<div>Background: Agriculture plays a significant role in economic and social development in India. With the rapid development of Information and Communication Technologies, information and data can be effectively generated, stored and used by farmers to improve agricultural productivity. For this, smart farming technologies using mobile applications (apps) that help reduce costs, maximize yields and increase profits are being employed. Here, we present an overview of several mobile apps available for traceability in agricultural sectors, discuss their features, functions and how they are lacking in some domains. 
 
Methods: In this study during Aug 2019-Sep 2019, several online databases were used for the survey according to the guidelines for Transparent Reporting of Systematic Reviews and Meta-Analyses. 73 agriculture mobile apps were selected based upon the hits using keywords like &ldquo;mobile apps, agriculture mobile app, animal husbandry, etc. and with the inclusion criteria of a good review and proper up to date information. These apps were found to be used in different agriculture allied sectors in India. 
 
Result: Among these 73 mobile apps used by Indian farmers in various agricultural sectors viz. farm management, fisheries, poultry, livestock and animal husbandry, food traceability and pure agriculture sectors; the respective availability percentage of apps were 12, 14, 14, 23, 23 and 14%. These apps are discussed here in detail along with their gap analysis and a new traceability mechanism has been proposed as well.&nbsp;</div>

<div>Agricultural Mobile Apps used in India: Current Status and Gap Analysis</div>

<div>Background: Joint health is very vital in canines. Immediate attention and diagnosis will help to prevent suffering in canines. This study was designed to evaluate the in vivo efficacy of the poly-herbal formulation Mobility Plus&reg; for arthritis, inflammation and analgesic activities in canines. 
 
Methods: A total of 18 client-owned dogs with a history of joint inflammation, hip dysplasia and arthritis were selected (n = 18) and supplemented with one tablet of Mobility Plus&reg; daily until complete recovery. The changes in assessment parameters scores, viz. lameness score, joint mobility score, pain on palpation score, weight-bearing score and the overall clinical condition score were evaluated. The results revealed that lameness, joint mobility, pain and weight-bearing scores significantly (p&lt;0.001) decreased in dogs as early as day 15; hence, the overall clinical condition score also decreased significantly (p&lt;0.001) as early as day 15 after Mobility Plus&reg; supplementation. 
 
Result: Present study revealed that lameness, joint mobility, pain and weight-bearing scores significantly (p and lt; 0.001) decreased in dogs as early as day 15; hence, the overall clinical condition score also decreased significantly (p and lt; 0.001) as early as day 15 after Mobility Plus&reg; supplementation. Supplementation of Mobility Plus&reg; has antiarthritic and anti-inflammatory activities. Mobility Plus&reg; could be recommended for the amelioration of joint inflammation and osteoarthritis conditions in canines.</div>

Evaluation of the Efficacy of Mobility Plus&reg; in the Management of Osteoarthritis and Joint Inflammation in Canines

<div>Background: Simple Sequence Repeat (SSR) markers are essential for molecular breeding in pigeonpea (Cajanus cajan L.) compared to traditional development methods, which exhibit 30-70% marker failure rates. Moreover,&nbsp; no quantitative frameworks exist for predicting marker quality in this orphan legume crop. Therefore, the objective of this study was to develop the first machine learning framework for automated SSR marker quality prediction in pigeonpea and identify key determinants of marker success. 
&nbsp; 
Methods: Fifteen predictive features were engineered features from 2,770 computationally designed pigeonpea SSR markers, including primer design parameters, compatibility metrics and SSR structural characteristics. Quality scores (0-100) integrated predicted amplification success, specificity and polymorphism likelihood. Three algorithms (Random forest, support vector regression, neural network) were trained using five-fold cross-validation. 
&nbsp; 
Result: Support Vector Regression (SVR) achieved the best performance (R&sup2; = 0.487, MAE = 2.341, CV R&sup2; = 0.441&plusmn; 0.032). Feature importance analysis revealed primer compatibility as the dominant predictor (72.9% combined importance): melting temperature difference (25.2%), average melting temperature (23.0%) and primer length difference (17.8%). The framework enables prioritization of top 554 markers (20%), potentially reducing experimental validation costs by 40-60%. This study presents the first quantitative framework for SSR marker quality assessment in pigeonpea. The finding that primer pair compatibility accounts for 72.9% of predictive importance fundamentally shifts understanding from individual primer optimization to primer pair harmony. This validated framework provides scalable methodology for resource-limited breeding programs and is transferable to other orphan legume species.</div>

