Smart farming is a development that has emphasized information and communication technology used in machinery, equipment, and sensors in network-based hi-tech farm supervision cycles. Innovative technologies, the Internet of Things (IoT), and cloud computing are anticipated to inspire growth and initiate the use of robots and artificial intelligence in farming. Such ground-breaking deviations are unsettling current agriculture approaches, while also presenting a range of challenges. This paper investigates the tools and equipment used in applications of wireless sensors in IoT agriculture, and the anticipated challenges faced when merging technology with conventional farming activities. Furthermore, this technical knowledge is helpful to growers during crop periods from sowing to harvest; and applications in both packing and transport are also investigated.

The soil–environmental relationship identified and standardised over the years has expedited the growth of digital soil-mapping techniques; hence, various machine learning algorithms are involved in predicting soil attributes. Therefore, comparing the different machine learning algorithms is essential to provide insights into the performance of the different algorithms in predicting soil information for Indian landscapes. In this study, we compared a suite of six machine learning algorithms to predict quantitative (Cubist, decision tree, k-NN, multiple linear regression, random forest, support vector regression) and qualitative (C5.0, k-NN, multinomial logistic regression, naïve Bayes, random forest, support vector machine) soil information separately at a regional level. The soil information, including the quantitative (pH, OC, and CEC) and qualitative (order, suborder, and great group) attributes, were extracted from the legacy soil maps using stratified random sampling procedures. A total of 4479 soil observations sampled were non-spatially partitioned and intersected with 39 environmental covariate parameters. The predicted maps depicted the complex soil–environmental relationships for the study area at a 30 m spatial resolution. The comparison was facilitated based on the evaluation metrics derived from the test datasets and visual interpretations of the predicted maps. Permutation feature importance analysis was utilised as the model-agnostic interpretation tool to determine the contribution of the covariate parameters to the model’s calibration. The R² values for the pH, OC, and CEC ranged from 0.19 to 0.38; 0.04 to 0.13; and 0.14 to 0.40, whereas the RMSE values ranged from 0.75 to 0.86; 0.25 to 0.26; and 8.84 to 10.49, respectively. Irrespective of the algorithms, the overall accuracy percentages for the soil order, suborder, and great group class ranged from 31 to 67; 26 to 65; and 27 to 65, respectively. The tree-based ensemble random forest and rule-based tree models’ (Cubist and C5.0) algorithms efficiently predicted the soil properties spatially. However, the efficiency of the other models can be substantially increased by advocating additional parameterisation measures. The range and scale of the quantitative soil attributes, in addition to the sampling frequency and design, greatly influenced the model’s output. The comprehensive comparison of the algorithms can be utilised to support model selection and mapping at a varied scale. The derived digital soil maps will help farmers and policy makers to adopt precision information for making decisions at the farm level leading to productivity enhancements through the optimal use of nutrients and the sustainability of the agricultural ecosystem, ensuring food security.

The study predicted the soil erosion map of the Coimbatore district, Western part of Tamil Nadu, India, to assess the potential of Digital Soil Mapping (DSM) against the empirical model, Revised Universal Soil Loss Equation (RUSLE). A total of 386 surface soil samples were collected and their properties used to predict soil erosion spatially. The Geographical Information System (GIS) enabled RUSLE factors to be derived spatially and integrated for annual soil loss estimation. The soil loss values calculated based on the RUSLE factors ranged from 0 to 88.58 t ha^–1 yr^–1. The spatial soil erosion was further predicted based on the SCORPAN model using the decision tree methodology to compare the suitability of the approaches in erosion mapping. The modelling tool, Cubist, was adopted to model the continuous soil erosion properties with 33 environmental covariates as independent variables. The LS factor, profile curvature, geology, mean annual rainfall, maximum air temperature, land use, minimum air temperature, elevation, slope and geomorphology covariates were the most influencing variables in predicting soil loss. The Cubist model’s rules are applied to the NLCD-Cubist classifier to generate a soil erosion map, and the annual soil loss ranged from 0 to 52.54 t ha^–1 yr^–1. The spatial pattern of soil erosion classes indicated that the Coimbatore district’s western part has a high erosion risk. Based on the validation sample points (n = 190), the DSM approach showed a higher concordance correlation (r = 0.857), whereas the RUSLE model showed less agreement (r = 0.632). From the concordance correlation values, it could be inferred that the decision tree algorithm performed better than the RUSLE erosion model. However, from the error map generated, it is advisable to have more soil observations with suitable environmental covariates to improve digital soil erosion mapping accuracy. The DSM approach is exploited well with point-based soil erosion loss information. The RUSLE model would perform well if the R, K, LS, C, and P factor maps were available at uniform scale and spatial resolution.

Crop yield prediction is becoming increasingly crucial due to global food security concerns, as highlighted by recent reports from the World Health Organization. Accurate early predictions can mitigate famine risks by estimating food supply, which is essential for 820 million people facing hunger globally. Rice is the primary staple food consumed worldwide; therefore, global rice yield and rice area are monitored using emerging technologies such as remote sensing (RS) and machine learning (ML). These technologies provide valuable tools for enhancing rice yield predictions. RS includes critical information on crop health, soil conditions and weather patterns. In contrast, ML algorithms analyze these datasets to identify patterns and relationships that affect yield. Integrating these technologies offers promising improvements in yield forecasting accuracy, with applications showing successful yield predictions 1-3 months before harvest. Various ML techniques, including Random Forest, Support Vector Machines and deep learning models such as LSTM (Long-Short Term Memory), have been employed, often in combination with RS data. However, these models face challenges, such as data quality, managing high-dimensional RS data and accounting for spatial and temporal variability. Despite these challenges, integrating RS and ML has significant potential for advancing precision agriculture and achieving sustainable food production. This study explores the advancements, practical applications and challenges associated with using RS and ML for rice yield prediction, emphasizing the importance of these technologies in addressing global food security and promoting sustainable agricultural practices.

Rice (Oryza sativa L.) is cultivated in diverse environments, contributing significantly to global methane (CH4) emissions, accounting for approximately 12% of total methane emissions worldwide. With the demand for increasing rice production, methane emissions from rice fields continue to rise. This underscores the need for reliable strategies for estimation and mitigation strategies. This study aims to estimate methane emissions from rice fields of the Cauvery Delta region of Tamil Nadu using remote sensing data. Sentinel 1A Synthetic Aperture Radar (SAR) data was used to delineate rice areas and assess agronomic flooding, while Land Surface Temperature (LST) derived from MODIS satellite data was used to estimate methane emissions. The semi-empirical methane emission model was employed to estimate methane flux based on temperature-related factors and rice area. The spatial methane estimates derived from the LST-based method were compared with field observations. The results showed that during the Kharif and Rabi seasons of 2023, a total of 169679 and 356270 ha of rice area were delineated, respectively. The total methane emissions of 7.16 and 17.09 Gg were estimated for both seasons, respectively. The agreement between estimated and observed methane for both seasons was 84.74 % and 87.52 %, respectively. This study provides an efficient empirical method for estimating methane emissions across large areas and highlights the need for continued monitoring and the development of mitigation strategies to reduce methane emissions from rice cultivation.