Crop plant diseases are a significant threat to productivity and sustainable development in agriculture. Early prediction of disease attacks is useful for the effective control of the disease by taking proactive actions against their attacks. Modern Information and Communication Technologies (ICTs) have a predominant role in Precision Agriculture (PA) applications to support sustainable developments. There is an immense need for solutions for the early prediction of the disease attack for proactive control against the plant disease attack. The present solution of disease detection using the computer vision approach can only detect the existence of the disease, once the disease has already appeared. This study aims to propose a Machine Learning (ML) approach for the early prediction of the probability of disease attack based on Internet of Things (IoT) directly sensed crop field environmental conditions. Plant disease life cycles are strongly correlated with environmental conditions. The crop field environmental conditions are used to predict the occurrence of plant diseases. The Multiple Linear Regression (MLR) is applied as the ML model, due to the existence of a linear relationship between disease attack and environmental conditions. Internet of Things (IoT) based crop field environmental conditions help to accurately predict the occurrence of plant diseases using the ML approach. The proposed model is implemented for the prediction of blister blight (Exobasidium vexans) for tea (Camellia sinensis) plant to check the effectiveness of the proposed solution. The implementation of the proposed model from 2015 to 2019 reveals that the accuracy of prediction of occurrence of the disease reaches up to 91% in 2019.

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An accurate amount of fertilizer according to the real-time context is the basis of precision agriculture in terms of sustainability and profitability. Many fertilizers recommendation systems are proposed without considering the real-time context in terms of soil fertility level, crop type, and soil type. The major obstacle in developing the real-time context-aware fertilizer recommendation system is related to the complexity associated with the real-time mapping of soil fertility. Furthermore, the existing methods of determining the real-time soil fertility levels for the recommendation of fertilizer are costly, timeconsuming, and laborious. Therefore, to tackle this issue, we propose a machine learning-based fertilizer recommendation methodology according to the real-time soil fertility context captured through the Internet of Things (IoT) assisted soil fertility mapping to improve the accuracy of the fertilizer recommendation system. For real-time soil fertility mapping, an IoT architecture is also proposed to support context-aware fertilizer recommendations. The proposed solution is practically implemented in real crop fields to assess the accuracies of IoT-assisted fertility mapping. The accuracy of IoT-assisted fertility mapping is assessed by comparing the proposed solution with the standard soil chemical analysis method in terms of observing Nitrogen (N), Phosphorous (P), and Potassium (K). The results reveal that the observations by both methods are in line with a mean difference of 0.34, 0.36, and -0.13 for N, P, and K observations, respectively. The context-aware fertilizer recommendation is implemented with the Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbor (KNN) machine learning models to assess the performance of these machine learning models. The evaluation of the proposed solution reveals that the GNB model is more accurate as compared to the machine learning models evaluated, with accuracies of 96% and 94% from training and testing datasets, respectively.INDEX TERMS Internet of Things (IoT), machine learning, soil fertility mapping, fertilizer recommendation, support vector machine (SVM), Gaussian Naïve Bayes (GNB), logistic regression (LR), k-nearest neighbor (KNN).

Soil salinity accumulates a high concentration of salts in soils that interfere with normal plant growth. Early detection and quantification of soil salinity are essential to effectively deal with soil salinity in agriculture. Soil salinity quantification and mapping at the irrigation scheme level are vital to evaluating saline soil's reclamation activity. Existing solutions of salinity mapping are costly, time-consuming, and inadequate for applications at the irrigation scheme level. Internet of Things (IoT) assisted salinity mapping at the irrigation scheme level is proposed to quantify and map the soil salinity in agriculture. The proposed IoT-assisted salinity mapping characterizes the soil salinity in terms of Electric Conductivity, pH, and Total Dissolved Salts. The proposed IoT-assisted salinity mapping effectively observes impacts of reclamation activities in saline soil by frequent observation of soil salinity cost-effectively. The accuracy of proposed IoT-assisted salinity mapping is evaluated against the standard method of salinity measurements. The proposed IoT-assisted salinity mapping is cost-effective, and portable, which is very useful for site-specific treatments and soil zones management in saline soils.

Accurate estimation of Reference Evapotranspiration (ET 0 ) is important for efficient management and conservation of irrigation water. Existing methods of ET 0 rate determination are complex for application at the farmer level. Apart from standard methods of ET 0 determination, many data-driven soft computing approaches were also proposed to determine the ET 0 with limited data set. We proposed a temperature and humidity-based ML approach for ET 0 rate determination on directly sensed environmental conditions of the crop field. Crop field environmental conditions for (ET 0 ) rate determination are sensed by the proposed Internet of Things (IoT) architecture. Crop field environmental conditions from the year 2015 to 2021 in Pakistan are used for the training and testing of the proposed model. Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), and Artificial Neural Network (ANN) based models are compared for performance. Crop fields directly sensed temperature and humidity pass to the model to train and predict the ET 0 -rate of crop fields. The 10-fold cross-validation technique is applied for the evaluation of the proposed approach. The accuracy of the proposed solution for the ET 0 rate is compared against the Food and Agriculture Organization (FAO) recommended Penman-Monteith method for ET 0 rate determination. As concerned of the ML-based models the KNN model is more accurate as compared to SVM,GNB and ANN models with 92% accuracy. The KNN model of ET 0 is more efficient in reducing the Root Mean Squared Errors (RMSE) by 16% and Mean Absolute Errors (MAE) by 3% against the state of the art approach.INDEX TERMS Internet of Things (IoT), Reference Evapotranspiration (ET 0 ), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), K-Nearest Neighbour (KNN), Artificial Neural Network (ANN), Penman-Monteith