Machine learning techniques for real-time tillage quality assessment - PRIYADHARSHINI, B; THAMBIDURAI, S; PADMANATHAN, P K; P, KAMARAJ; GANAPATI, PATIL SANTOSH; THIYAGARAJAN, R (The Indian Journal of Agricultural Sciences, 2025)
Tillage quality significantly impacts soil structure, nutrient availability, and crop growth. The present study was carried out in 2024 in the delta region of Tamil Nadu at Puvalur village, Trichy District to evaluate tillage quality based on high-resolution soil images through a machine-learning approach using a Random Forest (RF) model. Key soil properties such as circularity, aspect ratio, and solidity were analyzed, with the model achieving an R² score of 0.86 and a Mean Squared Error (MSE) of 0.0023. Data from 100 soil images were processed using ImageJ software version 1.54 g. The Random Forest model was chosen for its robustness, interpretability, scalability, and efficiency in handling complex, real-time data. Also, the RF model provided accurate, real-time insights, reducing labor and optimizing resource use, promoting sustainability in agriculture across diverse soil conditions.