Peanuts are an important source of oil and feed crops. It is vital to elevate the economy and feed the increasing population. Due to urbanization, the decrease and aging of the workforce threaten the agriculture sector. Consequently, countries around the world are urgently in need of innovation, particularly from an adoptable peanut harvesting technology perspective. As a result, this work focused on reviewing the developments in peanut harvesting, emphasizing the impacts of manual to mechanical harvesting methods. Specifically, mechanical harvesting is affected by soil characteristics, peanut plant characteristics, maturity at harvest, land size, peanut harvesting methods, and the importance of innovating peanut harvesting machinery. This endeavor covers legitimate scientific publications from 2010 to 2023, sustained by online public data from different government agencies. The review ends with the recommendation for standardization of peanut harvester machine specifications and testing methods to easily compare machine performances, aiding farmers, researchers, and industry stakeholders in better decisions when selecting mechanical peanut harvesters. Further, standardizing testing methods could establish operational efficiency and foster innovation and improvement within the industry.

Peanut harvesting remains labor-intensive and costly due to the limited availability of locally developed mechanized harvesting technologies. Thus, this study presents the design, optimization, and evaluation of a four-wheel tractor peanut digger to advance harvesting. Structural integrity was evaluated using Finite Element Analysis (FEA) in ANSYS under a draft load of 2008.2 N, yielding a maximum von Mises stress of 33.012 MPa, a peak strain of 0.00015, a maximum deformation of 1.72 mm, and a factor of safety of 5.74, confirming adequate structural reliability. Field performance was optimized using a Box–Behnken Design (BBD) with tractor speed, digger blade lift angle, and clearance angle as factors, while material capacity and digging efficiency served as response variables. Regression analysis showed that the main effects significantly influenced both responses, whereas interaction effects were insignificant. Response Surface Methodology (RSM) optimization identified optimal operating conditions of a 14.95° lift angle, 15.70° clearance angle, and 1.74 km·h⁻¹ forward speed, achieving a predicted material capacity of 0.56 t·h⁻¹ and digging efficiency of 96.69%. Validation tests improved these values to 0.59 t·h⁻¹ and 97.42%, demonstrating that integrating numerical methods provides an effective approach for developing adoptable and structurally reliable peanut harvesting machinery.