<div style="text-align:justify">Background: The soil nematodes can affect the crops in various ways. The plant-parasitic nematodes can lead to severe yield losses. The extent of crop yield loss depends on the susceptibility of the variety or host tolerance, population density of the nematode and various environmental variables. However, no tool is available for the prediction of nematode population buildup in soil therefore it has been difficult to issue advisories for timely management of these pathogens. Here we developed a method to accurately predict the nematode population buildup in soil for its timely management.&nbsp; 
 
Methods: Nematode population index of a plant-parasitic nematode Tylenchorynchus was taken from two crops i.e. mung bean and crotalaria. The model was developed considering various weather variables to predict the population of the Tylenchorynchus in the fields of mung bean and crotalaria. Weather parameters such as maximum and minimum temperature, relative humidity, wind speed and sunshine hours were considered for developing the model for Tylenchorynchus population prediction. Stepwise regression method was applied to predict the nematode population.&nbsp; 
 
Result: The regression analysis between estimated and observed values of Tylenchorynchus population gave the R2 value as 0.98 for mung bean and 0.87 for crotalaria. Well timed prediction can help the growers to apply the required management practices to make it beneficial economically. This method can be extended to predict the population buildup of other serious nematode pests of crops.</div>

<div>Prediction of Nematode Population Dynamics using Weather Variables in Leguminous Crops</div>

<div>Background: Crop yield prediction is an important issue for the proper selection of crop for sowing. Earlier prediction of crop is done by the farmer&rsquo;s experience on a particular type of field and crop. Predicting the crop is done by the farmer&rsquo;s experience based on the factors like soil types, climatic condition, seasons and weather, rainfall and irrigation facilities.&nbsp; 
 
Methods: Data mining techniques is the better choice for predicting the crop. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year&rsquo;s crop production. This research proposes and implements a system to predict crop yield from soil data. This is achieved by applying Decision Tree Algorithm on agricultural data. The main aim of this research is to pinpoint the accuracy of Decision Tree Algorithm and C 5.0 algorithm which is used to predict the crop yield.&nbsp; 
 
Result: This paper presents a brief analysis of Crop yield prediction using data mining technique based decision tree algorithm and C5.0 algorithm for the selected region (Krishnagiri) district of Tamil Nadu in India. The experimental result shows that the proposed work efficiently to determine the accuracy of decision tree algorithm and also to predict the crop yield production using R- Tool.</div>

<div>Soil Data Analysis and Crop Yield Prediction in Data Mining using R-Tool</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: In agriculture, Crop Yield Prediction (CYP) by Deep Learning (DL) models such as Deep Neural Network (DNN) and Convolutional Neural Network (CNN) is an emerging technique that utilizes weather, soil and previous year yield information for different crops. On the other hand, these models often struggle to effectively handle time-series data, which can lead to challenges in capturing temporal relationships between weather, soil and crop yield factors. This can affect the accuracy of predicting crop yields. 
 
Methods:&nbsp;To address this issue, the Fixed-Adaptive Temporal Attention Network (FAT-AttNet) is proposed in this study for CYP using a time-series crop yield dataset, which includes historical information on soil, weather and crop yields. A normalization technique is used to preprocess the dataset, removing irregularities and empty values. Next, the FAT-AttNet model is trained to predict crop yields. This model consists of fixed and adaptive temporal modules. In the fixed temporal module, 18 parallel one-dimensional (1D) CNNs are used to process the stable factors, such as soil data, to capture temporal features. In the adaptive temporal module, 16 parallel two-dimensional (2D) CNNs capture the dynamic relationships among weather, soil and crop growth factors over time. The results from these models are combined using the lateral connection and an attention strategy is applied to adjust the weight of each factor for more accurate predictions. Furthermore, a Fully Connected (FC) layer and an output layer are used to predict crop yields. 
 
