Field Evaluation of Smart Sensor System for Plant Disease Prediction Using LSTM Network

Abstract

Leaf wetness duration (LWD), soil moisture, soil temperature, ambient temperature, and relative humidity information are the essential factors that leads to germination of plant disease. In this work, an internet of things (IoT) enabled leaf wetness sensor (LWS) and soil moisture sensor (SMS) is developed. Subsequently, commercial soil temperature (ST), relative humidity (RH) and ambient temperature (AT) are used for plant disease prediction. The developed LWS offers a response of about 250% when exposed to air and water and response time of about 20 seconds and attributes a hysteresis of about ±3 %. Acrylic protective lacquer (APL) coating of about 25- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$75 ~\mu \text{m}$ </tex-math></inline-formula> thin is deposited on LWS and it is observed that the sensor capacitance changes only by 2% when temperature varies from 20 °C to 65 °C. Likewise, fabricated SMS offers a response of 10 kHz ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {\Delta } \text{F}$ </tex-math></inline-formula> ) with only a 2% change in frequency when temperature varies from 20 °C to 65 °C and works with an accuracy of ±3%. Further, aforementioned sensors along with in-house developed IoT-enabled system has been deployed under field conditions for about four months. In this work, we considered Powdery mildew (D1), Anthracnose (D2), and Root rot (D3) disease on the Mango plant. Further, we have implemented the Long Short Term Memory (LSTM) network which performs better compared to the existing methods discussed on plant disease management. The proposed network achieves an accuracy of 96%, precision-recall and F1 score of 97%, 98%, and 99%, respectively.