arXiv:2608. 13073v1 Announce Type: new Abstract: Significant health risks are associated with the illegal, yet commonly practiced use of industrial-grade Calcium Carbide (CaC2) for ripening climacteric fruits like mango and banana, which leaves behind trace residues of arsenic and phosphorus.
By Gurbhit Chaurakoti, Harshit Kumar, Hani Kumar, Anurag Singh, Ram Asrey
arXiv:2607. 00834v1 Announce Type: new Abstract: Near-Infrared (NIR) spectroscopy has emerged as a promising alternative to traditional soil analysis methods, offering advantages such as speed, low cost, and non-destructive testing.
By Vinicius Herique Kieling, Guilherme Macedo Baggio, Felipe Augusto Bueno Rossi, Marco Antonio de Castro Barbosa, Dalcimar Casanova, Larissa Macedo dos Santos Tonial, Jefferson Tales Oliva
arXiv:2608. 07801v1 Announce Type: cross Abstract: Precision nitrogen (N) management (PNM) for cotton requires in-season monitoring of crop growth parameters and N status indicators to decide fertilizer timing, placement, and application rates for optimal canopy development and yield.
By Vaishali Swaminathan, Nithya Rajan, J Alex Thomasson, Amrit Shrestha, Karem Meza Capcha, Robert Hardin, Pramod Pokhrel
arXiv:2608. 11759v1 Announce Type: cross Abstract: Non-destructive X-ray imaging can reveal internal hazelnut defects that are difficult to detect by external inspection alone; however, automated interpretation remains challenging because of subtle radiographic differences among classes, marked class imbalance, and limited annotated data.
By Giancarlo Sportelli, Nicola Belcari, Roberta Pace, Umberto Bernardo, Sharmin Sultana, Alessandra Toncelli, Matteo Giaccone
arXiv:2606. 00821v1 Announce Type: new Abstract: This study addresses the challenge of controlling a complex, multi-parameter technological process -- pectin hydrolysis--extraction -- using machine learning methods.
By Mullosharaf K. Arabov, Shavkat Yo. Kholov, Zainiddin K. Muhiddin
arXiv:2608. 01202v1 Announce Type: cross Abstract: Fruit ripeness prediction (FRP) is a classification-based agricultural computer vision task that has attracted much attention, thanks to its wide-ranging advantages in agriculture field for both pre-harvest and post-harvest management.
By Ahmed Baha Ben Jmaa, Faten Chaieb, Anna Fabija\'nska