples collected at fortnightly interval were analyzed for Packed Cell volume (PCV, %), hemoglobin (Hb, g/dl), Total Serum Protein (TSP, g/l), albumin (g/l), globulin (g/l) and albumin globulin (A:G) ratio and hormones viz., triiodothyronine (T3, ng/ml), thyroxine (T4, ng/ml) and cortisol (ng/ml). The total body weight gain and average daily gain (ADG) was significantly (P&lt;0.01) higher in G2 as compared to G1. The PCV values were significantly (P&lt;0.05) higher on day 15 and day 45 in G1 than G2. The albumin and A:G Ratio were significantly (P&lt;0.05) higher on day 60 and 45, respectively in G1 than G2. The cortisol levels were higher in G1 than G2 and differences were highly significant (P&lt;0.01) on 15th day and significant (P&lt;0.05) on 45th day. Significantly (P&lt;0.01) higher values of &nbsp;T3 and T4 were observed on 15th and 45th day in G1 as compared to G2. On the basis of the results, it may be concluded that Infrared lamps can be effectively used to protect newborn calves from adverse conditions of winter and to improve their body growth performance. &nbsp;

Role of Infrared lamps in cold stress alleviation during winter in Murrah calves

Background: Rapid prediction with near infrared (NIR) spectroscopy on quality traits is pretty popular recently, for the convenience and simple operation. But to make good use of this technology, precise and suitable calibration equations are very important to get dependable result. In this study, we mostly refer to the building of the equation and how the pretreatment effect them.&nbsp;&nbsp; 
 
Methods: In this paper, near infrared (NIR) spectroscopy was used to simultaneously predict the quality traits of soybean, including oil content, protein content, oleic acid content, linoleic acid content, stearic acid content. Near infrared spectral data of a total of 112 samples is collected from given materials in Chongqing. Samples were scanned from 1000 nm to 2500 nm using a monochromator instrument (SuperNIR-2700). Calibration equations were developed from NIR data using partial least squares (PLS) regression with internal cross validation. In addition, in this study, we also cover the affection of different pre-treatments to the different calibration equations predicting different quality traits. And measure the effect with three indicators including R, SECV and RPD.&nbsp; 
 
Result: Eventually we find the most suitable combination of pre-treatments for each calibration equation predicting a certain trait soybean. The present study would lay the foundations of rapid detection of quality traits in soybean.

Establishment of Detection Model of Soybean Quality Traits by Near Infrared Spectroscopy

<div>Background: Forecasting bud physiologic conditions can help &lsquo;Fuji&rsquo; apple farmers manage their orchards more efficiently. Being able to determine the nature of a bud before bud burst is one such forecast that could be of use to these &lsquo;Fuji&rsquo; growers. The aim of this research project was to determine if a device, a visible/near-infrared spectrometer, could be employed to determine whether a bud is a flower or non-flower bud without destroying the bud.&nbsp; 
 
Methods: Experiments were conducted on buds taken from a &lsquo;Fuji&rsquo; apple tree, beginning on January 29 through to March 31, 2021, three days before bud burst. The data from the visible/near-infrared spectrometer clarified whether a bud was a flower or a non-flower bud. The Spectro data Classification Learner App proved to be an accurate classification method to analyze flower and non-flower bud Spectro data. 
 
Result: Three days before bud burst, the chlorophyll content levels of the non-flower buds were markedly higher (P&le;0.05) than those of the flower bud, which explains why the visible border of the near-infrared spectrometer might have been changed by the chlorophyll content of buds. The visible and near-infrared bands of the buds showed that the Spectro data of the non-flower buds were higher than those of the flower buds when measurements were made three days before bud burst. Three days before bud burst Cubic KNN of KNN classifier analyzed flower and non-flower buds smoothly. Spectro data were labeled as accuracy 75.9%, sensitivity 86% and specificity 67%. The results that were obtained suggest that farmers could use a visible/near-infrared spectrometer to identify flower and non-flower buds in their orchards, without damaging the buds, three days before bud burst.</div>

Application of a Visible/Near-infrared Spectrometer in Identifying Flower and Non-flower Buds on &lsquo;Fuji&rsquo; Apple Trees

<div>Background: Chickpea is the third major pulse produced globally, with 11.6 million tonnes produced per annum (Merga and Haji, 2019).&nbsp;Sugar alcohols, inulin, starch are all prebiotic carbohydrates found in chickpeas (Johnson et al., 2020).&nbsp;Near-Infrared (NIR) spectroscopy is a non-destructive, versatile and powerful analytical technique. 
&nbsp;&nbsp; 
Methods: Spectral data obtained from NIR spectroscopy requires application of various techniques to extract useful information from spectral data which is further used for building various models for prediction of physical or chemical components presents in agricultural crops. The main aim of this study is to apply various machine learning algorithms especially effective in predicting sugar concentration in chickpea. Sugar prediction models are developed using Linear Regression (LR), Artificial Neural Network (ANN), Random Forest (RF), Support Vector Regression (SVR) and Decision Tree Regression (DTR) algorithms. Performance of the models is evaluated using measures namely, Root Mean Square Error (RMSE), Residual Standard Error (RSE), Coefficient of Determination (R2) and Adjusted Coefficient of Determination (adjusted R2). 
 
