The effectiveness of farming relies heavily on the condition of machinery and equipment, as well as maintaining the ideal soil conditions for the desired yields. Soil cultivation tools endure substantial stress and wear, emphasizing the need to study their durability surrounding soil contact. Our research focuses on enhancing the lifespan of worn-out ploughshares through various heat treatment methods and hot metal spraying. By remelting the surface of ploughshares using a flame or laser, we aim to identify the most effective treatment for agricultural production. The improved surface treatment of the furrows in field tillers can significantly cut costs and enhance tillage efficiency. Our preliminary findings suggest that the metal spraying and remelting of nickel alloy hold promise for achieving these goals.

Accurate, non-destructive assessment of watermelon ripeness remains a significant challenge in horticultural production, particularly under field conditions where traditional visual and tactile evaluation methods are subjective and often inconsistent. Although mechanical, acoustic, and spectroscopic techniques have demonstrated promising performance, their reliance on controlled laboratory environments limits their practical applicability in real-world agricultural settings. This study presents a field-deployable, AI-assisted computer vision system designed for objective, real-time classification of watermelon ripeness. The proposed prototype combines controlled illumination with RGB imaging and convolutional neural networks trained on thousands of annotated outdoor images collected over multiple growing seasons. A phased development strategy—encompassing proof-of-concept modelling, field integration, and multi-season validation—supports robustness against variable lighting conditions and environmental influences. The anticipated outcome is a reliable, non-destructive decision-support tool for growers, capable of identifying ripe fruit for manual harvesting while providing a technological foundation for future autonomous harvesting and precision agriculture applications.