An Interpretable Artificial Intelligence Based Smart Agriculture System

Abstract

With increasing world population the demand of food production has increased exponentially. Internet of Things (IoT) based smart agriculture system can play a vital role in optimising crop yield by managing crop requirements in real-time. Interpretability can be an important factor to make such systems trusted and easily adopted by farmers. In this paper, we propose a novel artificial intelligence-based agriculture system that uses IoT data to monitor the environment and alerts farmers to take the required actions for maintaining ideal conditions for crop production. The strength of the proposed system is in its interpretability which makes it easy for farmers to understand, trust and use it. The use of fuzzy logic makes the system customisable in terms of types/number of sensors, type of crop, and adaptable for any soil types and weather conditions. The proposed system can identify anomalous data due to security breaches or hardware malfunction using machine learning algorithms. To ensure the viability of the system we have conducted thorough research related to agricultural factors such as soil type, soil moisture, soil temperature, plant life cycle, irrigation requirement and water application timing for Maize as our target crop. The experimental results show that our proposed system is interpretable, can detect anomalous data, and triggers actions accurately based on crop requirements.

Study of applications of Internet of Things and Machine Learning for Smart Agriculture

“…Different sensor nodes, network layer protocols, cloud services and ML algorithms developed for smart agriculture applications viz. irrigation monitoring ( [1], [26], [27], [34], [43], [44]), production process management ( [28], [29], [41]), plant growth and disease monitoring ( [30], [31], [32], [38], [39], [42]) and precision agriculture ( [33], [34], [40]) are considered in Table III.…”

“…Additionally, the increase in number of sensor nodes amount to massive data generation, thus adding to the ambiguity in data analysis and interpretation [38]. While ML algorithms have been effectively utilised to identify security vulnerabilities and hardware failures [30], the full potential of these algorithms is yet to be realised.…”

Analisis Bibliometric : Satu Dekade Penelitian Kecerdasan Buatan Data Anggrek

“…Kami harus memperhatikan bahwa istilah ML dan kecerdasan buatan (AI) digunakan secara bergantian dalam ulasan ini [5]. Interpretabilitas dapat menjadi faktor penting untuk membuat sistem tersebut dipercaya dan mudah diadopsi oleh petani [6]. Big Data dan Artificial Intelligence (AI) dapat digabungkan untuk mencapai tujuan yang diinginkan [6].…”

Design and Implementation of Smart Hydroponics Farming Using IoT‐Based AI Controller with Mobile Application System

“…Traditionally, either farmers are manually classifying the diseases or pathologist are identifying the disease through lab experiments. However, the performance of traditional systems is purely depending on their experience, and it also a time-consuming task [11,12]. Further, the early detection and prevention of plant diseases can improve the hydroponics performance.…”