Result: It was observed that, RF outperformed all other models in terms of accuracy for predicting sugar component from preprocessed spectra, with RMSE, RSE, R2 and adjusted R2 values of 0.054, 0.062, 0.954 and 0.937, respectively. The accuracy of the ANN model is similar to that of the RF, with minor differences in RMSE, RSE, R2 and adjusted R2 , values of 0.057, 0.067, 0.952 and 0.935.</div>

Comparing Various Machine Learning Algorithms for Sugar Prediction in Chickpea using Near-infrared Spectroscopy

<div>Background: Rice bran is the most valuable by-product of the rice milling operation. The FFA level rises immediately after milling and bran oil becomes hazardous for consumption owing to its lower pH, rancid flavor and soapy taste.&nbsp; 
 
Methods: A continuous-type infrared rice bran stabilizer was designed and process parameters such as moisture content, thickness, power density and treatment time were optimized for their impact on FFA content of bran, capacity and energy demand of the stabilizer using response surface methodologies. Under constant power density and moisture content, it has been confirmed that the capacity decreases with infrared exposure time.&nbsp; 
 
Result: The capacity was minimum (6.7 kg/h) at 0.5 cm thickness and 5 min exposer times, whereas it was maximum (40.2 kg/h) at 1 cm bed thickness and 3 min exposer time. The energy demand rose as the power density and time of exposure increased while the bed thickness and moisture content remained constant, whereas the FFA lowered nonlinearly as the power density and exposure time increased. FFA content was shown to be low at lower bed thickness and moisture content and to increase slowly as thickness and moisture content increased to 0.8 cm and 15% (w.b), respectively. However, when bed thickness and moisture level increase further, FFA content raised dramatically.</div>

Process Parameters Optimization for Continuous Infrared Rice Bran Stabilizer and Assessment of Their Impact on Quality and Shelf Life

Background: Wheat quality and safety are crucial for global food security and regional agricultural economies. Conventional analytical methods for assessing grain composition and quality-while accurate-are often time-consuming, destructive and resource-intensive. In contrast, near-infrared spectroscopy (NIR) has emerged as a rapid, non-destructive alternative that provides detailed chemical and physical insights. Recent advancements in machine learning have enhanced NIR&rsquo;s analytical capabilities by enabling the development of predictive models that accurately categorize, quantify and monitor grain quality parameters. These models-spanning from linear regression methods to intricate neural networks-enable researchers to discern significant patterns from spectral data, even amidst noise or overlapping signals. 
&nbsp; 
Methods: This study used NIR spectroscopy and machine learning models to evaluate wheat quality across multiple Albanian regions during the 2023 harvest. NIR spectra were preprocessed using standard normalization and smoothing techniques. Machine learning models-including PLSR, SVM and random forest-were trained to predict key quality traits such as protein content and moisture. Model performance was validated using cross-validation metrics (RMSE, R&sup2;, accuracy), demonstrating the potential of NIR-ML integration for rapid, non-destructive grain assessment. 
&nbsp; 
Result: Protein content ranged from 9.6% to 15.0%, gluten from 19.4% to 37.5% and starch from 66.3% to 71.5%, indicating consistent nutritional profiles across samples. Sedimentation index values (21.1-57.8 cm&sup3;) and moisture levels (9.8-12.5%) confirmed compliance with EU food safety standards. The integration of NIR spectroscopy with machine learning enabled rapid, non-destructive prediction of wheat quality traits, supporting scalable decision-making in agri-food systems.

Applications of Machine Learning Models in Near-infrared Spectroscopy for Small-grain Quality Control

Background: This study aimed to develop and evaluate a rapid, non-destructive method for predicting phosphorus content in chickpea flour using near-infrared (NIR) spectroscopy combined with machine learning techniques. The research sought to identify the most effective wavelength range and modeling approach for accurate phosphorus content estimation. 
&nbsp; 
Methods: NIR spectra was collected from 237 chickpea flour samples, of which 132 showed detectable phosphorus reflectance. The dataset underwent preprocessing techniques including noise elimination, data reduction and scatter correction. The interval Partial Least Squares (iPLS) algorithm was employed to identify the optimal wavelength range for phosphorus prediction. Five machine learning models-linear regression (LR), support vector regression (SVR), random forest (RF), decision tree regression (DTR) and neural network (NN)-were developed and compared using a dataset split into calibration (80%) and validation (20%) sets. Principal component analysis (PCA) was used for dimensionality reduction. 
&nbsp; 
Result: The most effective wavelength range for phosphorus prediction was identified as 1520-1658 nm. The Linear Regression model demonstrated the best performance on the testing data, with an R&sup2; of 0.945, Root Mean Square Error (RMSE) of 0.044 and residual standard error (RSE) of 0.045. All models except RF and DTR showed excellent predictive capability (RPD&gt;4.1). The Decision Tree Regression model performed best on the validation set, with an R&sup2; of 0.875 and RMSE of 0.079. The study confirmed the potential of NIR spectroscopy combined with machine learning as a rapid and accurate method for phosphorus content prediction in chickpea flour.

Infrared thermography as non-invasive technique for early detection of mastitis in dairy animals-A review

Digital infrared thermography as a tool to assess the effect of season, age and housing on seminal traits in Murrah bulls

Use of scrotal infrared thermography and ultrasonography to understand thermoregulation of testis and sperm quality in Karan Fries breeding bull

Role of Infrared lamps in cold stress alleviation during winter in Murrah calves

Establishment of Detection Model of Soybean Quality Traits by Near Infrared Spectroscopy

Application of a Visible/Near-infrared Spectrometer in Identifying Flower and Non-flower Buds on ‘Fuji’ Apple Trees

Comparing Various Machine Learning Algorithms for Sugar Prediction in Chickpea using Near-infrared Spectroscopy

Process Parameters Optimization for Continuous Infrared Rice Bran Stabilizer and Assessment of Their Impact on Quality and Shelf Life

Applications of Machine Learning Models in Near-infrared Spectroscopy for Small-grain Quality Control

Analysis of Phosphorus Content in Chickpea Samples using Near-infrared Spectroscopy and Machine Learning Models