Result: The experiments are conducted using an agricultural crop yield in Indian States dataset (ACY-ISD) to evaluate the performance of the FAT-AttNet model for five major crops in Tamil Nadu using the historical data from 2002 to 2023.&nbsp; The results show that the FAT-AttNet model achieves a Mean Absolute Error (MAE) of 0.05, 0.059, 0.061, 0.058 and 0.074 for groundnut, maize, moong, rice and urad yield predictions, respectively. &nbsp;The Root Mean Square Error (RMSE) for the groundnut, maize, moong, rice and urad yield predictions is 0.217, 0.268, 0.245, 0.235 and 0.266, respectively. The correlation coefficients for groundnut, maize, moong, rice and urad yield predictions are 0.862, 0.846, 0.864, 0.860 and 0.859, respectively. The results of FAT-AttCNN models outperform the Improved Optimizer Function with Long Short-Term Memory (IOF-LSTM), CNN-Bidirectional LSTM (BiLSTM), MayFly Algorithm empowered attention-BiLSTM (MFA-BiLSTM) and Spatio-Temporal Attention-based CNN (STACNN) models. 
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Fixed-adaptive Temporal Attention Network for Predicting Crop Yield

Background: This study utilized advanced Artificial Intelligence (AI) techniques to develop predictive models for legume crop yields in the context of climate change scenarios. With the escalating challenges posed by climate change, accurately forecasting agricultural outcomes is imperative for sustainable food production.&nbsp; 
 
Methods: Utilizing an extensive dataset comprising legume crop yields, climate change forecasts and relevant environmental factors, this study employs advanced machine learning techniques such as XGBoost to create strong predictive models. The analysis encompasses diverse climate change scenarios to assess the resilience of legume crops under varying environmental conditions. 
 
Result: Results indicate a significant enhancement in predictive accuracy compared to conventional models, demonstrating the efficacy of AI in anticipating legume crop yields amidst climatic uncertainties. The presented work not only improves the precision of agricultural predictive modeling but also underscores the vital role of AI in mitigating the detrimental effects of climate change on food security.&nbsp; The agriculture industry faces changing weather patterns, thus using AI-powered prediction models becomes essential for making well-informed decisions and implementing sustainable farming methods.

AI-Powered Predictive Modelling of Legume Crop Yields in a Changing Climate

Background: Accurate prediction of crop production is essential for efficient agricultural resource planning. Factors such as weather, soil moistureand temperature have a direct impact on crop yields, making precise forecasting vital. 
 
Methods: This study presents a hybrid model that enhances crop production prediction by integrating a 1D Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) network and an attention layer. The model is specifically applied to wheat and rice, major crops in India. The model evolves into a CNN-LSTM hybrid, designed to improve prediction accuracy by incorporating modifications, including multi-head attention and a multiplication skip connection. 
 
Result: When compared with conventional methods like Support Vector Regressor, Decision Tree Regressor and Random Forest Regressor, the proposed hybrid model shows significantly better performance. It achieves a Root Mean Square Error (RMSE) of 0.017, indicating low prediction error, a Mean Absolute Error (MAE) of 0.09 and a strong correlation between predicted and actual yields, with an R&sup2; of 0.967.

Crop Yield Prediction using Deep Learning Algorithm based on CNN-LSTM with Attention Layer and Skip Connection

Background: The purpose of this project is to evaluate the prospective use of drone-based remote sensing for assessing legume crop development and predicting their yields. Traditional agricultural procedures often fall short in delivering timely and accurate monitoring, necessitating the adoption of innovative techniques. 
 
Methods: The study considers vegetative indicators such as NDVI, GNDVI and canopy cover to track the growth of three legume crops-peanut, soybean and common bean. Machine learning models, including random forest, support vector machines and multiple linear regression, were developed to predict agricultural production using remote sensing data. Statistical analysis was performed to verify the trustworthiness of vegetation indicators against ground-truth measurements. 
 
Result: The models achieved high accuracy, with R&sup2; values reaching up to 0.92. Statistical analysis confirmed strong relationships between vegetation indicators and ground-truth data. Among the studied crops, soybeans exhibited the highest growth vigor and yield. The study demonstrates that integrating machine learning with drone photography can enhance precision agriculture, making it more scalable and sustainable. Future research is recommended to explore different crop varieties and environmental conditions to further optimize the application of these technologies.

Assessing Legume Crop Growth and Yield Prediction using Drone-based Remote Sensing

Background: Sustainable crop production in the vast populated countries such as India has always been challenging problem since ancient times. The mismanagement of crops has led to reduced yields. It is of utmost important to study the Soil conditions and predict the suitable crop for cultivation under such conditions. 
 