Machine Learning Framework for Automated Simple Sequence Repeat (SSR) Marker Quality Assessment in Pigeonpea (Cajanus cajan L.)&nbsp;

<div style="text-align:justify">Background: Climate change is emerging as a major threat on agriculture, food security and livelihood of millions of people in many places of the world. Agromet advisories services is such a climate resilient technology which provides the valuable information about all agricultural operations from land preparation sowing to harvest based on weather forecasting. There have been plenty of studies investigating farmers&rsquo; decision to adopt an innovation but a lot of more segregated studies exist, highlighting the importance of individual factors affecting adoption. Most of the theoretical models on adoption of innovation have tended to present discipline guided explanations to the adoption decision, although adoption is subject to a combination of social, economic, psychological, as well as cultural factors. 
Methods: The aim of the paper is to present a framework to study farmer&rsquo;s decision with respect to adoption of AAS. Under aegis of NICRA, Agromet Advisories are provided to the farmers through its 121 Krishi Vigyan Kendra located in India. The agromet advsiories along with weather data gives crop specific recommendations. Generally, these advisories ae given to farmers two times in a week. The developed framework will be used to study their intention to adopt these AAS. Extensive review of literature on the adoption of innovation in agriculture that was based on TPB or theory of reasoned change (TRA) was carried out. 
Result: Various factors were taken into consideration like the demographic variables, socio- psychological constructs for explaining the adoption decisions. The framework developed for the study is an extended version of ajzen&rsquo;s Theory of Planned Behavior (TPB). In addition to the traditional construct i.e intention, perceived behavioural control and subjective norms, perceived usefulness was added to the extended version of TPB to study the adoption of Agromet Advisories Services (AAS). The framework will help the academician, policy makers and other stakeholder to develop different perspectives of farmer&rsquo;s decision making in adoption of AAS.</div>

A Framework to Study Farmers Decisions on Adoption of Agromet Advisories Services for Risk Management: Insights from Theory of Planned Behavior

<div>Background: Pulses in Indian agriculture hold a vital position. They are considered as a rich source of vegetable protein, vitamins, minerals and calories to the entire vegetarians in India. Due to long and indiscriminate use of chemical fertilizers, there is deterioration of soil health and hence decrease in productivity. Nutrient mobilizers such as Rhizobium, PGPRs are biological microorganisms that help in atmospheric N2 fixation along with phosphorous solubilization hence; increase the availability of nutrients to the plants. 
 
Methods: In this paper a field experiment was conducted during the kharif season of 2017 and 2018 to evaluate the influence of nutrient mobilizers along with recommended dose of fertilizers on productivity and nutrient uptake in blackgram.&nbsp; 
 
Result: A pronounced effect of nutrient mobilizers was observed on the yield attributing characters of urdbean. Maximum number of seeds per pod, 1000-seed weight, grain yield, straw yield, harvest index, maximum nutrient content and uptake in grain and straw was observed in the treatment T11 (Zn Nm + PGPR + ZnO @ 28.0 kg/ha + Rh+ RDF) followed by T10 (Rh + Zn Nm + PGPR + ZnO @ 14.0 kg/ha + RDF). It can be concluded that nutrient mobilizers not only increase the yield attributes in blackgram but also responsible for increase in nutrient content in the grains which ultimately signals positive attitude regarding nutritional security of the nation.</div>

<div style="text-align:justify">Influence of Nutrient Mobilizers on Productivity and Nutrient Uptake in Blackgram (Vigna mungo L.) Crop under the Tarai Region of Uttarakhand</div>

Background: Accurate global crop yield predictions are crucial for ensuring food security, effective agricultural planning and climate adaptation. However, existing machine learning and deep learning approaches lack crop-specific feature learning, uncertainty quantification and multiscale spatial contexts, which limits their application in precision agriculture. 
 
