Development, performance evaluation and prediction of optimal operational conditions for a double-row sugarcane harvester using deep learning

Sugarcane is a vital global crop, serving as a primary source of sugar, biofuel, and renewable energy. Advancements in harvesting are critical to meeting rising demand, enhancing profitability, and supporting eco-friendly agricultural practices in the sugarcane sector. Based on the current challenges of sugarcane harvesting in developed countries, the current study aimed to develop a semiautomatic whole-stalk sugarcane harvester (SWSH) for harvesting two rows of sugarcane stalks at a time and to be front-mounted on a classic four-wheel agricultural tractor. Then performance evaluation and prediction of optimal operational conditions for a double-row sugarcane harvester using Feedforward Neural Network (FNN) and Deep Neural Network (DNN) at different levels of forward speeds (3, 3.5, 4.5, and 5 km/h), row spacing (71, 78.89, and 88.75 cm), cutting heights (0, 2, and 4 cm), and numbers of knives (2 and 4) of the cutting systems. The obtained results showed that the cutting efficiency of the developed SWSH reached 100%. Where the higher cutting efficiency was observed at a cutting height equal to zero (ground level), forward speed of 3 km/hand row spacing of 71 cm using both 2 and 4 knives. The minimum total operating cost of the developed SWSH was about 4.42 USD/ha, and it was detected when using a forward speed of 4.5 km/h, row spacing of 88.75 cm, a cutting height of 4 cm, and two knives only on the cutting disk. Furthermore, at a row spacing of 88.75 cm, the maximum field capacity of the developed SWSH was 0.554 ha/h, observed at a forward speed of 4.5 km/h.