Methods: In the present investigation a gadget is developed based on IoT and Arduino to monitor the soil based on parameters like pH, temperature and moisture, which helps in identifying soil conditions and decide the suitable crop and their management practices. For this purpose, the dataset of different crops has been passed with their parameters. Blynk application is used for remote monitoring. Hence the user can remotely view sensor data, log and view data, etc. 
 
Result: The results suggested that even after 24 hours of irrigation the black soil holds the high volumetric moisture. The device was correctly able to identify the suitable crops under the investigating environment viz Wheat, Lemon and Sunflower for Black soil. The system recommends the type of fertilizers that may be used and also the microbial condition of the soil. The system would enable farmers for better selection of crops under different conditions.

Real-Time Soil Monitoring with IoT Enabled System for Crop Prediction

<div>Background: Freshwater in sufficient quantity and adequate quality is a prerequisite for human societies and natural ecosystems. To adequately feed 9.3 billion people in 2050, consumptive water use (i.e. transpired water) by all food and fodder crops needs to increase from its present estimated level of 7000 km3/year to 12,586 km3/year. However, fresh water resources are increasingly getting scarce because of increased competition among a multitude of users. Getting agriculture to perform with progressively smaller allocation of renewable water resources will remain a challenge on global scale. To meet the challenge there is an urgent need to improve the crop water productivity to ensure the sustainability of agriculture. 
 
Methods: An experiment was carried out at village Menawali, Hanumangarh, Rajasthan during both kharif and rabi seasons to assess productivity, economics, N-uptake and water use of different crops. An area of 187 ha comprising 25 farmers irrigated by common irrigation channels were selected to collect the information. The information required i.e. soil, crop management, growth, phonological, yields, water balance, N-uptake and water use efficiency of each crops were collected from 15 farmers. Bt-cotton and clusterbean of kharif and wheat and Indian mustard in rabi were prominent crops, cotton-wheat, cotton-mustard, clusterbean-wheat and clusterbean-mustard were major cropping sequences of the study region. 
 
Result: In kharif season, Bt-cotton gave higher economic yields than clusterbean and amongst rabi season crops, economic yields of wheat and mustard were 4255, 1778 kg/ha, respectively. The economic yield of cropping sequences varied from 3741-6514 kg/ha and were higher for cotton-wheat (6218 kg/ha), intermediate for clusterbean-wheat (5785 kg/ha) and lower for cotton-mustard (3741 kg/ha) and clusterbean-mustard (3308 kg/ha). The cotton-wheat (₹1181.2 mm) sequence had highest water use. Clusterbean-wheat cropping system recorded highest water productivity (16.5 kg/ha mm) followed by clusterbean-mustard (14.9 kg/ha mm). The clusterbean-mustard (₹456/ha mm) cropping sequence was most profitable and fetched highest net return followed by clusterbean-wheat (₹383/ha mm).</div>

Assessing Crop and Water Productivity Performance of Different Crops and Cropping Systems

Background: With the intensification of agriculture, optimizing fertilizer use is critical for enhancing crop yields while maintaining soil health. This study evaluates the impact of three NPK fertilizer grades-Hey Won NPK 14-14-14, 14-5-21 and 20-8-12-on the productivity of maize-based cropping systems and their influence on soil properties. 
 
Methods: A two-year field experiment (2022-2024) was conducted at the research farm of the Department of Soil Science, Punjab Agricultural University (PAU), Ludhiana, India, using a split-plot design. Four maize-based cropping systems were assigned to the main plots and five fertilizer treatments to the sub-plots. Key parameters such as grain yield, plant nutrient levels (N, P, K) and soil nutrient status (pre- and post-fertilization) were assessed. 
 
Result: Preliminary results demonstrated significant differences in crop yields, system productivity and equivalent yields due to varying fertilizer treatments. The findings highlight the benefits of targeted NPK applications in boosting crop performance and improving soil nutrient profiles, supporting sustainable agriculture. Additionally, predictive modeling and performance analysis using Python identified key nutrient-yield relationships and the pivotal role of specific crops in enhancing nutrient-use efficiency. The study underscores the value of data-driven strategies for optimizing fertilizer management, offering actionable insights for farmers, agronomists and policymakers to improve productivity while preserving soil health across diverse cropping systems. 
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Productivity and Soil Health Evaluation of Cropping Systems with Hey Won NPK Fertilizers: A Predictive Modeling and Performance Analysis Approach Through Python