Methods: This study presents a hybrid ensemble deep learning framework integrated with a crop-aware transformer encoder with heteroscedastic uncertainty for global multi-crop yield prediction (Maize, rice, wheat, soybean) at a 5-arcminute (~9 km) global resolution. The architecture integrates three key innovations: (1) conservative feature engineering using historical yields (4-year sequences with a 3-year temporal gap), geographic coordinates, climate zone indicators alongside temporal trends and stress indicators; (2) crop-aware multi-head attention different mechanisms with crop-specific Query/Key/Value projections enabling differential pattern learning per crop and (3) heteroscedastic output heads predicting both mean yield (&mu;) and uncertainty (&sigma;) via negative log-likelihood optimization. We implemented rigorous validation using temporal splitting (training: years &le;2013; test: years &gt;2013) and geographic blocking (5-fold GroupKFold with 0.5&deg; spatial blocks) to prevent both spatial and temporal data leakage. 
 
Result: Evaluated on the GlobalCropYield5min dataset (1982-2015) across four crops with 60,000 samples in 34 years. The proposed model achieved an overall test R2 of 0.9281, RMSE of 0.585 t/ha and MAE of 0.379 t/ha, with statistical significance confirmed by a paired t-test (p=0.011). Five-fold geographic cross-validation yielded R2&nbsp;of 0.9337&plusmn;0.0074 and rice R&sup2;=0.9462 (best), maize R&sup2;=0.9268, wheat R&sup2;=0.9201 and soybean R&sup2;=0.8298. Uncertainty quantification achieved excellent calibration (Expected calibration error = 0.024), with empirical coverage matching theoretical values (68% intervals: 69.1% coverage; 95% intervals: 94.8% coverage). Regional analysis showed consistent performance across continents (R&sup2;=0.871-0.941), with data-scarce regions showing the expected performance reduction. Ablation studies confirmed that crop-aware attention contributed +3.4% to R2, multiscale spatial features contributed 5.8% and temporal sequence features contributed +3.66%.

A Multi-crop Ensemble Framework for Yield Prediction

Background: Simple Sequence Repeat (SSR) markers are crucial tools for molecular breeding and genetic diversity studies in legumes. Traditional SSR marker development relies on subjective quality assessment methods, which are time-consuming, costly and prone to inconsistency. The lack of quantitative frameworks for predicting marker quality limits the efficiency of breeding programs and genomic studies. To develop a comprehensive machine learning framework for automated SSR marker quality prediction, identify key determinants of marker success through quantitative analysis and establish evidence-based design principles for efficient marker development in legume crops. 
 
Methods: We engineered 15 predictive features from primer design parameters and SSR structural characteristics of 99 horsegram SSR markers. Three machine learning algorithms (Random Forest, Support Vector Regression and Neural Network) were trained and validated using cross-validation. Feature importance analysis quantified the contribution of each parameter to marker quality prediction. 
 
Result: Random Forest achieved optimal performance (R&sup2; = 0.378, MAE = 8.106) with 37.8% explained variance in marker quality prediction. Feature importance analysis revealed primer compatibility factors as dominant predictors: GC content balance between primers (35.6% importance) and melting temperature compatibility (32.2% importance). SSR structural features contributed 22.9% importance, with motif complexity (8.6%) and motif length (8.5%) being most significant. Cross-validation confirmed robust model performance (CV R&sup2; = 0.342 &plusmn; 0.089) across different data subsets.

Machine Learning Framework for Automated SSR Marker Quality Assessment in Horsegram (Macrotyloma uniflorum)

<div style="text-align: justify;">The aim of the study was to investigate the physical indicators of energy reserves mobilization (ERM) viz., Body weight (BW), Body Condition Score (BCS), altered BW percentage (ABWP) and altered BCS percentage (ABCSP) levels in forty eight dairy buffaloes allotted to three dry period lengths (60 or &gt; 60d; 46 to 60 d; and 30 to 45 d). Hybrid Napier and paddy straw, used as roughage, were supplied together with concentrate mixture; and the buffaloes were fed individually according to their requirements. The mean body weights of the group I, II and III buffaloes were 549.41 &plusmn; 4.36, 597.97 &plusmn; 6.20, and 584.54 &plusmn; 3.84, respectively. The BW, BCS, ABWP, and ABCSP levels were influenced by length of dry period, as well as pre and postpartum periods of the entire experimental study. The BW (kg) and BCS showed an increasing (P&lt;0.01) trend from the day of drying to calving with highest (P&lt;0.05) ABWP and ABCSP in traditional dry period group compared to shortened dry periods. Subsequently, the BW and BCS showed a decreasing trend (P&lt;0.01) from calving to 90 days postpartum with highest (P&lt;0.05) decrease of ABWP and ABCSP in group I. Further, 6% Fat corrected milk (FCM) production had a negative significant (P&lt;0.01) correlation with BCS at 30d postpartum, followed by positive (P&lt;0.05) correlation with BCS and BW at 60 or 90 d postpartum.</div>

