Precision Agriculture Using Soil Sensor Driven Machine Learning for Smart Strawberry Production

Abstract

Ubiquitous sensor networks collecting real-time data have been adopted in many industrial settings. This paper describes the second stage of an end-to-end system integrating modern hardware and software tools for precise monitoring and control of soil conditions. In the proposed framework, the data are collected by the sensor network distributed in the soil of a commercial strawberry farm to infer the ultimate physicochemical characteristics of the fruit at the point of harvest around the sensor locations. Empirical and statistical models are jointly investigated in the form of neural networks and Gaussian process regression models to predict the most significant physicochemical qualities of strawberry. Color, for instance, either by itself or when combined with the soluble solids content (sweetness), can be predicted within as little as 9% and 14% of their expected range of values, respectively. This level of accuracy will ultimately enable the implementation of the next phase in controlling the soil conditions where data-driven quality and resource-use trade-offs can be realized for sustainable and high-quality strawberry production.

Sustainability insights on learning-based approaches in precision agriculture in internet-of-things

“…The work carried out by Thilakarathne et al [25] has developed another recommendation model towards PA using ML. The work carried out by Elashmawy and Uysal [26] has presented a sensor-based model towards studying the condition of the soil. The authors have considered using a regression framework using the Gaussian process and neural network to conduct predictive soil analysis.…”

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

“…Grain yield prediction is the process of estimating the grain production of a specific crop, considering diverse factors like weather conditions, soil quality, seed variety, and management practices [7]. This information is crucial for farmers and agricultural organizations as it optimizes crop production [8] and guides decisions on planting, fertilization, and harvest. Numerous methods exist for predicting grain yield, such as statistical modeling, machine learning algorithms [9], and crop simulation models.…”