<div style="text-align: justify;">Effects of shortened dry period on the physical indicators of energy reserves mobilization in high yielding Murrah buffaloes.</div>

Background: Chickpea (Cicer arietinum&nbsp;L.)&nbsp;is&nbsp;one of the most important&nbsp;pulse&nbsp;crops in the world&nbsp;and&nbsp;it is greatly contributing to global protein security and sustainable agricultural systems. However, fluctuations in agro-climatic conditions and genotype-environment interactions considerably influence yield performance and seed quality traits. Accurate prediction and assessment mechanisms are therefore essential to support precision agriculture and breeding programs. 
 
Methods: This paper proposes an integrated biological-computational framework utilizing a novel hybrid attention-based random forest optimizer using artificial intelligence (HARFO-AI) for yield prediction and seed quality assessment in chickpea. Field experiments were conducted to determine the important morphological, physiological, and seed-related variables like plant height, chlorophyll index, number of pods per plant, weight of 100 seeds, germination percentage, vigor index and moisture content. These biological variables were used for training and testing the HARFO-AI model. Statistical parameters like coefficient of determination, root mean square error, mean absolute error, and classification accuracy were used to evaluate the performance of the HARFO-AI model. 
 
Result: The proposed framework yielded a prediction with R = 0.94, RMSE = 0.19 t ha, and MAE = 0.15 t ha, accuracy of 93.1%. The classification accuracy of seed quality was 95.2%. The comparative analysis showed that the performance was about 20% higher than that of the traditional regression models. The findings indicate that integrating biologically significant parameters with advanced AI optimization techniques enhances predictive reliability and provides an effective decision-support tool for precision breeding and sustainable chickpea production. 
&nbsp;

Integrated Framework for Yield Prediction and Seed Quality Assessment in Chickpea (Cicer arietinum L.) using Artificial Intelligence

Background: Tomato (Solanum lycopersicum) leaf diseases, which reduce crop productivity, the quality of products and farm income, have a significant impact on agriculture worldwide. It is crucial to perform accurate, early identification of diseases to realize sustainable crop management and precise agriculture. Traditional manual diagnosis is costly, labor-intensive, inaccurate and subjective, which is unsuitable for large-scale agriculture. Though deep learning has been proven to be successful in classifying plant diseases, conventional methods have difficulties with background noises, inter-class similarity and insufficient learning of contextual features. 
 
Methods: To overcome these issues, a hybrid deep learning framework based on adaptive patch decomposition (APD), graph-based disease correlation learning (GDCL) and hybrid decision fusion classification (HDFC) is proposed to efficiently and accurately classify tomato leaf diseases. The model was trained using 20,000 tomato leaf images with ten different diseases and healthy condition. First, image quality and training stability was enhanced by applying adaptive preprocessing techniques like image resizing, de-noising, contrast enhancement and normalization to the images. The ADPS module segmented lesions from irrelevant background information and generated the critical parts for disease-feature extraction. CNN-based local feature extraction and global context pooling were then performed to capture detailed lesion features and general leaf appearance, respectively. Graph-based disease correlation learning (GDCL) module was designed to establish inter-class disease correlation by using graph-based feature refine mechanism to differentiate between visually indistinguishable diseases. Lastly, hybrid decision fusion classification (HDFC) was proposed to enhance classification accuracy by combining prototype distance learning and graph similarity scores for disease classification. 
 
Result: Experiment results showed that the proposed framework outperformed compared to other deep learning methods, including VGG16, ResNet50, DenseNet121, EfficientNet-B0 and InceptionV3, with the overall accuracy of 99.03%, precision of 98.98%, recall of 98.94%, F1-score of 98.78% and minimum loss of 0.053. The proposed system achieved robust stability and generalization ability tested with 5-fold cross validation. The model was proved to reduce inter-class confusion and obtain outstanding performance in learning the feature from lesions and it could be widely used in intelligent agricultural disease monitoring system and precise agriculture applications.

A Hybrid Deep Learning Framework Integrating Adaptive Patch Decomposition and Graph-based Disease Correlation Learning for Accurate Tomato Leaf Disease Classification